Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

Science.gov (United States)

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

Lack of evidence for low-dimensional chaos in heart rate variability

DEFF Research Database (Denmark)

Kanters, J K; Holstein-Rathlou, N H; Agner, E

1994-01-01

INTRODUCTION: The term chaos is used to describe erratic or apparently random time-dependent behavior in deterministic systems. It has been suggested that the variability observed in the normal heart rate may be due to chaos, but this question has not been settled. METHODS AND RESULTS: Heart rate...... in the experimental data, but the prediction error as a function of the prediction length increased at a slower rate than characteristic of a low-dimensional chaotic system. CONCLUSION: There is no evidence for low-dimensional chaos in the time series of RR intervals from healthy human subjects. However, nonlinear...

Heart rate variability in healthy population

International Nuclear Information System (INIS)

Alamgir, M.; Hussain, M.M.

2010-01-01

Background: Heart rate variability has been considered as an indicator of autonomic status. Little work has been done on heart rate variability in normal healthy volunteers. We aimed at evolving the reference values of heart rate variability in our healthy population. Methods: Twenty-four hour holter monitoring of 37 healthy individuals was done using Holter ECG recorder 'Life card CF' from 'Reynolds Medical'. Heart rate variability in both time and frequency domains was analysed with 'Reynolds Medical Pathfinder Digital/700'. Results: The heart rate variability in normal healthy volunteers of our population was found in time domain using standard deviation of R-R intervals (SDNN), standard deviation of average NN intervals (SDANN), and Square root of the mean squared differences of successive NN intervals (RMSSD). Variation in heart rate variability indices was observed between local and foreign volunteers and RMSSD was found significantly increased (p<0.05) in local population. Conclusions: The values of heart rate variability (RMSSD) in healthy Pakistani volunteers were found increased compared to the foreign data reflecting parasympathetic dominance in our population. (author)

Predictive Value of Respiratory Rate Thresholds in Pneumonia ...

African Journals Online (AJOL)

A study was carried out to determine the predictive value of respiratory rate in the clinical diagnosis of pneumonia in 101 children with respiratory symptoms of <28 days duration. Clinical, demographic and anthropometric variables were obtained at presentation while confirmation of the diagnosis was by a chest x-ray in ...

Predictive Variable Gain Iterative Learning Control for PMSM

Directory of Open Access Journals (Sweden)

Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

The Impact of Soil Sampling Errors on Variable Rate Fertilization

Energy Technology Data Exchange (ETDEWEB)

R. L. Hoskinson; R C. Rope; L G. Blackwood; R D. Lee; R K. Fink

2004-07-01

Variable rate fertilization of an agricultural field is done taking into account spatial variability in the soil’s characteristics. Most often, spatial variability in the soil’s fertility is the primary characteristic used to determine the differences in fertilizers applied from one point to the next. For several years the Idaho National Engineering and Environmental Laboratory (INEEL) has been developing a Decision Support System for Agriculture (DSS4Ag) to determine the economically optimum recipe of various fertilizers to apply at each site in a field, based on existing soil fertility at the site, predicted yield of the crop that would result (and a predicted harvest-time market price), and the current costs and compositions of the fertilizers to be applied. Typically, soil is sampled at selected points within a field, the soil samples are analyzed in a lab, and the lab-measured soil fertility of the point samples is used for spatial interpolation, in some statistical manner, to determine the soil fertility at all other points in the field. Then a decision tool determines the fertilizers to apply at each point. Our research was conducted to measure the impact on the variable rate fertilization recipe caused by variability in the measurement of the soil’s fertility at the sampling points. The variability could be laboratory analytical errors or errors from variation in the sample collection method. The results show that for many of the fertility parameters, laboratory measurement error variance exceeds the estimated variability of the fertility measure across grid locations. These errors resulted in DSS4Ag fertilizer recipe recommended application rates that differed by up to 138 pounds of urea per acre, with half the field differing by more than 57 pounds of urea per acre. For potash the difference in application rate was up to 895 pounds per acre and over half the field differed by more than 242 pounds of potash per acre. Urea and potash differences

Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortality – an experimental study on the FINCAVAS cohort

Directory of Open Access Journals (Sweden)

Paruthi ePradhapan

2014-06-01

Full Text Available The non-linear inverse relationship between RR-intervals and heart rate (HR contributes significantly to the heart rate variability (HRV parameters and their performance in mortality prediction. To determine the level of influence HR exerts over HRV parameters’ prognostic power, we studied the predictive performance for different HR levels by applying eight correction procedures, multiplying or dividing HRV parameters by the mean RR-interval (RRavg to the power 0.5-16. Data collected from 1288 patients in The Finnish Cardiovascular Study (FINCAVAS, who satisfied the inclusion criteria, was used for the analyses. HRV parameters (RMSSD, VLF Power and LF Power were calculated from 2-minute segment in the rest phase before exercise and 2-minute recovery period immediately after peak exercise. Area under the receiver operating characteristic curve (AUC was used to determine the predictive performance for each parameter with and without HR corrections in rest and recovery phases. The division of HRV parameters by segment’s RRavg to the power 2 (HRVDIV-2 showed the highest predictive performance under the rest phase (RMSSD: 0.67/0.66; VLF Power: 0.70/0.62; LF Power: 0.79/0.65; cardiac mortality/non-cardiac mortality with minimum correlation to HR (r = -0.15 to 0.15. In the recovery phase, Kaplan-Meier (KM survival analysis revealed good risk stratification capacity at HRVDIV-2 in both groups (cardiac and non-cardiac mortality. Although higher powers of correction (HRVDIV-4 and HRVDIV-8 improved predictive performance during recovery, they induced an increased positive correlation to HR. Thus, we inferred that predictive capacity of HRV during rest and recovery is augmented when its dependence on HR is weakened by applying appropriate correction procedures.

Heart rate variability in prediction of individual adaptation to endurance training in recreational endurance runners.

Science.gov (United States)

Vesterinen, V; Häkkinen, K; Hynynen, E; Mikkola, J; Hokka, L; Nummela, A

2013-03-01

The aim of this study was to investigate whether nocturnal heart rate variability (HRV) can be used to predict changes in endurance performance during 28 weeks of endurance training. The training was divided into 14 weeks of basic training (BTP) and 14 weeks of intensive training periods (ITP). Endurance performance characteristics, nocturnal HRV, and serum hormone concentrations were measured before and after both training periods in 28 recreational endurance runners. During the study peak treadmill running speed (Vpeak ) improved by 7.5 ± 4.5%. No changes were observed in HRV indices after BTP, but after ITP, these indices increased significantly (HFP: 1.9%, P=0.026; TP: 1.7%, P=0.007). Significant correlations were observed between the change of Vpeak and HRV indices (TP: r=0.75, PHRV among recreational endurance runners, it seems that moderate- and high-intensity training are needed. This study showed that recreational endurance runners with a high HRV at baseline improved their endurance running performance after ITP more than runners with low baseline HRV. © 2011 John Wiley & Sons A/S.

Emotional exhaustion and workload predict clinician-rated and objective patient safety

Science.gov (United States)

Welp, Annalena; Meier, Laurenz L.; Manser, Tanja

2015-01-01

Aims: To investigate the role of clinician burnout, demographic, and organizational characteristics in predicting subjective and objective indicators of patient safety. Background: Maintaining clinician health and ensuring safe patient care are important goals for hospitals. While these goals are not independent from each other, the interplay between clinician psychological health, demographic and organizational variables, and objective patient safety indicators is poorly understood. The present study addresses this gap. Method: Participants were 1425 physicians and nurses working in intensive care. Regression analysis (multilevel) was used to investigate the effect of burnout as an indicator of psychological health, demographic (e.g., professional role and experience) and organizational (e.g., workload, predictability) characteristics on standardized mortality ratios, length of stay and clinician-rated patient safety. Results: Clinician-rated patient safety was associated with burnout, trainee status, and professional role. Mortality was predicted by emotional exhaustion. Length of stay was predicted by workload. Contrary to our expectations, burnout did not predict length of stay, and workload and predictability did not predict standardized mortality ratios. Conclusion: At least in the short-term, clinicians seem to be able to maintain safety despite high workload and low predictability. Nevertheless, burnout poses a safety risk. Subjectively, burnt-out clinicians rated safety lower, and objectively, units with high emotional exhaustion had higher standardized mortality ratios. In summary, our results indicate that clinician psychological health and patient safety could be managed simultaneously. Further research needs to establish causal relationships between these variables and support to the development of managerial guidelines to ensure clinicians’ psychological health and patients’ safety. PMID:25657627

Emotional Exhaustion and Workload Predict Clinician-Rated and Objective Patient Safety

Directory of Open Access Journals (Sweden)

Annalena eWelp

2015-01-01

Full Text Available Aims: To investigate the role of clinician burnout, demographic and organizational characteristics in predicting subjective and objective indicators of patient safety. Background: Maintaining clinician health and ensuring safe patient care are important goals for hospitals. While these goals are not independent from each other, the interplay between clinician psychological health, demographic and organizational variables and objective patient safety indicators is poorly understood. The present study addresses this gap. Method: Participants were 1425 physicians and nurses working in intensive care. (Multilevel regression analysis was used to investigate the effect of burnout as an indicator of psychological health, demographic (e.g., professional role and experience and organizational (e.g., workload, predictability characteristics on standardized mortality ratios, length of stay and clinician-rated patient safety. Results: Clinician-rated patient safety were associated with burnout, trainee status, and professional role. Mortality was predicted by emotional exhaustion. Length of stay was predicted by workload. Contrary to our expectations, burnout did not predict length of stay, and workload and predictability did not predict standardized mortality ratios.Conclusion: At least in the short-term, clinicians seem to be able to maintain safety despite high workload and low predictability. Nevertheless, burnout poses a safety risk. Subjectively, burnt-out clinicians rated safety lower, and objectively, units with high emotional exhaustion had higher standardized mortality ratios. In summary, our results indicate that clinician psychological health and patient safety could be managed simultaneously. Further research needs to establish causal relationships between these variables or and support the development of managerial guidelines to ensure clinicians’ psychological health and patients’ safety.

Achievable data rate in spectrum-sharing channels with variable-rate variable-power primary users

KAUST Repository

Yang, Yuli; Aï ssa, Sonia

2012-01-01

In this work, we propose a transmission strategy for secondary users (SUs) within a cognitive radio network where primary users (PUs) exploit variable-rate variable-power modulation. By monitoring the PU's transmissions, the SU adjusts its transmit

Evaluating the underlying factors behind variable rate debt.

Science.gov (United States)

McCue, Michael J; Kim, Tae Hyun Tanny

2007-01-01

Recent trends show a greater usage of variable rate debt among health care bond issues. In 2004, 63.4% of the total health care bonds issued were variable rate compared with 30.6% in 1995 (Fitch Ratings, 2005). The purpose of this study is to gain a better understanding of the underlying factors, credit spread, issue characteristics, and issuer factors behind why hospitals and health system borrowers select variable rate debt compared with fixed rate debt. From 2000 to 2004, this study sampled 230 newly issued tax-exempt bonds issued by acute care hospitals and health care systems that included both variable and fixed rate debt issues. Using a logistic regression model, hospitals with variable rate debt issues were assigned a value of 1, whereas hospitals with fixed rate debt issues were assigned a value of 0. This study found a positive association between bond insurance and variable rate debt and a negative association between callable feature and variable rate debt. Facilities located in certificate-of-need states that possessed higher case mix acuity, earned higher profit margins, generated higher debt service coverage, and held less debt were more likely to issue variable rate debt. Overall, hospital managers and board members of hospitals possessing a strong financial performance have an interest in utilizing variable rate debt to lower their cost of capital. In addition, this outcome may also reflect that investment bankers are doing a better job in educating senior hospital management about the interest rate savings benefit of variable rate compared with fixed rate debt.

New considerations on variability of creep rupture data and life prediction

International Nuclear Information System (INIS)

Kim, Seon Jin; Jeong, Won Taek; Kong, Yu Sik

2009-01-01

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in thee creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time and state steady creep rate on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model.

New Considerations on Variability of Creep Rupture Data and Life Prediction

International Nuclear Information System (INIS)

Jung, Won Taek; Kong, Yu Sik; Kim, Seon Jin

2009-01-01

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in the creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time (RT) and steady state creep rate (SSCR) on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model

Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson’s Disease

Science.gov (United States)

Ridgel, Angela L.; Abdar, Hassan Mohammadi; Alberts, Jay L.; Discenzo, Fred M.; Loparo, Kenneth A.

2014-01-01

Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease. PMID:23144045

Predicting higher education graduation rates from institutional characteristics and resource allocation

Directory of Open Access Journals (Sweden)

Florence A. Hamrick

2004-05-01

Full Text Available This study incorporated institutional characteristics (e.g., Carnegie type, selectivity and resource allocations (e.g., instructional expenditures, student affairs expenditures into a statistical model to predict undergraduate graduation rates. Instructional expenditures, library expenditures, and a number of institutional classification variables were significant predictors of graduation rates. Based on these results, recommendations as well as warranted cautions are included about allocating academic financial resources to optimize graduation rates

Fatty liver incidence and predictive variables

International Nuclear Information System (INIS)

Tsuneto, Akira; Seto, Shinji; Maemura, Koji; Hida, Ayumi; Sera, Nobuko; Imaizumi, Misa; Ichimaru, Shinichiro; Nakashima, Eiji; Akahoshi, Masazumi

2010-01-01

Although fatty liver predicts ischemic heart disease, the incidence and predictors of fatty liver need examination. The objective of this study was to determine fatty liver incidence and predictive variables. Using abdominal ultrasonography, we followed biennially through 2007 (mean follow-up, 11.6±4.6 years) 1635 Nagasaki atomic bomb survivors (606 men) without fatty liver at baseline (November 1990 through October 1992). We examined potential predictive variables with the Cox proportional hazard model and longitudinal trends with the Wilcoxon rank-sum test. In all, 323 (124 men) new fatty liver cases were diagnosed. The incidence was 19.9/1000 person-years (22.3 for men, 18.6 for women) and peaked in the sixth decade of life. After controlling for age, sex, and smoking and drinking habits, obesity (relative risk (RR), 2.93; 95% confidence interval (CI), 2.33-3.69, P<0.001), low high-density lipoprotein-cholesterol (RR, 1.87; 95% CI, 1.42-2.47; P<0.001), hypertriglyceridemia (RR, 2.49; 95% CI, 1.96-3.15; P<0.001), glucose intolerance (RR, 1.51; 95% CI, 1.09-2.10; P=0.013) and hypertension (RR, 1.63; 95% CI, 1.30-2.04; P<0.001) were predictive of fatty liver. In multivariate analysis including all variables, obesity (RR, 2.55; 95% CI, 1.93-3.38; P<0.001), hypertriglyceridemia (RR, 1.92; 95% CI, 1.41-2.62; P<0.001) and hypertension (RR, 1.31; 95% CI, 1.01-1.71; P=0.046) remained predictive. In fatty liver cases, body mass index and serum triglycerides, but not systolic or diastolic blood pressure, increased significantly and steadily up to the time of the diagnosis. Obesity, hypertriglyceridemia and, to a lesser extent, hypertension might serve as predictive variables for fatty liver. (author)

Variable exhumation rates and variable displacement rates: Documenting recent slowing of Himalayan shortening in western Bhutan

Science.gov (United States)

McQuarrie, Nadine; Tobgay, Tobgay; Long, Sean P.; Reiners, Peter W.; Cosca, Michael A.

2014-01-01

exhumation are temporally decoupled. Our combined cooling curves highlight that the youngest cooling ages may not mark the fastest thrusting rates or the window of fastest exhumation. Instead, temporal variations in exhumation are best viewed through identifying transients in exhumation rate. We suggest that the strongest control on exhumation magnitude and variability is fold–thrust belt geometry, particularly the locations and magnitudes of footwall ramps, which can change over 10ʼs of km distance. Balanced cross sections predict the location and magnitude of these ramps and how they vary in space and time, providing an untapped potential for testing permissible cross-section geometries and kinematics against measured cooling histories.

Predicting higher education graduation rates from institutional characteristics and resource allocation

OpenAIRE

Florence A. Hamrick; John H. Schuh; Mack C. Shelley

2004-01-01

This study incorporated institutional characteristics (e.g., Carnegie type, selectivity) and resource allocations (e.g., instructional expenditures, student affairs expenditures) into a statistical model to predict undergraduate graduation rates. Instructional expenditures, library expenditures, and a number of institutional classification variables were significant predictors of graduation rates. Based on these results, recommendations as well as warranted cautions are included about allocat...

Predicting Bond Betas using Macro-Finance Variables

DEFF Research Database (Denmark)

Aslanidis, Nektarios; Christiansen, Charlotte; Cipollini, Andrea

We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-fi…nance variables. We explore differences across long-term government ...... bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas....

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

Science.gov (United States)

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Study (Prediction of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods

Directory of Open Access Journals (Sweden)

Massoud Tabesh

2011-07-01

Full Text Available Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models and Evolutionary polynomial Regression method (EPR pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods.

Speed control variable rate irrigation

Science.gov (United States)

Speed control variable rate irrigation (VRI) is used to address within field variability by controlling a moving sprinkler’s travel speed to vary the application depth. Changes in speed are commonly practiced over areas that slope, pond or where soil texture is predominantly different. Dynamic presc...

Reduced heart rate variability in social anxiety disorder: associations with gender and symptom severity.

Directory of Open Access Journals (Sweden)

Gail A Alvares

Full Text Available BACKGROUND: Polyvagal theory emphasizes that autonomic nervous system functioning plays a key role in social behavior and emotion. The theory predicts that psychiatric disorders of social dysfunction are associated with reduced heart rate variability, an index of autonomic control, as well as social inhibition and avoidance. The purpose of this study was to examine whether heart rate variability was reduced in treatment-seeking patients diagnosed with social anxiety disorder, a disorder characterized by social fear and avoidance. METHODS: Social anxiety patients (n = 53 were recruited prior to receiving psychological therapy. Healthy volunteers were recruited through the University of Sydney and the general community and were matched by gender and age (n = 53. Heart rate variability was assessed during a five-minute recording at rest, with participants completing a range of self-report clinical symptom measures. RESULTS: Compared to controls, participants with social anxiety exhibited significant reductions across a number of heart rate variability measures. Reductions in heart rate variability were observed in females with social anxiety, compared to female controls, and in patients taking psychotropic medication compared to non-medicated patients. Finally, within the clinical group, we observed significant associations between reduced heart rate variability and increased social interaction anxiety, psychological distress, and harmful alcohol use. CONCLUSIONS: The results of this study confirm that social anxiety disorder is associated with reduced heart rate variability. Resting state heart rate variability may therefore be considered a marker for social approach-related motivation and capacity for social engagement. Additionally, heart rate variability may provide a useful biomarker to explain underlying difficulties with social approach, impaired stress regulation, and behavioral inhibition, especially in disorders associated with

7 CFR 1735.33 - Variable interest rate loans.

Science.gov (United States)

2010-01-01

... 7 Agriculture 11 2010-01-01 2010-01-01 false Variable interest rate loans. 1735.33 Section 1735.33... § 1735.33 Variable interest rate loans. After June 10, 1991, and prior to November 1, 1993, RUS made certain variable rate loans at interest rates less than 5 percent but not less than 2 percent. For those...

A Prediction Model for Community Colleges Using Graduation Rate as the Performance Indicator

Science.gov (United States)

Moosai, Susan

2010-01-01

In this thesis a prediction model using graduation rate as the performance indicator is obtained for community colleges for three cohort years, 2003, 2004, and 2005 in the states of California, Florida, and Michigan. Multiple Regression analysis, using an aggregate of seven predictor variables, was employed in determining this prediction model.…

Are Macro variables good predictors? A prediction based on the number of total medals acquired

Directory of Open Access Journals (Sweden)

Shahram Shafiee

2012-01-01

Full Text Available A large amount of effort is spent on forecasting the outcome of sporting events. Moreover, there are large quantities of data regarding the outcomes of sporting events and the factors which are assumed to contribute to those outcomes. In this paper we tried to predict the success of nations at the Asian Games through macro-economic, political, social and cultural variables. we used the information of variables include urban population, Education Expenditures, Age Structure, GDP Real Growth Rate, GDP Per Capita, Unemployment Rate, Population, Inflation Average, current account balance, life expectancy at birth and Merchandise Trade for all of the participating countries in Asian Games from 1970 to 2006 in order to build the model and then this model was tested by the information of variables in 2010. The prediction is based on the number of total medals acquired each country. In this research we used WEKA software that is a popular suite of machine learning software written in Java. The value of correlation coefficient between the predicted and original ranks is 90.42%. Neural Network Model, between 28 countries mentioned, predicts their ranks according to the maximum difference between predicted and original ranks of 19 countries (67.85% is 3, the maximum difference between predicted and original ranks of 8 countries (28.57% is between 4 to 6 and the difference between predicted and original ranks of 1 countries (3.57% is more than 6.

12 CFR 619.9340 - Variable interest rate.

Science.gov (United States)

2010-01-01

... 12 Banks and Banking 6 2010-01-01 2010-01-01 false Variable interest rate. 619.9340 Section 619.9340 Banks and Banking FARM CREDIT ADMINISTRATION FARM CREDIT SYSTEM DEFINITIONS § 619.9340 Variable interest rate. An interest rate on the outstanding loan balances, which may be changed from time to time...

Sensitivity, specificity and predictive values of linear and nonlinear indices of heart rate variability in stable angina patients

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Pivatelli Flávio

2012-10-01

Full Text Available Abstract Background Decreased heart rate variability (HRV is related to higher morbidity and mortality. In this study we evaluated the linear and nonlinear indices of the HRV in stable angina patients submitted to coronary angiography. Methods We studied 77 unselected patients for elective coronary angiography, which were divided into two groups: coronary artery disease (CAD and non-CAD groups. For analysis of HRV indices, HRV was recorded beat by beat with the volunteers in the supine position for 40 minutes. We analyzed the linear indices in the time (SDNN [standard deviation of normal to normal], NN50 [total number of adjacent RR intervals with a difference of duration greater than 50ms] and RMSSD [root-mean square of differences] and frequency domains ultra-low frequency (ULF ≤ 0,003 Hz, very low frequency (VLF 0,003 – 0,04 Hz, low frequency (LF (0.04–0.15 Hz, and high frequency (HF (0.15–0.40 Hz as well as the ratio between LF and HF components (LF/HF. In relation to the nonlinear indices we evaluated SD1, SD2, SD1/SD2, approximate entropy (−ApEn, α1, α2, Lyapunov Exponent, Hurst Exponent, autocorrelation and dimension correlation. The definition of the cutoff point of the variables for predictive tests was obtained by the Receiver Operating Characteristic curve (ROC. The area under the ROC curve was calculated by the extended trapezoidal rule, assuming as relevant areas under the curve ≥ 0.650. Results Coronary arterial disease patients presented reduced values of SDNN, RMSSD, NN50, HF, SD1, SD2 and -ApEn. HF ≤ 66 ms2, RMSSD ≤ 23.9 ms, ApEn ≤−0.296 and NN50 ≤ 16 presented the best discriminatory power for the presence of significant coronary obstruction. Conclusion We suggest the use of Heart Rate Variability Analysis in linear and nonlinear domains, for prognostic purposes in patients with stable angina pectoris, in view of their overall impairment.

What Does Eye-Blink Rate Variability Dynamics Tell Us About Cognitive Performance?

Directory of Open Access Journals (Sweden)

Rafal Paprocki

2017-12-01

Full Text Available Cognitive performance is defined as the ability to utilize knowledge, attention, memory, and working memory. In this study, we briefly discuss various markers that have been proposed to predict cognitive performance. Next, we develop a novel approach to characterize cognitive performance by analyzing eye-blink rate variability dynamics. Our findings are based on a sample of 24 subjects. The subjects were given a 5-min resting period prior to a 10-min IQ test. During both stages, eye blinks were recorded from Fp1 and Fp2 electrodes. We found that scale exponents estimated for blink rate variability during rest were correlated with subjects' performance on the subsequent IQ test. This surprising phenomenon could be explained by the person to person variation in concentrations of dopamine in PFC and accumulation of GABA in the visual cortex, as both neurotransmitters play a key role in cognitive processes and affect blinking. This study demonstrates the possibility that blink rate variability dynamics at rest carry information about cognitive performance and can be employed in the assessment of cognitive abilities without taking a test.

Amniotic fluid index predicts the relief of variable decelerations after amnioinfusion bolus.

Science.gov (United States)

Spong, C Y; McKindsey, F; Ross, M G

1996-10-01

Our purpose was to determine whether intrapartum amniotic fluid index before amnioinfusion can be used to predict response to therapeutic amnioinfusion. Intrapartum patients (n = 85) with repetitive variable decelerations in fetal heart rate that necessitated amnioinfusion (10 ml/min for 60 minutes) underwent determination of amniotic fluid index before and after bolus amnioinfusion. The fetal heart tracing was scored (scorer blinded to amniotic fluid index values) for number and characteristics of variable decelerations before and 1 hour after initiation of amnioinfusion. The amnioinfusion was considered successful if it resulted in a decrease of > or = 50% in total number of variable decelerations or a decrease of > or = 50% in the rate of atypical or severe variable decelerations after administration of the bolus. Spontaneous vaginal births before completion of administration of the bolus (n = 18) were excluded from analysis. The probability of success of amnioinfusion in relation to amniotic fluid index was analyzed with the chi(2) test for progressive sequence. The mean amniotic fluid index before amnioinfusion was 6.2 +/- 3.3 cm. An amniotic fluid index of amnioinfusion decreased with increasing amniotic fluid index before amnioinfusion (76% [16/21] when initial amniotic fluid index was 0 to 4 cm, 63% [17/27] when initial amniotic fluid index was 4 to 8 cm, 44% [7/16] when initial amniotic fluid index was 8 to 12 cm, and 33% [1/3] when initial amniotic fluid index was > 12 cm, p = 0.03). The incidence of nuchal cords or true umbilical cord knots increased in relation to amniotic fluid index before amnioinfusion. Amniotic fluid index before amnioinfusion can be used to predict the success of amnioinfusion for relief of variable decelerations in fetal heart rate. Failure of amnioinfusion at a high amniotic fluid index before amnioinfusion may be explained by the increased prevalence of nuchal cords or true knots in the umbilical cord.

Response-rate differences in variable-interval and variable-ratio schedules: An old problem revisited

OpenAIRE

Cole, Mark R.

1994-01-01

In Experiment 1, a variable-ratio 10 schedule became, successively, a variable-interval schedule with only the minimum interreinforcement intervals yoked to the variable ratio, or a variable-interval schedule with both interreinforcement intervals and reinforced interresponse times yoked to the variable ratio. Response rates in the variable-interval schedule with both interreinforcement interval and reinforced interresponse time yoking fell between the higher rates maintained by the variable-...

Executive functioning independently predicts self-rated health and improvement in self-rated health over time among community-dwelling older adults.

Science.gov (United States)

McHugh, Joanna Edel; Lawlor, Brian A

2016-01-01

Self-rated health, as distinct from objective measures of health, is a clinically informative metric among older adults. The purpose of our study was to examine the cognitive and psychosocial factors associated with self-rated health. 624 participants over the age of 60 were assessed at baseline, and of these, 510 were contacted for a follow-up two years later. Measures of executive function and self-rated health were assessed at baseline, and self-rated health was assessed at follow-up. We employed multiple linear regression analyses to investigate the relationship between executive functioning and self-rated health, while controlling for demographic, psychosocial and biological variables. Controlling for other relevant variables, executive functioning independently and solely predicted self-rated health, both at a cross-sectional level, and also over time. Loneliness was also found to cross-sectionally predict self-rated health, although this relationship was not present at a longitudinal level. Older adults' self-rated health may be related to their executive functioning and to their loneliness. Self-rated health appeared to improve over time, and the extent of this improvement was also related to executive functioning at baseline. Self-rated health may be a judgement made of one's functioning, especially executive functioning, which changes with age and therefore may be particularly salient in the reflections of older adults.

Variable importance and prediction methods for longitudinal problems with missing variables.

Directory of Open Access Journals (Sweden)

Iván Díaz

Full Text Available We present prediction and variable importance (VIM methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed. Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner, which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO. In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC for a receiver-operator curve (ROC. Thus, given that 1 our VIM

Heart Rate Variability - A Historical Perspective

Directory of Open Access Journals (Sweden)

George E Billman

2011-11-01

Full Text Available Heart rate variability (HRV, the beat-to-beat variation in either heart rate or the duration of the R-R interval – the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration – the so called respiratory sinus arrhythmia, RSA and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of heart rate variability. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the Physician’s Pulse Watch (a watch with a second hand that could be stopped in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733 was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895 and advent of digital signal processing techniques in the 1960’s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations that are commonly used to measure heart rate variability.

Heart rate variability interventions for concussion and rehabilitation

OpenAIRE

Conder, Robert L.; Conder, Alanna A.

2014-01-01

The study of Heart Rate Variability (HRV) has emerged as an essential component of cardiovascular health, as well as a physiological mechanism by which one can increase the interactive communication between the cardiac and the neurocognitive systems (i.e., the body and the brain). It is well-established that lack of heart rate variability implies cardiopathology, morbidity, reduced quality-of-life, and precipitous mortality. On the positive, optimal heart rate variability has been associated ...

Assessment of variable application rates of biological amendment ...

African Journals Online (AJOL)

Assessment of variable application rates of biological amendment substances on establishment and growth characteristics of maize plants. ... Hence, a greenhouse experiment was conducted in 2008 to assess the effects of variable rates (50, 75 and 100% of the recommended rates) of industrial manufactured biological ...

Continuously variable rating: a new, simple and logical procedure to evaluate original scientific publications

Directory of Open Access Journals (Sweden)

Mauricio Rocha e Silva

2011-01-01

Full Text Available OBJECTIVE: Impact Factors (IF are widely used surrogates to evaluate single articles, in spite of known shortcomings imposed by cite distribution skewness. We quantify this asymmetry and propose a simple computer-based procedure for evaluating individual articles. METHOD: (a Analysis of symmetry. Journals clustered around nine Impact Factor points were selected from the medical ‘‘Subject Categories’’ in Journal Citation Reports 2010. Citable items published in 2008 were retrieved and ranked by granted citations over the Jan/2008 - Jun/2011 period. Frequency distribution of cites, normalized cumulative cites and absolute cites/decile were determined for each journal cluster. (b Positive Predictive Value. Three arbitrarily established evaluation classes were generated: LOW (1.33.9. Positive Predictive Value for journal clusters within each class range was estimated. (c Continuously Variable Rating. An alternative evaluation procedure is proposed to allow the rating of individually published articles in comparison to all articles published in the same journal within the same year of publication. The general guiding lines for the construction of a totally dedicated software program are delineated. RESULTS AND CONCLUSIONS: Skewness followed the Pareto Distribution for (1Predictive Values ranged from 24 - 43% for over 98% of the selected journals in the ISI database. Continuously Variable Rating is shown to be a simple computer based procedure capable of accurately providing a valid rating for each article within the journal and time frame in which it was published.

[Resonance hypothesis of heart rate variability origin].

Science.gov (United States)

Sheĭkh-Zade, Iu R; Mukhambetaliev, G Kh; Cherednik, I L

2009-09-01

A hypothesis is advanced of the heart rate variability being subjected to beat-to-beat regulation of cardiac cycle duration in order to ensure the resonance interaction between respiratory and own fluctuation of the arterial system volume for minimization of power expenses of cardiorespiratory system. Myogenic, parasympathetic and sympathetic machanisms of heart rate variability are described.

Drowsiness detection using heart rate variability.

Science.gov (United States)

Vicente, José; Laguna, Pablo; Bartra, Ariadna; Bailón, Raquel

2016-06-01

It is estimated that 10-30 % of road fatalities are related to drowsy driving. Driver's drowsiness detection based on biological and vehicle signals is being studied in preventive car safety. Autonomous nervous system activity, which can be measured noninvasively from the heart rate variability (HRV) signal obtained from surface electrocardiogram, presents alterations during stress, extreme fatigue and drowsiness episodes. We hypothesized that these alterations manifest on HRV and thus could be used to detect driver's drowsiness. We analyzed three driving databases in which drivers presented different sleep-deprivation levels, and in which each driving minute was annotated as drowsy or awake. We developed two different drowsiness detectors based on HRV. While the drowsiness episodes detector assessed each minute of driving as "awake" or "drowsy" with seven HRV derived features (positive predictive value 0.96, sensitivity 0.59, specificity 0.98 on 3475 min of driving), the sleep-deprivation detector discerned if a driver was suitable for driving or not, at driving onset, as function of his sleep-deprivation state. Sleep-deprivation state was estimated from the first three minutes of driving using only one HRV feature (positive predictive value 0.80, sensitivity 0.62, specificity 0.88 on 30 drivers). Incorporating drowsiness assessment based on HRV signal may add significant improvements to existing car safety systems.

Leukocyte Populations are Associated with Heart Rate Variability After a Triathlon

Directory of Open Access Journals (Sweden)

Cruz Germán Hernández

2016-12-01

Full Text Available The purpose of this study was to analyze cellular immune components and their association with heart rate variability in triathlon athletes. Twelve athletes were included (age 36.41 ± 5.57 years, body mass 81.84 ± 10.97 kg and blood samples were taken one week before, immediately, at 2 and 48 hours, and one week after competition. Total lymphocytes and their subpopulations, neutrophils, basophils, eosinophils and monocytes were analyzed. At the same time, heart rate variability was recorded for 30 minutes using Polar Team2®. A significant difference between lymphocyte subpopulations and heart rate variability was found in the different study periods. A positive correlation was found between total lymphocytes and rMSSD (r = .736, p <0.05, CD3+ and rMSSD (r = .785, p <0.05, and CD4+ and rMSSD (r = .795, p < 0.05 at the end of the competition. After one week of competition, a negative correlation was found between eosinophils and MRR, SDNN, pNN50, and rMSSD (p <0.01; and basophils and MRR, SDNN, pNN50, and rMSSD (p <0.01; while a positive correlation was found between CD19+ (B cells and pNN50 (r = .678, p <0.05. Our results suggest that it is possible to predict the effect of training with regard to the athlete's performance.

Kalman Filter or VAR Models to Predict Unemployment Rate in Romania?

Directory of Open Access Journals (Sweden)

Simionescu Mihaela

2015-06-01

Full Text Available This paper brings to light an economic problem that frequently appears in practice: For the same variable, more alternative forecasts are proposed, yet the decision-making process requires the use of a single prediction. Therefore, a forecast assessment is necessary to select the best prediction. The aim of this research is to propose some strategies for improving the unemployment rate forecast in Romania by conducting a comparative accuracy analysis of unemployment rate forecasts based on two quantitative methods: Kalman filter and vector-auto-regressive (VAR models. The first method considers the evolution of unemployment components, while the VAR model takes into account the interdependencies between the unemployment rate and the inflation rate. According to the Granger causality test, the inflation rate in the first difference is a cause of the unemployment rate in the first difference, these data sets being stationary. For the unemployment rate forecasts for 2010-2012 in Romania, the VAR models (in all variants of VAR simulations determined more accurate predictions than Kalman filter based on two state space models for all accuracy measures. According to mean absolute scaled error, the dynamic-stochastic simulations used in predicting unemployment based on the VAR model are the most accurate. Another strategy for improving the initial forecasts based on the Kalman filter used the adjusted unemployment data transformed by the application of the Hodrick-Prescott filter. However, the use of VAR models rather than different variants of the Kalman filter methods remains the best strategy in improving the quality of the unemployment rate forecast in Romania. The explanation of these results is related to the fact that the interaction of unemployment with inflation provides useful information for predictions of the evolution of unemployment related to its components (i.e., natural unemployment and cyclical component.

Identify the dominant variables to predict stream water temperature

Science.gov (United States)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

Assessment of heart rate, acidosis, consciousness, oxygenation, and respiratory rate to predict noninvasive ventilation failure in hypoxemic patients.

Science.gov (United States)

Duan, Jun; Han, Xiaoli; Bai, Linfu; Zhou, Lintong; Huang, Shicong

2017-02-01

To develop and validate a scale using variables easily obtained at the bedside for prediction of failure of noninvasive ventilation (NIV) in hypoxemic patients. The test cohort comprised 449 patients with hypoxemia who were receiving NIV. This cohort was used to develop a scale that considers heart rate, acidosis, consciousness, oxygenation, and respiratory rate (referred to as the HACOR scale) to predict NIV failure, defined as need for intubation after NIV intervention. The highest possible score was 25 points. To validate the scale, a separate group of 358 hypoxemic patients were enrolled in the validation cohort. The failure rate of NIV was 47.8 and 39.4% in the test and validation cohorts, respectively. In the test cohort, patients with NIV failure had higher HACOR scores at initiation and after 1, 12, 24, and 48 h of NIV than those with successful NIV. At 1 h of NIV the area under the receiver operating characteristic curve was 0.88, showing good predictive power for NIV failure. Using 5 points as the cutoff value, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy for NIV failure were 72.6, 90.2, 87.2, 78.1, and 81.8%, respectively. These results were confirmed in the validation cohort. Moreover, the diagnostic accuracy for NIV failure exceeded 80% in subgroups classified by diagnosis, age, or disease severity and also at 1, 12, 24, and 48 h of NIV. Among patients with NIV failure with a HACOR score of >5 at 1 h of NIV, hospital mortality was lower in those who received intubation at ≤12 h of NIV than in those intubated later [58/88 (66%) vs. 138/175 (79%); p = 0.03). The HACOR scale variables are easily obtained at the bedside. The scale appears to be an effective way of predicting NIV failure in hypoxemic patients. Early intubation in high-risk patients may reduce hospital mortality.

Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

Directory of Open Access Journals (Sweden)

G Khalili-Zadeh-Mahani

2016-07-01

Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

Changes in heart rate variability and QT variability during the first trimester of pregnancy.

Science.gov (United States)

Carpenter, R E; D'Silva, L A; Emery, S J; Uzun, O; Rassi, D; Lewis, M J

2015-03-01

The risk of new-onset arrhythmia during pregnancy is high, presumably relating to changes in both haemodynamic and cardiac autonomic function. The ability to non-invasively assess an individual's risk of developing arrhythmia during pregnancy would therefore be clinically significant. We aimed to quantify electrocardiographic temporal characteristics during the first trimester of pregnancy and to compare these with non-pregnant controls. Ninety-nine pregnant women and sixty-three non-pregnant women underwent non-invasive cardiovascular and haemodynamic assessment during a protocol consisting of various physiological states (postural manoeurvres, light exercise and metronomic breathing). Variables measured included stroke volume, cardiac output, heart rate, heart rate variability, QT and QT variability and QTVI (a measure of the variability of QT relative to that of RR). Heart rate (p pregnancy only during the supine position (p pregnancy in all physiological states (p pregnancy in all states (p pregnancy is associated with substantial changes in heart rate variability, reflecting a reduction in parasympathetic tone and an increase in sympathetic activity. QTVI shifted to a less favourable value, reflecting a greater than normal amount of QT variability. QTVI appears to be a useful method for quantifying changes in QT variability relative to RR (or heart rate) variability, being sensitive not only to physiological state but also to gestational age. We support the use of non-invasive markers of cardiac electrical variability to evaluate the risk of arrhythmic events in pregnancy, and we recommend the use of multiple physiological states during the assessment protocol.

Predicting Basal Metabolic Rate in Men with Motor Complete Spinal Cord Injury.

Science.gov (United States)

Nightingale, Tom E; Gorgey, Ashraf S

2018-01-08

To assess the accuracy of existing basal metabolic rate (BMR) prediction equations in men with chronic (>1 year) spinal cord injury (SCI). The primary aim is to develop new SCI population-specific BMR prediction models, based on anthropometric, body composition and/or demographic variables that are strongly associated with BMR. Thirty men with chronic SCI (Paraplegic; n = 21, Tetraplegic; n = 9), aged 35 ± 11 years (mean ± SD) participated in this cross-sectional study. Criterion BMR values were measured by indirect calorimetry. Body composition (dual energy X-ray absorptiometry; DXA) and anthropometric measurements (circumferences and diameters) were also taken. Multiple linear regression analysis was performed to develop new SCI-specific BMR prediction models. Criterion BMR values were compared to values estimated from six existing and four developed prediction equations RESULTS: Existing equations that use information on stature, weight and/or age, significantly (P BMR by a mean of 14-17% (187-234 kcal/day). Equations that utilised fat-free mass (FFM) accurately predicted BMR. The development of new SCI-specific prediction models demonstrated that the addition of anthropometric variables (weight, height and calf circumference) to FFM (Model 3; r = 0.77), explained 8% more of the variance in BMR than FFM alone (Model 1; r = 0.69). Using anthropometric variables, without FFM, explained less of the variance in BMR (Model 4; r = 0.57). However, all the developed prediction models demonstrated acceptable mean absolute error ≤ 6%. BMR can be more accurately estimated when DXA derived FFM is incorporated into prediction equations. Utilising anthropometric measurements provides a promising alternative to improve the prediction of BMR, beyond that achieved by existing equations in persons with SCI.

Prediction of university student’s addictability based on some demographic variables, academic procrastination, and interpersonal variables

Directory of Open Access Journals (Sweden)

Mohammad Ali Tavakoli

2014-02-01

Full Text Available Objectives: This study aimed to predict addictability among the students, based on demographic variables, academic procrastination, and interpersonal variables, and also to study the prevalence of addictability among these students. Method: The participants were 500 students (260 females, 240 males selected through a stratified random sampling among the students in Islamic Azad University Branch Abadan. The participants were assessed through Individual specification inventory, addiction potential scale and Aitken procrastination Inventory. Findings: The findings showed %23/6 of students’ readiness for addiction. Men showed higher addictability than women, but age wasn’t an issue. Also variables such as economic status, age, major, and academic procrastination predicted %13, and among interpersonal variables, the variables of having friends who use drugs and dissociated family predicted %13/2 of the variance in addictability. Conclusion: This study contains applied implications for addiction prevention.

Effects of Liraglutide on Heart Rate and Heart Rate Variability

DEFF Research Database (Denmark)

Kumarathurai, Preman; Anholm, Christian; Larsen, Bjørn Strøier

2017-01-01

OBJECTIVE: Reduced heart rate variability (HRV) and increased heart rate (HR) have been associated with cardiovascular mortality. Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) increase HR, and studies have suggested that they may reduce HRV. We examined the effect of the GLP-1 RA...

Achievable data rate in spectrum-sharing channels with variable-rate variable-power primary users

KAUST Repository

Yang, Yuli

2012-08-01

In this work, we propose a transmission strategy for secondary users (SUs) within a cognitive radio network where primary users (PUs) exploit variable-rate variable-power modulation. By monitoring the PU\\'s transmissions, the SU adjusts its transmit power based on the gap between the PU\\'s received effective signal-to-noise power ratio (SNR) and the lower SNR boundary for the modulation mode that is being used in the primary link. Thus, at the SU\\'s presence, the PU\\'s quality of service (QoS) is guaranteed without increasing its processing complexity thanks to no interference cancellation required in the PU\\'s operation. To demonstrate the advantage of our proposed transmission strategy, we analyze the secondary user\\'s achievable data rate by taking into account different transmission capabilities for the secondary transmitter. The corresponding numerical results not only prove the validity of our derivations but also provide a convenient tool for the network design with the proposed transmission strategy. © 2012 IEEE.

Resting-state qEEG predicts rate of second language learning in adults.

Science.gov (United States)

Prat, Chantel S; Yamasaki, Brianna L; Kluender, Reina A; Stocco, Andrea

2016-01-01

Understanding the neurobiological basis of individual differences in second language acquisition (SLA) is important for research on bilingualism, learning, and neural plasticity. The current study used quantitative electroencephalography (qEEG) to predict SLA in college-aged individuals. Baseline, eyes-closed resting-state qEEG was used to predict language learning rate during eight weeks of French exposure using an immersive, virtual scenario software. Individual qEEG indices predicted up to 60% of the variability in SLA, whereas behavioral indices of fluid intelligence, executive functioning, and working-memory capacity were not correlated with learning rate. Specifically, power in beta and low-gamma frequency ranges over right temporoparietal regions were strongly positively correlated with SLA. These results highlight the utility of resting-state EEG for studying the neurobiological basis of SLA in a relatively construct-free, paradigm-independent manner. Published by Elsevier Inc.

Classification and prediction of port variables

Energy Technology Data Exchange (ETDEWEB)

Molina Serrano, B.

2016-07-01

Many variables are included in planning and management of port terminals. They can beeconomic, social, environmental and institutional. Agent needs to know relationshipbetween these variables to modify planning conditions. Use of Bayesian Networks allowsfor classifying, predicting and diagnosing these variables. Bayesian Networks allow forestimating subsequent probability of unknown variables, basing on know variables.In planning level, it means that it is not necessary to know all variables because theirrelationships are known. Agent can know interesting information about how port variablesare connected. It can be interpreted as cause-effect relationship. Bayesian Networks can beused to make optimal decisions by introduction of possible actions and utility of theirresults.In proposed methodology, a data base has been generated with more than 40 port variables.They have been classified in economic, social, environmental and institutional variables, inthe same way that smart port studies in Spanish Port System make. From this data base, anetwork has been generated using a non-cyclic conducted grafo which allows for knowingport variable relationships - parents-children relationships-. Obtained network exhibits thateconomic variables are – in cause-effect terms- cause of rest of variable typologies.Economic variables represent parent role in the most of cases. Moreover, whenenvironmental variables are known, obtained network allows for estimating subsequentprobability of social variables.It has been concluded that Bayesian Networks allow for modeling uncertainty in aprobabilistic way, even when number of variables is high as occurs in planning andmanagement of port terminals. (Author)

Risks and rewards of variable-rate debt.

Science.gov (United States)

Jordahl, Eric A

2012-05-01

Hospital and health system finance leaders should position their organizations to participate in the variable-rate market. To this end, one important step is to establish the right baseline variable-rate exposure target for the organization based on its credit and risk profile. Leaders also should be thoroughly familiar with the available products and understand the circumstances (pricing, terms, and embedded risk) under which the organization would be willing to deploy them within the overall capital structure.

Zone edge effects with variable rate irrigation

Science.gov (United States)

Variable rate irrigation (VRI) systems may offer solutions to enhance water use efficiency by addressing variability within a field. However, the design of VRI systems should be considered to maximize application uniformity within sprinkler zones, while minimizing edge effects between such zones alo...

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

Science.gov (United States)

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

An analysis of prediction skill of monthly mean climate variability

Energy Technology Data Exchange (ETDEWEB)

Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

2011-09-15

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

Heart rate variability in newborns.

Science.gov (United States)

Javorka, K; Lehotska, Z; Kozar, M; Uhrikova, Z; Kolarovszki, B; Javorka, M; Zibolen, M

2017-09-22

Heart rate (HR) and heart rate variability (HRV) in newborns is influenced by genetic determinants, gestational and postnatal age, and other variables. Premature infants have a reduced HRV. In neonatal HRV evaluated by spectral analysis, a dominant activity can be found in low frequency (LF) band (combined parasympathetic and sympathetic component). During the first postnatal days the activity in the high frequency (HF) band (parasympathetic component) rises, together with an increase in LF band and total HRV. Hypotrophy in newborn can cause less mature autonomic cardiac control with a higher contribution of sympathetic activity to HRV as demonstrated by sequence plot analysis. During quiet sleep (QS) in newborns HF oscillations increase - a phenomenon less expressed or missing in premature infants. In active sleep (AS), HRV is enhanced in contrast to reduced activity in HF band due to the rise of spectral activity in LF band. Comparison of the HR and HRV in newborns born by physiological vaginal delivery, without (VD) and with epidural anesthesia (EDA) and via sectio cesarea (SC) showed no significant differences in HR and in HRV time domain parameters. Analysis in the frequency domain revealed, that the lowest sympathetic activity in chronotropic cardiac chronotropic regulation is in the VD group. Different neonatal pathological states can be associated with a reduction of HRV and an improvement in the health conditions is followed by changes in HRV what can be use as a possible prognostic marker. Examination of heart rate variability in neonatology can provide information on the maturity of the cardiac chronotropic regulation in early postnatal life, on postnatal adaptation and in pathological conditions about the potential dysregulation of cardiac function in newborns, especially in preterm infants.

Using lexical variables to predict picture-naming errors in jargon aphasia

Directory of Open Access Journals (Sweden)

Catherine Godbold

2015-04-01

Full Text Available Introduction Individuals with jargon aphasia produce fluent output which often comprises high proportions of non-word errors (e.g., maf for dog. Research has been devoted to identifying the underlying mechanisms behind such output. Some accounts posit a reduced flow of spreading activation between levels in the lexical network (e.g., Robson et al., 2003. If activation level differences across the lexical network are a cause of non-word outputs, we would predict improved performance when target items reflect an increased flow of activation between levels (e.g. more frequently-used words are often represented by higher resting levels of activation. This research investigates the effect of lexical properties of targets (e.g., frequency, imageability on accuracy, error type (real word vs. non-word and target-error overlap of non-word errors in a picture naming task by individuals with jargon aphasia. Method Participants were 17 individuals with Wernicke’s aphasia, who produced a high proportion of non-word errors (>20% of errors on the Philadelphia Naming Test (PNT; Roach et al., 1996. The data were retrieved from the Moss Aphasic Psycholinguistic Database Project (MAPPD, Mirman et al., 2010. We used a series of mixed models to test whether lexical variables predicted accuracy, error type (real word vs. non-word and target-error overlap for the PNT data. As lexical variables tend to be highly correlated, we performed a principal components analysis to reduce the variables into five components representing variables associated with phonology (length, phonotactic probability, neighbourhood density and neighbourhood frequency, semantics (imageability and concreteness, usage (frequency and age-of-acquisition, name agreement and visual complexity. Results and Discussion Table 1 shows the components that made a significant contribution to each model. Individuals with jargon aphasia produced more correct responses and fewer non-word errors relative to

Model Predictive Control of a Nonlinear System with Known Scheduling Variable

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

Influence of forced respiration on nonlinear dynamics in heart rate variability

DEFF Research Database (Denmark)

Kanters, J K; Højgaard, M V; Agner, E

1997-01-01

Although it is doubtful whether the normal sinus rhythm can be described as low-dimensional chaos, there is evidence for inherent nonlinear dynamics and determinism in time series of consecutive R-R intervals. However, the physiological origin for these nonlinearities is unknown. The aim...... with a metronome set to 12 min(-1). Nonlinear dynamics were measured as the correlation dimension and the nonlinear prediction error. Complexity expressed as correlation dimension was unchanged from normal respiration, 9.1 +/- 0.5, compared with forced respiration, 9.3 +/- 0.6. Also, nonlinear determinism...... expressed as the nonlinear prediction error did not differ between spontaneous respiration, 32.3 +/- 3.4 ms, and forced respiration, 31.9 +/- 5.7. It is concluded that the origin of the nonlinear dynamics in heart rate variability is not a nonlinear input from the respiration into the cardiovascular...

Predicting sun protection behaviors using protection motivation variables.

Science.gov (United States)

Ch'ng, Joanne W M; Glendon, A Ian

2014-04-01

Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

Antipredator defenses predict diversification rates

Science.gov (United States)

Arbuckle, Kevin; Speed, Michael P.

2015-01-01

The “escape-and-radiate” hypothesis predicts that antipredator defenses facilitate adaptive radiations by enabling escape from constraints of predation, diversified habitat use, and subsequently speciation. Animals have evolved diverse strategies to reduce the direct costs of predation, including cryptic coloration and behavior, chemical defenses, mimicry, and advertisement of unprofitability (conspicuous warning coloration). Whereas the survival consequences of these alternative defenses for individuals are well-studied, little attention has been given to the macroevolutionary consequences of alternative forms of defense. Here we show, using amphibians as the first, to our knowledge, large-scale empirical test in animals, that there are important macroevolutionary consequences of alternative defenses. However, the escape-and-radiate hypothesis does not adequately describe them, due to its exclusive focus on speciation. We examined how rates of speciation and extinction vary across defensive traits throughout amphibians. Lineages that use chemical defenses show higher rates of speciation as predicted by escape-and-radiate but also show higher rates of extinction compared with those without chemical defense. The effect of chemical defense is a net reduction in diversification compared with lineages without chemical defense. In contrast, acquisition of conspicuous coloration (often used as warning signals or in mimicry) is associated with heightened speciation rates but unchanged extinction rates. We conclude that predictions based on the escape-and-radiate hypothesis must incorporate the effect of traits on both speciation and extinction, which is rarely considered in such studies. Our results also suggest that knowledge of defensive traits could have a bearing on the predictability of extinction, perhaps especially important in globally threatened taxa such as amphibians. PMID:26483488

Exchange rate variability, market activity and heterogeneity

OpenAIRE

Rime, Dagfinn; Sucarrat, Genaro

2007-01-01

We study the role played by geographic and bank-size heterogeneity in the relation between exchange rate variability and market activity. We find some support for the hypothesis that increases in short-term global interbank market activity, which can be interpreted as due to variation in information arrival, increase variability. However, our results do not suggest that local short-term activity increases variability. With respect to long-term market activity, which can be interpreted as a me...

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Directory of Open Access Journals (Sweden)

Minh Vu Trieu

2017-03-01

Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Science.gov (United States)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

THE RELATIONSHIP BETWEEN MACROECONOMIC VARIABLES AND ROMANIAN CORPORATE DEFAULT RATES BETWEEN 2002-2008

Directory of Open Access Journals (Sweden)

Suveg Orsolya

2011-07-01

the variables, the volatility of the BET-C index proves to be the most important in predicting the evolution of the default rates, as it didn't proved to be significant only for the construction sector. The evolution of FDI and the volatility of the BET-C index proved to be very important in determining the evolution of the corporate default rates, as well. The first was a very important factor in the financing of companies, especially during the analyzed period, and the risk meter is something that never should be disregarded when it comes of analyzing default rates.

Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2011-10-31

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain

Heart rate variability in bipolar disorder

DEFF Research Database (Denmark)

Faurholt-Jepsen, Maria; Kessing, Lars Vedel; Munkholm, Klaus

2017-01-01

Background Heart rate variability (HRV) has been suggested reduced in bipolar disorder (BD) compared with healthy individuals (HC). This meta-analysis investigated: HRV differences in BD compared with HC, major depressive disorder or schizophrenia; HRV differences between affective states; HRV...

Predictive modeling and reducing cyclic variability in autoignition engines

Science.gov (United States)

Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

2016-08-30

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

HEART RATE VARIABILITY CLASSIFICATION USING SADE-ELM CLASSIFIER WITH BAT FEATURE SELECTION

Directory of Open Access Journals (Sweden)

R Kavitha

2017-07-01

Full Text Available The electrical activity of the human heart is measured by the vital bio medical signal called ECG. This electrocardiogram is employed as a crucial source to gather the diagnostic information of a patient’s cardiopathy. The monitoring function of cardiac disease is diagnosed by documenting and handling the electrocardiogram (ECG impulses. In the recent years many research has been done and developing an enhanced method to identify the risk in the patient’s body condition by processing and analysing the ECG signal. This analysis of the signal helps to find the cardiac abnormalities, arrhythmias, and many other heart problems. ECG signal is processed to detect the variability in heart rhythm; heart rate variability is calculated based on the time interval between heart beats. Heart Rate Variability HRV is measured by the variation in the beat to beat interval. The Heart rate Variability (HRV is an essential aspect to diagnose the properties of the heart. Recent development enhances the potential with the aid of non-linear metrics in reference point with feature selection. In this paper, the fundamental elements are taken from the ECG signal for feature selection process where Bat algorithm is employed for feature selection to predict the best feature and presented to the classifier for accurate classification. The popular machine learning algorithm ELM is taken for classification, integrated with evolutionary algorithm named Self- Adaptive Differential Evolution Extreme Learning Machine SADEELM to improve the reliability of classification. It combines Effective Fuzzy Kohonen clustering network (EFKCN to be able to increase the accuracy of the effect for HRV transmission classification. Hence, it is observed that the experiment carried out unveils that the precision is improved by the SADE-ELM method and concurrently optimizes the computation time.

Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

NARCIS (Netherlands)

Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

NARCIS (Netherlands)

Roelen, C.A.M.; Stapelfeldt, C.M.; Heijmans, M.W.; van Rhenen, W.; Labriola, M.; Nielsen, C.V.; Bultmann, U.; Jensen, C.

2015-01-01

Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models’ risk discrimination was also investigated. Methods 2,562 municipal eldercare

Predictability and Variability of Wave and Wind

DEFF Research Database (Denmark)

Chozas, Julia Fernandez; Kofoed, Jens Peter; Sørensen, Hans Christian

This project covers two fields of study: a) Wave energy predictability and electricity markets. b) Variability of the power output of WECs in diversified systems : diversified renewable systems with wave and offshore wind production. See page 2-4 in the report for a executive summery....

[Voluntary alpha-power increasing training impact on the heart rate variability].

Science.gov (United States)

Bazanova, O M; Balioz, N V; Muravleva, K B; Skoraia, M V

2013-01-01

biofeedback training has no such effect. The positive correlation between the alpha-peak frequency and pNN50 in patients with initially low, but negative--those with high baseline alpha frequency explains the multidirectional biofeedback effects on HRV in low and high alpha frequency subjects. The individual alpha-frequency EEG pattern determines the effectiveness of the alpha EEG biofeedback training in changing heart rate variability, which provides a basis for predicting the results and develop individual approaches to the biofeedback technology implementation that can be used in clinical practice for treatment and rehabilitation of psychosomatic syndromes and in educational training.

Entropies, Partitionings and Heart Rate Variability

Czech Academy of Sciences Publication Activity Database

Paluš, Milan; Zebrowski, J.

2009-01-01

Roč. 51, č. 2 (2009), s. 65-72 ISSN 0001-7604 Institutional research plan: CEZ:AV0Z10300504 Keywords : coarse-grained entropy rate * HR variability * entropy Subject RIV: BB - Applied Statistics, Operational Research http://www.activitas.org/index.php/nervosa/article/view/25

Predicting Performance Ratings Using Motivational Antecedents

National Research Council Canada - National Science Library

Zazania, Michelle

1998-01-01

This research examined the role of motivation in predicting peer and trainer ratings of student performance and contrasted the relative importance of various antecedents for peer and trainer ratings...

Optimal no-go theorem on hidden-variable predictions of effect expectations

Science.gov (United States)

Blass, Andreas; Gurevich, Yuri

2018-03-01

No-go theorems prove that, under reasonable assumptions, classical hidden-variable theories cannot reproduce the predictions of quantum mechanics. Traditional no-go theorems proved that hidden-variable theories cannot predict correctly the values of observables. Recent expectation no-go theorems prove that hidden-variable theories cannot predict the expectations of observables. We prove the strongest expectation-focused no-go theorem to date. It is optimal in the sense that the natural weakenings of the assumptions and the natural strengthenings of the conclusion make the theorem fail. The literature on expectation no-go theorems strongly suggests that the expectation-focused approach is more general than the value-focused one. We establish that the expectation approach is not more general.

An empirical model for prediction of household solid waste generation rate - A case study of Dhanbad, India.

Science.gov (United States)

Kumar, Atul; Samadder, S R

2017-10-01

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R 2 ) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R 2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Heart Rate Variability Interventions for Concussion and Rehabilitation

Directory of Open Access Journals (Sweden)

Robert Lake Conder

2014-08-01

Full Text Available The study of Heart Rate Variability (HRV has emerged as an essential component of cardiovascular health, as well as a physiological mechanism by which one can increase the interactive communication between the cardiac and the neurocognitive systems (i.e., the body and the brain. It is well-established that lack of heart rate variability implies cardiopathology, morbidity, reduced quality-of-life, and precipitous mortality. On the positive, optimal heart rate variability has been associated with good cardiovascular health, autonomic nervous system (ANS control, emotional regulation, and enhanced neurocognitive processing. In addition to health benefits, optimal HRV has been shown to improve neurocognitive performance by enhancing focus, visual acuity and readiness, and by promoting emotional regulation needed for peak performance. In concussed athletes and soldiers, concussions not only alter brain connectivity, but also alter cardiac functioning and impair cardiovascular performance upon exertion. Altered sympathetic and parasympathetic balance in the ANS has been postulated as a critical factor in refractory Post Concussive Syndrome (PCS. This article will review both the pathological aspects of reduced heart rate variability on athletic performance, as well as the cardiovascular and cerebrovascular components of concussion and PCS. Additionally, this article will review interventions with HRV biofeedback (HRV BFB training as a promising and underutilized treatment for sports and military-related concussion. Finally, this article will review research and promising case studies pertaining to use of HRV BFB for enhancement of cognition and performance, with applicability to concussion rehabilitation.

A predictability study of Lorenz's 28-variable model as a dynamical system

Science.gov (United States)

Krishnamurthy, V.

1993-01-01

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

Science.gov (United States)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with

Heart Rate Variability Is Associated with Exercise Capacity in Patients with Cardiac Syndrome X.

Directory of Open Access Journals (Sweden)

Dai-Yin Lu

Full Text Available Heart rate variability (HRV reflects the healthiness of autonomic nervous system, which is associated with exercise capacity. We therefore investigated whether HRV could predict the exercise capacity in the adults with cardiac syndrome X (CSX. A total of 238 subjects (57±12 years, 67.8% men, who were diagnosed as CSX by the positive exercise stress test and nearly normal coronary angiogram were enrolled. Power spectrum from the 24-hour recording of heart rate was analyzed in frequency domain using total power (TP and spectral components of the very low frequency (VLF, low frequency (LF and high frequency (HF ranges. Among the study population, 129 subjects with impaired exercise capacity during the treadmill test had significantly lower HRV indices than those with preserved exercise capacity (≥90% of the age predicted maximal heart rate. After accounting for age, sex, and baseline SBP and heart rate, VLF (odds ratio per 1SD and 95% CI: 2.02, 1.19-3.42, LF (1.67, 1.10-2.55, and TP (1.82, 1.17-2.83 remained significantly associated with preserved exercise capacity. In addition, increased HRV indices were also associated with increased exercise duration, rate-pressure product, and heart rate recovery, independent of age, body mass index, and baseline SBP and heart rate. In subgroup analysis, HRV indices demonstrated similar predictive values related to exercise capacity across various subpopulations, especially in the young. In patients with CSX, HRV was independently associated with exercise capacity, especially in young subjects. The healthiness of autonomic nervous system may have a role in modulating the exercise capacity in patients with CSX.

13 CFR 120.214 - What conditions apply for variable interest rates?

Science.gov (United States)

2010-01-01

... interest rates? 120.214 Section 120.214 Business Credit and Assistance SMALL BUSINESS ADMINISTRATION BUSINESS LOANS Policies Specific to 7(a) Loans Maturities; Interest Rates; Loan and Guarantee Amounts § 120.214 What conditions apply for variable interest rates? A Lender may use a variable rate of interest...

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

DEFF Research Database (Denmark)

Roelen, Corné; Thorsen, Sannie; Heymans, Martijn

2018-01-01

LTSA during follow-up. Results: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC...... population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between...... employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience...

Intraindividual Variability in Basic Reaction Time Predicts Middle-Aged and Older Pilots’ Flight Simulator Performance

Science.gov (United States)

2013-01-01

Objectives. Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Method. Two-hundred and thirty-six pilots (40–69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Results. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%–12% of the negative age effect on initial flight performance. Discussion. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance. PMID:23052365

Intraindividual variability in basic reaction time predicts middle-aged and older pilots' flight simulator performance.

Science.gov (United States)

Kennedy, Quinn; Taylor, Joy; Heraldez, Daniel; Noda, Art; Lazzeroni, Laura C; Yesavage, Jerome

2013-07-01

Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Two-hundred and thirty-six pilots (40-69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%-12% of the negative age effect on initial flight performance. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance.

Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.

Science.gov (United States)

Bielak, Allison A M; Hultsch, David F; Strauss, Esther; Macdonald, Stuart W S; Hunter, Michael A

2010-11-01

Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later. Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used. Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p < .01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p < .01, Exp(β) = 1.24. For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed. (c) 2010 APA, all rights reserved

Leg pain and psychological variables predict outcome 2-3 years after lumbar fusion surgery.

Science.gov (United States)

Abbott, Allan D; Tyni-Lenné, Raija; Hedlund, Rune

2011-10-01

Prediction studies testing a thorough range of psychological variables in addition to demographic, work-related and clinical variables are lacking in lumbar fusion surgery research. This prospective cohort study aimed at examining predictions of functional disability, back pain and health-related quality of life (HRQOL) 2-3 years after lumbar fusion by regressing nonlinear relations in a multivariate predictive model of pre-surgical variables. Before and 2-3 years after lumbar fusion surgery, patients completed measures investigating demographics, work-related variables, clinical variables, functional self-efficacy, outcome expectancy, fear of movement/(re)injury, mental health and pain coping. Categorical regression with optimal scaling transformation, elastic net regularization and bootstrapping were used to investigate predictor variables and address predictive model validity. The most parsimonious and stable subset of pre-surgical predictor variables explained 41.6, 36.0 and 25.6% of the variance in functional disability, back pain intensity and HRQOL 2-3 years after lumbar fusion. Pre-surgical control over pain significantly predicted functional disability and HRQOL. Pre-surgical catastrophizing and leg pain intensity significantly predicted functional disability and back pain while the pre-surgical straight leg raise significantly predicted back pain. Post-operative psychomotor therapy also significantly predicted functional disability while pre-surgical outcome expectations significantly predicted HRQOL. For the median dichotomised classification of functional disability, back pain intensity and HRQOL levels 2-3 years post-surgery, the discriminative ability of the prediction models was of good quality. The results demonstrate the importance of pre-surgical psychological factors, leg pain intensity, straight leg raise and post-operative psychomotor therapy in the predictions of functional disability, back pain and HRQOL-related outcomes.

The Effect of Sex on Heart Rate Variability at High Altitude.

Science.gov (United States)

Boos, Christopher John; Vincent, Emma; Mellor, Adrian; O'Hara, John; Newman, Caroline; Cruttenden, Richard; Scott, Phylip; Cooke, Mark; Matu, Jamie; Woods, David Richard

2017-12-01

There is evidence suggesting that high altitude (HA) exposure leads to a fall in heart rate variability (HRV) that is linked to the development of acute mountain sickness (AMS). The effects of sex on changes in HRV at HA and its relationship to AMS are unknown. HRV (5-min single-lead ECG) was measured in 63 healthy adults (41 men and 22 women) 18-56 yr of age at sea level (SL) and during a HA trek at 3619, 4600, and 5140 m, respectively. The main effects of altitude (SL, 3619 m, 4600 m, and 5140 m) and sex (men vs women) and their potential interaction were assessed using a factorial repeated-measures ANOVA. Logistic regression analyses were performed to assess the ability of HRV to predict AMS. Men and women were of similar age (31.2 ± 9.3 vs 31.7 ± 7.5 yr), ethnicity, and body and mass index. There was main effect for altitude on heart rate, SD of normal-to-normal (NN) intervals (SDNN), root mean square of successive differences (RMSSD), number of pairs of successive NN differing by >50 ms (NN50), NN50/total number of NN, very low-frequency power, low-frequency (LF) power, high-frequency (HF) power, and total power (TP). The most consistent effect on post hoc analysis was reduction in these HRV measures between 3619 and 5140 m at HA. Heart rate was significantly lower and SDNN, RMSSD, LF power, HF power, and TP were higher in men compared with women at HA. There was no interaction between sex and altitude for any of the HRV indices measured. HRV was not predictive of AMS development. Increasing HA leads to a reduction in HRV. Significant differences between men and women emerge at HA. HRV was not predictive of AMS.

Discrepancies between self and observer ratings of depression. The relationship to demographic, clinical and personality variables.

Science.gov (United States)

Enns, M W; Larsen, D K; Cox, B J

2000-10-01

The observer-rated Hamilton depression scale (HamD) and the self-report Beck Depression Inventory (BDI) are among the most commonly used rating scales for depression, and both have well demonstrated reliability and validity. However, many depressed subjects have discrepant scores on these two assessment methods. The present study evaluated the ability of demographic, clinical and personality factors to account for the discrepancies observed between BDI and HamD ratings. The study group consisted of 94 SCID-diagnosed outpatients with a current major depressive disorder. Subjects were rated with the 21-item HamD and completed the BDI and the NEO-Five Factor Inventory. Younger age, higher educational attainment, and depressive subtype (atypical, non-melancholic) were predictive of higher BDI scores relative to HamD observer ratings. In addition, high neuroticism, low extraversion and low agreeableness were associated with higher endorsement of depressive symptoms on the BDI relative to the HamD. In general, these predictive variables showed a greater ability to explain discrepancies between self and observer ratings of psychological symptoms of depression compared to somatic symptoms of depression. The study does not determine which aspects of neuroticism and extraversion contribute to the observed BDI/HamD discrepancies. Depression ratings obtained with the BDI and HamD are frequently discordant and a number of patient characteristics robustly predict the discrepancy between these two rating methods. The value of multi-modal assessment in the conduct of research on depressive disorders is re-affirmed.

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

Science.gov (United States)

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

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

Science.gov (United States)

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.

Transforming variability to profitability – variable seed rates in New Zealand maize

OpenAIRE

Holmes, A

2017-01-01

The use of variable rate seeding (VRS) in arable crops to match seeding rates to areas with homogenous paddock performance, known as Management Zones (MZ) is widespread worldwide. However, VRS has not been undertaken in commercial maize crops in New Zealand. This paper outlines a single maize VRS trial carried out in the 2015/16 growing season in the Waikato, New Zealand, to investigate the relationship between different seeding rates and MZ to maximise crop yield, but also gross margin (GM)....

Heart rate variability and baroreflex sensitivity in bilateral lung transplant recipients.

Science.gov (United States)

Fontolliet, Timothée; Gianella, Pietro; Pichot, Vincent; Barthélémy, Jean-Claude; Gasche-Soccal, Paola; Ferretti, Guido; Lador, Frédéric

2018-01-09

The effects of lung afferents denervation on cardiovascular regulation can be assessed on bilateral lung transplantation patients. The high-frequency component of heart rate variability is known to be synchronous with breathing frequency. Then, if heart beat is neurally modulated by breathing frequency, we may expect disappearance of high frequency of heart rate variability in bilateral lung transplantation patients. On 11 patients and 11 matching healthy controls, we measured R-R interval (electrocardiography), blood pressure (Portapres ® ) and breathing frequency (ultrasonic device) in supine rest, during 10-min free breathing, 10-min cadenced breathing (0·25 Hz) and 5-min handgrip. We analysed heart rate variability and spontaneous variability of arterial blood pressure, by power spectral analysis, and baroreflex sensitivity, by the sequence method. Concerning heart rate variability, with respect to controls, transplant recipients had lower total power and lower low- and high-frequency power. The low-frequency/high-frequency ratio was higher. Concerning systolic, diastolic and mean arterial pressure variability, transplant recipients had lower total power (only for cadenced breathing), low frequency and low-frequency/high-frequency ratio during free and cadenced breathing. Baroreflex sensitivity was decreased. Denervated lungs induced strong heart rate variability reduction. The higher low-frequency/high-frequency ratio suggested that the total power drop was mostly due to high frequency. These results support the hypothesis that neural modulation from lung afferents contributes to the high frequency of heart rate variability. © 2018 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

The Extent of Variability of Rates of Building Items in Botswana ...

African Journals Online (AJOL)

This paper reports findings of a study carried out to investigate the variability of rates of common building items used in public building projects in Botswana. The paper concludes that tiling and glazing were found to have the highest rate of variability, while reinforcement and masonry had the lowest price variability.

Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

DEFF Research Database (Denmark)

Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh

2013-01-01

This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

Heart rate variability - a historical perspective.

Science.gov (United States)

Billman, George E

2011-01-01

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.

Prediction of Basic Math Course Failure Rate in the Physics, Meteorology, Mathematics, Actuarial Sciences and Pharmacy Degree Programs

Directory of Open Access Journals (Sweden)

Luis Rojas-Torres

2014-09-01

Full Text Available This paper summarizes a study conducted in 2013 with the purpose of predicting the failure rate of math courses taken by Pharmacy, Mathematics, Actuarial Science, Physics and Meteorology students at Universidad de Costa Rica (UCR. Using the Logistics Regression statistical techniques applied to the 2010 cohort, failure rates were predicted of students in the aforementioned programs in one of their Math introductory courses (Calculus 101 for Physics and Meteorology, Math Principles for Mathematics and Actuarial Science and Applied Differential Equations for Pharmacy. For these models, the UCR admission average, the student’s genre, and the average correct answers in the Quantitative Skills Test were used as predictor variables. The most important variable for all models was the Quantitative Skills Test, and the model with the highest correct classification rate was the Logistics Regression. For the estimated Physics-Meteorology, Pharmacy and Mathematics-Actuarial Science models, correct classifications were 89.8%, 73.6%, and 93.9%, respectively.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

Science.gov (United States)

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

Hydroclimatic variability and predictability: a survey of recent research

Directory of Open Access Journals (Sweden)

R. D. Koster

2017-07-01

Full Text Available Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i variability in general, (ii droughts, (iii floods, (iv land–atmosphere coupling, and (v hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

Science.gov (United States)

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

Two-Stage Variable Sample-Rate Conversion System

Science.gov (United States)

Tkacenko, Andre

2009-01-01

A two-stage variable sample-rate conversion (SRC) system has been pro posed as part of a digital signal-processing system in a digital com munication radio receiver that utilizes a variety of data rates. The proposed system would be used as an interface between (1) an analog- todigital converter used in the front end of the receiver to sample an intermediatefrequency signal at a fixed input rate and (2) digita lly implemented tracking loops in subsequent stages that operate at v arious sample rates that are generally lower than the input sample r ate. This Two-Stage System would be capable of converting from an input sample rate to a desired lower output sample rate that could be var iable and not necessarily a rational fraction of the input rate.

Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating

Directory of Open Access Journals (Sweden)

Bingkun Wang

2016-01-01

Full Text Available 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.

Social integration prospectively predicts changes in heart rate variability among individuals undergoing migration stress.

Science.gov (United States)

Gouin, Jean-Philippe; Zhou, Biru; Fitzpatrick, Stephanie

2015-04-01

Poor social integration increases risk for poor health. The psychobiological pathways underlying this effect are not well-understood. This study utilized a migration stress model to prospectively investigate the impact of social integration on change in high-frequency heart rate variability (HF-HRV), a marker of autonomic functioning. Sixty new international students were recruited shortly after their arrival in the host country and assessed 2 and 5 months later. At each assessment period, participants provided information on social integration and loneliness and had their resting HF-HRV evaluated. There was an overall decrease in HF-HRV over time. The magnitude of the within-person and between-person effects of social integration on HRV increased over time, such that greater social integration was associated with higher HF-HRV at later follow-ups. These results suggest that altered autonomic functioning might represent a key pathway linking social integration to health outcomes.

Variable context Markov chains for HIV protease cleavage site prediction.

Science.gov (United States)

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

Real Exchange Rate Variability: An Empirical Analysis of the Developing Countries Case

OpenAIRE

Sebastian Edwards

1986-01-01

The purpose of this paper is to investigate the potential role of monetary and real factors in explaining real exchange rate variability in developing countries. For this purpose two indexes of real effective exchange rate variability that measure short-term and long-term variability were constructed for 30 countries. The results obtained, using a generalized least squares procedures on cross section data, indicate that real exchange rate variability has been affected both by real and monetar...

Variable dose rate single-arc IMAT delivered with a constant dose rate and variable angular spacing

International Nuclear Information System (INIS)

Tang, Grace; Earl, Matthew A; Yu, Cedric X

2009-01-01

Single-arc intensity-modulated arc therapy (IMAT) has gained worldwide interest in both research and clinical implementation due to its superior plan quality and delivery efficiency. Single-arc IMAT techniques such as the Varian RapidArc(TM) deliver conformal dose distributions to the target in one single gantry rotation, resulting in a delivery time in the order of 2 min. The segments in these techniques are evenly distributed within an arc and are allowed to have different monitor unit (MU) weightings. Therefore, a variable dose-rate (VDR) is required for delivery. Because the VDR requirement complicates the control hardware and software of the linear accelerators (linacs) and prevents most existing linacs from delivering IMAT, we propose an alternative planning approach for IMAT using constant dose-rate (CDR) delivery with variable angular spacing. We prove the equivalence by converting VDR-optimized RapidArc plans to CDR plans, where the evenly spaced beams in the VDR plan are redistributed to uneven spacing such that the segments with larger MU weighting occupy a greater angular interval. To minimize perturbation in the optimized dose distribution, the angular deviation of the segments was restricted to ≤± 5 deg. This restriction requires the treatment arc to be broken into multiple sectors such that the local MU fluctuation within each sector is reduced, thereby lowering the angular deviation of the segments during redistribution. The converted CDR plans were delivered with a single gantry sweep as in the VDR plans but each sector was delivered with a different value of CDR. For four patient cases, including two head-and-neck, one brain and one prostate, all CDR plans developed with the variable spacing scheme produced similar dose distributions to the original VDR plans. For plans with complex angular MU distributions, the number of sectors increased up to four in the CDR plans in order to maintain the original plan quality. Since each sector was

Smartphone-Enabled Heart Rate Variability and Acute Mountain Sickness.

Science.gov (United States)

Mellor, Adrian; Bakker-Dyos, Josh; OʼHara, John; Woods, David Richard; Holdsworth, David A; Boos, Christopher J

2018-01-01

The autonomic system and sympathetic activation appears integral in the pathogenesis of acute mountain sickness (AMS) at high altitude (HA), yet a link between heart rate variability (HRV) and AMS has not been convincingly shown. In this study we investigated the utility of the smartphone-derived HRV score to predict and diagnose AMS at HA. Twenty-one healthy adults were investigated at baseline at 1400 m and over 10 days during a trek to 5140 m. HRV was recorded using the ithlete HRV device. Acute mountain sickness occurred in 11 subjects (52.4%) at >2650 m. HRV inversely correlated with AMS Scores (r = -0.26; 95% CI, -0.38 to -0.13: P HRV significantly fell at 3700, 4100, and 5140 m versus low altitude. HRV scores were lower in those with both mild (69.7 ± 14.0) and severe AMS (67.1 ± 13.1) versus those without AMS (77.5 ± 13.1; effect size n = 0.043: P = 0.007). The HRV score was weakly predictive of severe AMS (AUC 0.74; 95% CI, 0.58-0.89: P = 0.006). The change (delta) in the HRV Score (compared with baseline at 1400 m) was a moderate diagnostic marker of severe AMS (AUC 0.80; 95% CI, 0.70-0.90; P = 0.0004). A fall in the HRV score of >5 had a sensitivity of 83% and specificity of 60% to identify severe AMS (likelihood ratio 1.9). Baseline HRV at 1400 m was not predictive of either AMS at higher altitudes. The ithlete HRV score can be used to help in the identification of severe AMS; however, a baseline score is not predictive of future AMS development at HA.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Science.gov (United States)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

The role of heart rate variability in sports physiology.

Science.gov (United States)

Dong, Jin-Guo

2016-05-01

Heart rate variability (HRV) is a relevant marker reflecting cardiac modulation by sympathetic and vagal components of the autonomic nervous system (ANS). Although the clinical application of HRV is mainly associated with the prediction of sudden cardiac death and assessing cardiovascular and metabolic illness progression, recent observations have suggested its applicability to physical exercise training. HRV is becoming one of the most useful tools for tracking the time course of training adaptation/maladaptation of athletes and in setting the optimal training loads leading to improved performances. However, little is known regarding the role of HRV and the internal effects of physical exercise on an athlete, which may be useful in designing fitness programs ensuring sufficient training load that may correspond with the specific ability of the athlete. In this review, we offer a comprehensive assessment of investigations concerning the interrelation between HRV and ANS, and examine how the application of HRV to physical exercise may play a role in sports physiology.

Days on radiosensitivity: individual variability and predictive tests

International Nuclear Information System (INIS)

2008-01-01

The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

Evolutionary rate variation and RNA secondary structure prediction

DEFF Research Database (Denmark)

Knudsen, B.; Andersen, E.S.; Damgaard, C.

2004-01-01

Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type...... by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions...

Gaussian Mixture Model of Heart Rate Variability

Science.gov (United States)

Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario

2012-01-01

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

Science.gov (United States)

Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

2018-01-01

The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

Increased heart rate variability but normal resting metabolic rate in hypocretin/orexin-deficient human narcolepsy.

NARCIS (Netherlands)

Fronczek, R.; Overeem, S.; Reijntjes, R.; Lammers, G.J.; Dijk, J.G.M.; Pijl, H.

2008-01-01

STUDY OBJECTIVES: We investigated autonomic balance and resting metabolic rate to explore their possible involvement in obesity in hypocretin/orexin-deficient narcoleptic subjects. METHODS: Resting metabolic rate (using indirect calorimetry) and variability in heart rate and blood pressure were

An application of the variable-r method to subpopulation growth rates in a 19th century agricultural population

Directory of Open Access Journals (Sweden)

Corey Sparks

2009-07-01

Full Text Available This paper presents an analysis of the differential growth rates of the farming and non-farming segments of a rural Scottish community during the 19th and early 20th centuries using the variable-r method allowing for net migration. Using this method, I find that the farming population of Orkney, Scotland, showed less variability in their reproduction and growth rates than the non-farming population during a period of net population decline. I conclude by suggesting that the variable-r method can be used in general cases where the relative growth of subpopulations or subpopulation reproduction is of interest.

Physiologically-based, predictive analytics using the heart-rate-to-Systolic-Ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS.

Science.gov (United States)

Danner, Omar K; Hendren, Sandra; Santiago, Ethel; Nye, Brittany; Abraham, Prasad

2017-04-01

Enhancing the efficiency of diagnosis and treatment of severe sepsis by using physiologically-based, predictive analytical strategies has not been fully explored. We hypothesize assessment of heart-rate-to-systolic-ratio significantly increases the timeliness and accuracy of sepsis prediction after emergency department (ED) presentation. We evaluated the records of 53,313 ED patients from a large, urban teaching hospital between January and June 2015. The HR-to-systolic ratio was compared to SIRS criteria for sepsis prediction. There were 884 patients with discharge diagnoses of sepsis, severe sepsis, and/or septic shock. Variations in three presenting variables, heart rate, systolic BP and temperature were determined to be primary early predictors of sepsis with a 74% (654/884) accuracy compared to 34% (304/884) using SIRS criteria (p < 0.0001)in confirmed septic patients. Physiologically-based predictive analytics improved the accuracy and expediency of sepsis identification via detection of variations in HR-to-systolic ratio. This approach may lead to earlier sepsis workup and life-saving interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

Analyst-to-Analyst Variability in Simulation-Based Prediction

Energy Technology Data Exchange (ETDEWEB)

Glickman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Romero, Vicente J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.

Predicting travel time variability for cost-benefit analysis

NARCIS (Netherlands)

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

2010-01-01

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

Gender differences in the predictive role of self-rated health on short-term risk of mortality among older adults

Directory of Open Access Journals (Sweden)

Shervin Assari

2016-09-01

Full Text Available Objectives: Despite the well-established association between self-rated health and mortality, research findings have been inconsistent regarding how men and women differ on this link. Using a national sample in the United States, this study compared American male and female older adults for the predictive role of baseline self-rated health on the short-term risk of mortality. Methods: This longitudinal study followed 1500 older adults (573 men (38.2% and 927 women (61.8% aged 66 years or older for 3 years from 2001 to 2004. The main predictor of interest was self-rated health, which was measured using a single item in 2001. The outcome was the risk of all-cause mortality during the 3-year follow-up period. Demographic factors (race and age, socio-economic factors (education and marital status, and health behaviors (smoking and drinking were covariates. Gender was the focal moderator. We ran logistic regression models in the pooled sample and also stratified by gender, with self-rated health treated as either nominal variables, poor compared to other levels (i.e. fair, good, or excellent or excellent compared to other levels (i.e. good, fair, or poor, or an ordinal variable. Results: In the pooled sample, baseline self-rated health predicted mortality risk, regardless of how the variable was treated. We found a significant interaction between gender and poor self-rated health, indicating a stronger effect of poor self-rated health on mortality risk for men compared to women. Gender did not interact with excellent self-rated health on mortality. Conclusion: Perceived poor self-rated health better reflects risk of mortality over a short period of time for older men compared to older women. Clinicians may need to take poor self-rated health of older men very seriously. Future research should test whether the differential predictive validity of self-rated health based on gender is due to a different meaning of poor self-rated health for older men

Approximate entropy and point correlation dimension of heart rate variability in healthy subjects

DEFF Research Database (Denmark)

Storella, R J; Wood, H W; Mills, K M

1999-01-01

The contribution of nonlinear dynamics to heart rate variability in healthy humans was examined using surrogate data analysis. Several measures of heart rate variability were used and compared. Heart rates were recorded for three hours and original data sets of 8192 R-R intervals created. For each...... original data set (n = 34), three surrogate data sets were made by shuffling the order of the R-R intervals while retaining their linear correlations. The difference in heart rate variability between the original and surrogate data sets reflects the amount of nonlinear structure in the original data set....... Heart rate variability was analyzed by two different nonlinear methods, point correlation dimension and approximate entropy. Nonlinearity, though under 10 percent, could be detected with both types of heart rate variability measures. More importantly, not only were the correlations between...

Heart rate variability reflects self-regulatory strength, effort, and fatigue.

Science.gov (United States)

Segerstrom, Suzanne C; Nes, Lise Solberg

2007-03-01

Experimental research reliably demonstrates that self-regulatory deficits are a consequence of prior self-regulatory effort. However, in naturalistic settings, although people know that they are sometimes vulnerable to saying, eating, or doing the wrong thing, they cannot accurately gauge their capacity to self-regulate at any given time. Because self-regulation and autonomic regulation colocalize in the brain, an autonomic measure, heart rate variability (HRV), could provide an index of self-regulatory strength and activity. During an experimental manipulation of self-regulation (eating carrots or cookies), HRV was elevated during high self-regulatory effort (eat carrots, resist cookies) compared with low self-regulatory effort (eat cookies, resist carrots). The experimental manipulation and higher HRV at baseline independently predicted persistence at a subsequent anagram task. HRV appears to index self-regulatory strength and effort, making it possible to study these phenomena in the field as well as the lab.

Weight suppression predicts total weight gain and rate of weight gain in outpatients with anorexia nervosa.

Science.gov (United States)

Carter, Frances A; Boden, Joseph M; Jordan, Jennifer; McIntosh, Virginia V W; Bulik, Cynthia M; Joyce, Peter R

2015-11-01

The present study sought to replicate the finding of Wildes and Marcus, Behav Res Ther, 50, 266-274, 2012 that higher levels of weight suppression at pretreatment predict greater total weight gain, faster rate of weight gain, and bulimic symptoms amongst patients admitted with anorexia nervosa. Participants were 56 women with anorexia nervosa diagnosed by using strict or lenient weight criteria, who were participating in a randomized controlled psychotherapy trial (McIntosh et al., Am J Psychiatry, 162, 741-747, 2005). Thirty-five women completed outpatient treatment and post-treatment assessment. Weight suppression was the discrepancy between highest lifetime weight at adult height and weight at pretreatment assessment. Outcome variables were total weight gain, rate of weight gain, and bulimic symptoms in the month prior to post-treatment assessment [assessed using the Eating Disorders Examination (Fairburn et al., Binge-Eating: Nature, Assessment and Treatment. New York: Guilford, 1993)]. Weight suppression was positively associated with total weight gain and rate of weight gain over treatment. Regression models showed that this association could not be explained by covariates (age at onset of anorexia nervosa and treatment modality). Weight suppression was not significantly associated with bulimic symptoms in the month prior to post-treatment assessment, regardless of whether bulimic symptoms were examined as continuous or dichotomous variables. The present study reinforces the previous finding that weight suppression predicts total weight gain and rate of weight gain amongst patients being treated for anorexia nervosa. Methodological issues may explain the failure of the present study to find that weight suppression predicts bulimic symptoms. Weight suppression at pretreatment for anorexia nervosa should be assessed routinely and may inform treatment planning. © 2015 Wiley Periodicals, Inc.

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

OpenAIRE

Alejandro Baldominos Gómez; Esperanza Albacete; Ignacio Merrero; Yago Saez

2016-01-01

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user's actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to ...

Does the ability to express different emotions predict different indices of physical health? A skill-based study of physical symptoms and heart rate variability.

Science.gov (United States)

Tuck, Natalie L; Adams, Kathryn S; Consedine, Nathan S

2017-09-01

The outward expression of emotion has been frequently associated with better health outcomes, whereas suppressing emotion is thought to contribute to worse physical health. However, work has typically focused on trait expressive tendencies and the possibility that individual differences in the ability to express specific emotions may also be associated with health has not been widely tested. A cross-sectional study of community dwelling adults. One hundred and twenty-eight participants aged 18-88 years completed questionnaires assessing demographics and health status, before attending a testing session in which resting heart rate variability (HRV) was assessed. Participants then completed a performance-based test of expressive regulatory skill in which they were instructed to enhance and suppress their emotional expressions while they watched film clips validated to elicit amusement, sadness, and anger. Participants rated subjective emotional experience before and after each clip, and their degree of expressivity was scored using FACS-based Noldus FaceReader. Missing data resulted in a final sample size of 117. Linear regressions controlling for age, sex, diagnoses, and trait emotion revealed that greater ability to enhance sad expressions was associated with higher HRV while the ability to enhance expressions of joy was associated with lower symptom interference. In parallel models, the ability to flexibly regulate (both enhance and suppress) expressions of joy and sadness was also associated with lower symptom interference. Findings suggest that the ability to regulate expressions of both sadness and joy is associated with health indices even when controlling for trait affect and potential confounds. The present findings offer early evidence that individual differences in the ability to regulate the outward expression of emotion may be relevant to health and suggest that expressive regulatory skills offer a novel avenue for research and intervention. Statement

Effects of social stress on heart rate and heart rate variability in growing pigs

NARCIS (Netherlands)

Jong, de I.C.; Sgoifo, A.; Lambooij, E.; Korte, S.M.; Blokhuis, H.J.; Koolhaas, J.M.

2000-01-01

The effects of social stress on heart rate, heart rate variability and the occurrence of cardiac arrhythmias were studied in 12 growing pigs. Social stress was induced during a good competition test with a pen mate, and subsequently during a resident-intruder test with an unacquainted pig in which

Effects of social stress on heart rate and heart rate variability in growing pigs

NARCIS (Netherlands)

de Jong, IC; Sgoifo, A; Lambooij, E; Korte, SM; Blokhuis, HJ; Koolhaas, JM

The effects of social stress on heart rate, heart rate variability and the occurrence of cardiac arrhythmias were studied in 12 growing pigs. Social stress was induced during a good competition test with a pen mate, and subsequently during a resident-intruder test with an unacquainted pig in which

Review of some advances of the literature about predictive variables concerning subjective well-being

Directory of Open Access Journals (Sweden)

Gloria Cajiao

2013-06-01

Full Text Available This review of scientific literature presents some tendencies, conceptual advances, empirical findings and tests that measure the predictive variables of subjective well-being. It was done through the search in bibliographical database like ProQuest, PsycArticles, Psyctest, OVID SP, books and Thesis. Two types of predictive variables were recognized- internal and external to the individual-. Both of them influence the achievement of the subjective well-being. Besides, the studies and conceptualization about Subjetive well-being and some of the Predictive Variables were analyzed in the conclusion.

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (QSAR/Read-Across Predictions

Directory of Open Access Journals (Sweden)

Christoph Helma

2018-04-01

Full Text Available This study compares the accuracy of (QSAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

Can biomechanical variables predict improvement in crouch gait?

Science.gov (United States)

Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

Science.gov (United States)

Gengler, Sarah; Bogaert, Patrick

2014-12-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

International Nuclear Information System (INIS)

Gengler, Sarah; Bogaert, Patrick

2014-01-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression

Use efficiency of variable rate of nitrogen prescribed by optical sensor in corn

Directory of Open Access Journals (Sweden)

Jardes Bragagnolo

2016-02-01

Full Text Available ABSTRACT The efficiency of nitrogen fertilizer in corn is usually low, negatively affecting plant nutrition, the economic return, and the environment. In this context, a variable rate of nitrogen, prescribed by crop sensors, has been proposed as an alternative to the uniform rate of nitrogen traditionally used by farmers. This study tested the hypothesis that variable rate of nitrogen, prescribed by optical sensor, increases the nitrogen use efficiency and grain yield as compared to uniform rate of nitrogen. The following treatments were evaluated: 0; 70; 140; and 210 kg ha-1 under uniform rate of nitrogen, and 140 kg ha -1 under variable rate of nitrogen. The nitrogen source was urea applied on the soil surface using a distributor equipped with the crop sensor. In this study, the grain yield ranged from 10.2 to 15.5 Mg ha-1, with linear response to nitrogen rates. The variable rate of nitrogen increased by 11.8 and 32.6% the nitrogen uptake and nitrogen use efficiency, respectively, compared to the uniform rate of nitrogen. However, no significant increase in grain yield was observed, indicating that the major benefit of the variable rate of nitrogen was reducing the risk of environmental impact of fertilizer.

Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

Directory of Open Access Journals (Sweden)

B. M. Brentan

2017-01-01

Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor.

Science.gov (United States)

Bolea, Juan; Lázaro, Jesús; Gil, Eduardo; Rovira, Eva; Remartínez, José M; Laguna, Pablo; Pueyo, Esther; Navarro, Augusto; Bailón, Raquel

2017-09-01

Prophylactic treatment has been proved to reduce hypotension incidence after spinal anesthesia during cesarean labor. However, the use of pharmacological prophylaxis could carry out undesirable side-effects on mother and fetus. Thus, the prediction of hypotension becomes an important challenge. Hypotension events are hypothesized to be related to a malfunctioning of autonomic nervous system (ANS) regulation of blood pressure. In this work, ANS responses to positional changes of 51 pregnant women programmed for a cesarean labor were explored for hypotension prediction. Lateral and supine decubitus, and sitting position were considered while electrocardiographic and pulse photoplethysmographic signals were recorded. Features based on heart rate variability, pulse rate variability (PRV) and pulse transit time (PTT) analysis were used in a logistic regression classifier. The results showed that PRV irregularity changes, assessed by approximate entropy, from supine to lateral decubitus, and standard deviation of PTT in supine decubitus were found as the combination of features that achieved the best classification results sensitivity of 76%, specificity of 70% and accuracy of 72%, being normotensive the positive class. Peripheral regulation and blood pressure changes, measured by PRV and PTT analysis, could help to predict hypotension events reducing prophylactic side-effects in the low-risk population.

Changes in monthly unemployment rates may predict changes in the number of psychiatric presentations to emergency services in South Australia.

Science.gov (United States)

Bidargaddi, Niranjan; Bastiampillai, Tarun; Schrader, Geoffrey; Adams, Robert; Piantadosi, Cynthia; Strobel, Jörg; Tucker, Graeme; Allison, Stephen

2015-07-24

To determine the extent to which variations in monthly Mental Health Emergency Department (MHED) presentations in South Australian Public Hospitals are associated with the Australian Bureau of Statistics (ABS) monthly unemployment rates. Times series modelling of relationships between monthly MHED presentations to South Australian Public Hospitals derived from the Integrated South Australian Activity Collection (ISAAC) data base and the ABS monthly unemployment rates in South Australia between January 2004-June 2011. Time series modelling using monthly unemployment rates from ABS as a predictor variable explains 69% of the variation in monthly MHED presentations across public hospitals in South Australia. Thirty-two percent of the variation in current month's male MHED presentations can be predicted by using the 2 months' prior male unemployment rate. Over 63% of the variation in monthly female MHED presentations can be predicted by either male or female prior monthly unemployment rates. The findings of this study highlight that even with the relatively favourable economic conditions, small shifts in monthly unemployment rates can predict variations in monthly MHED presentations, particularly for women. Monthly ABS unemployment rates may be a useful metric for predicting demand for emergency mental health services.

Heart rate variability and implication for sport concussion.

Science.gov (United States)

Bishop, Scott A; Dech, Ryan T; Guzik, Przemyslaw; Neary, J Patrick

2017-11-16

Finding sensitive and specific markers for sports-related concussion is both challenging and clinically important. Such biomarkers might be helpful in the management of patients with concussion (i.e. diagnosis, monitoring and risk prediction). Among many parameters, blood flow-pressure metrics and heart rate variability (HRV) have been used to gauge concussion outcomes. Reports on the relation between HRV and both acute and prolonged concussion recovery are conflicting. While some authors report on differences in the low-frequency (LF) component of HRV during postural manipulations and postexercise conditions, others observe no significant differences in various HRV measures. Despite the early success of using the HRV LF for concussion recovery, the interpretation of the LF is debated. Recent research suggests the LF power is a net effect of several intrinsic modulatory factors from both sympathetic and parasympathetic branches of the autonomic nervous system, vagally mediated baroreflex and even some respiratory influences at lower respiratory rate. There are only a few well-controlled concussion studies that specifically examine the contribution of the autonomic nervous system branches with HRV for concussion management. This study reviews the most recent HRV- concussion literature and the underlying HRV physiology. It also highlights cerebral blood flow studies related to concussion and the importance of multimodal assessment of various biological signals. It is hoped that a better understanding of the physiology behind HRV might generate cost-effective, repeatable and reliable protocols, all of which will improve the interpretation of HRV throughout concussion recovery. © 2017 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

International Nuclear Information System (INIS)

Fatemi, M.H.

2006-01-01

Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

Heart rate variability in normal-weight patients with polycystic ovary syndrome.

Science.gov (United States)

Kilit, Celal; Paşalı Kilit, Türkan

2017-05-01

Polycystic ovary syndrome (PCOS) is an endocrine disease closely related to several risk factors of cardiovascular disease. Obese women with PCOS show altered autonomic modulation. The results of studies investigating cardiac autonomic functions of normal-weight women with PCOS are conflicting. The aim of the study was to assess the reactivity of cardiac sympathovagal balance in normal-weight women with PCOS by heart rate variability analysis. We examined the heart rate variability in 60 normal-weight women with PCOS and compared them with that in 60 age-matched healthy women having a similar metabolic profile. Time and frequency domain parameters of heart rate variability were analyzed based on 5-min-long continuous electrocardiography recordings for the following 3 periods: (1) during rest in supine position, (2) during controlled breathing, and (3) during isometric handgrip exercise. Time and frequency domain parameters of heart rate variability for the 3 periods assessed were similar in the two groups. Although modified Ferriman-Gallwey score and serum testosterone and luteinizing hormone levels were significantly higher in women with PCOS, homeostatic model assessment-insulin resistance (HOMA-IR) was not different the between the PCOS and control groups. There were no significant correlations between serum testosterone levels and heart rate variability parameters among the study population. The findings of this study suggest that the reactivity of cardiac sympathovagal balance is not altered in normal-weight women with PCOS having a normal HOMA-IR.

Predicting rates of interspecific interaction from phylogenetic trees.

Science.gov (United States)

Nuismer, Scott L; Harmon, Luke J

2015-01-01

Integrating phylogenetic information can potentially improve our ability to explain species' traits, patterns of community assembly, the network structure of communities, and ecosystem function. In this study, we use mathematical models to explore the ecological and evolutionary factors that modulate the explanatory power of phylogenetic information for communities of species that interact within a single trophic level. We find that phylogenetic relationships among species can influence trait evolution and rates of interaction among species, but only under particular models of species interaction. For example, when interactions within communities are mediated by a mechanism of phenotype matching, phylogenetic trees make specific predictions about trait evolution and rates of interaction. In contrast, if interactions within a community depend on a mechanism of phenotype differences, phylogenetic information has little, if any, predictive power for trait evolution and interaction rate. Together, these results make clear and testable predictions for when and how evolutionary history is expected to influence contemporary rates of species interaction. © 2014 John Wiley & Sons Ltd/CNRS.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

Science.gov (United States)

Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin

2013-10-01

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

Associations between bolus infusion of hydrocortisone, glycemic variability and insulin infusion rate variability in critically Ill patients under moderate glycemic control

NARCIS (Netherlands)

van Hooijdonk, Roosmarijn T. M.; Binnekade, Jan M.; Bos, Lieuwe D. J.; Horn, Janneke; Juffermans, Nicole P.; Abu-Hanna, Ameen; Schultz, Marcus J.

2015-01-01

We retrospectively studied associations between bolus infusion of hydrocortisone and variability of the blood glucose level and changes in insulin rates in intensive care unit (ICU) patients. 'Glycemic variability' and 'insulin infusion rate variability' were calculated from and expressed as the

ATLAS trigger operations: Monitoring with “Xmon” rate prediction system

CERN Document Server

Aukerman, Andrew Todd; The ATLAS collaboration

2017-01-01

We present the operations and online monitoring with the “Xmon” rate prediction system for the trigger system at the ATLAS Experiment. A two-level trigger system reduces the LHC’s bunch-crossing rate, 40 MHz at design capacity, to an average recording rate of about 1 kHz, while maintaining a high efficiency of selecting events of interest. The Xmon system uses the luminosity value to predict trigger rates that are, in turn, compared with incoming rates. The predictions rely on past runs to parameterize the luminosity dependency of the event rate for a trigger algorithm. Some examples are given to illustrate the performance of the tool during recent operations.

Effect of atrioventricular conduction on heart rate variability

KAUST Repository

Ahmad, Talha Jamal; Ali, Hussnain; Majeed, S. M Imran; Khan, Shoab A.

2011-01-01

This paper discusses the effect of atrioventricular conduction time (AVCT) on the short-term Heart Rate Variability (HRV) by computing HRV parameters using intervals between the onsets of successive P waves (PP time series) for three groups: normal

gHRV: Heart rate variability analysis made easy.

Science.gov (United States)

Rodríguez-Liñares, L; Lado, M J; Vila, X A; Méndez, A J; Cuesta, P

2014-08-01

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Event rate and reaction time performance in ADHD: Testing predictions from the state regulation deficit hypothesis using an ex-Gaussian model.

Science.gov (United States)

Metin, Baris; Wiersema, Jan R; Verguts, Tom; Gasthuys, Roos; van Der Meere, Jacob J; Roeyers, Herbert; Sonuga-Barke, Edmund

2014-12-06

According to the state regulation deficit (SRD) account, ADHD is associated with a problem using effort to maintain an optimal activation state under demanding task settings such as very fast or very slow event rates. This leads to a prediction of disrupted performance at event rate extremes reflected in higher Gaussian response variability that is a putative marker of activation during motor preparation. In the current study, we tested this hypothesis using ex-Gaussian modeling, which distinguishes Gaussian from non-Gaussian variability. Twenty-five children with ADHD and 29 typically developing controls performed a simple Go/No-Go task under four different event-rate conditions. There was an accentuated quadratic relationship between event rate and Gaussian variability in the ADHD group compared to the controls. The children with ADHD had greater Gaussian variability at very fast and very slow event rates but not at moderate event rates. The results provide evidence for the SRD account of ADHD. However, given that this effect did not explain all group differences (some of which were independent of event rate) other cognitive and/or motivational processes are also likely implicated in ADHD performance deficits.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

Science.gov (United States)

Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf

2018-01-01

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Characterization and prediction of rate-dependent flexibility in lumbar spine biomechanics at room and body temperature.

Science.gov (United States)

Stolworthy, Dean K; Zirbel, Shannon A; Howell, Larry L; Samuels, Marina; Bowden, Anton E

2014-05-01

The soft tissues of the spine exhibit sensitivity to strain-rate and temperature, yet current knowledge of spine biomechanics is derived from cadaveric testing conducted at room temperature at very slow, quasi-static rates. The primary objective of this study was to characterize the change in segmental flexibility of cadaveric lumbar spine segments with respect to multiple loading rates within the range of physiologic motion by using specimens at body or room temperature. The secondary objective was to develop a predictive model of spine flexibility across the voluntary range of loading rates. This in vitro study examines rate- and temperature-dependent viscoelasticity of the human lumbar cadaveric spine. Repeated flexibility tests were performed on 21 lumbar function spinal units (FSUs) in flexion-extension with the use of 11 distinct voluntary loading rates at body or room temperature. Furthermore, six lumbar FSUs were loaded in axial rotation, flexion-extension, and lateral bending at both body and room temperature via a stepwise, quasi-static loading protocol. All FSUs were also loaded using a control loading test with a continuous-speed loading-rate of 1-deg/sec. The viscoelastic torque-rotation response for each spinal segment was recorded. A predictive model was developed to accurately estimate spine segment flexibility at any voluntary loading rate based on measured flexibility at a single loading rate. Stepwise loading exhibited the greatest segmental range of motion (ROM) in all loading directions. As loading rate increased, segmental ROM decreased, whereas segmental stiffness and hysteresis both increased; however, the neutral zone remained constant. Continuous-speed tests showed that segmental stiffness and hysteresis are dependent variables to ROM at voluntary loading rates in flexion-extension. To predict the torque-rotation response at different loading rates, the model requires knowledge of the segmental flexibility at a single rate and specified

Sensory experience ratings (SERs) for 1,659 French words: Relationships with other psycholinguistic variables and visual word recognition.

Science.gov (United States)

Bonin, Patrick; Méot, Alain; Ferrand, Ludovic; Bugaïska, Aurélia

2015-09-01

We collected sensory experience ratings (SERs) for 1,659 French words in adults. Sensory experience for words is a recently introduced variable that corresponds to the degree to which words elicit sensory and perceptual experiences (Juhasz & Yap Behavior Research Methods, 45, 160-168, 2013; Juhasz, Yap, Dicke, Taylor, & Gullick Quarterly Journal of Experimental Psychology, 64, 1683-1691, 2011). The relationships of the sensory experience norms with other psycholinguistic variables (e.g., imageability and age of acquisition) were analyzed. We also investigated the degree to which SER predicted performance in visual word recognition tasks (lexical decision, word naming, and progressive demasking). The analyses indicated that SER reliably predicted response times in lexical decision, but not in word naming or progressive demasking. The findings are discussed in relation to the status of SER, the role of semantic code activation in visual word recognition, and the embodied view of cognition.

Does body fat percentage predict post-exercise heart rate response in non-obese children and adolescents?

Science.gov (United States)

Jezdimirovic, Tatjana; Stajer, Valdemar; Semeredi, Sasa; Calleja-Gonzalez, Julio; Ostojic, Sergej M

2017-05-24

A correlation between adiposity and post-exercise autonomic regulation has been established in overweight and obese children. However, little information exists about this link in non-obese youth. The main purpose of this cross-sectional study was to describe the relationship between body fat percentage (BFP) and heart rate recovery after exercise [post-exercise heart rate (PEHR)], a marker of autonomic regulation, in normal-weight children and adolescents. We evaluated the body composition of 183 children and adolescents (age 15.0±2.3 years; 132 boys and 51 girls) who performed a maximal graded exercise test on a treadmill, with the heart rate monitored during and immediately after exercise. A strong positive trend was observed in the association between BFP and PEHR (r=0.14; p=0.06). Hierarchical multiple regression revealed that our model explained 18.3% of the variance in PEHR (p=0.00), yet BFP accounted for only 0.9% of the variability in PEHR (p=0.16). The evaluation of the contribution of each independent variable revealed that only two variables made a unique statistically significant contribution to our model (pfatness seems to poorly predict PEHR in our sample of non-obese children and adolescents, while non-modifiable variables (age and gender) were demonstrated as strong predictors of heart rate recovery. The low amount of body fat reported in non-obese young participants was perhaps too small to cause disturbances in autonomic nervous system regulation.

iHeartLift: a closed loop system with bio-feedback that uses music tempo variability to improve heart rate variability.

Science.gov (United States)

Ho, Thomas C T; Chen, Xiang

2011-01-01

"Musica delenit bestiam feram" translates into "Music soothes the savage beast". There is a hidden truth in this ancient quip passed down from generations. Besides soothing the heart, it also incites the heart to a healthier level of heart rate variability (HRV). In this paper, an approach to use and test music and biofeedback to increase the heart rate variability for people facing daily stress is discussed. By determining the music tempo variability (MTV) of a piece of music and current heart rate variability, iHeartLift is able to compare the 2 trends and locate a musical piece that is suited to increase the user's heart rate variability to a healthier level. With biofeedback, the 2 trends are continuously compared in real-time and the musical piece is changed in accordance with the current comparisons. A study was conducted and it was generally found that HRV can be uplifted by music regardless of language and meaning of musical lyrics but with limitations to musical genre.

Relationship of suicide rates with climate and economic variables in Europe during 2000-2012

DEFF Research Database (Denmark)

Fountoulakis, Konstantinos N; Chatzikosta, Isaia; Pastiadis, Konstantinos

2016-01-01

BACKGROUND: It is well known that suicidal rates vary considerably among European countries and the reasons for this are unknown, although several theories have been proposed. The effect of economic variables has been extensively studied but not that of climate. METHODS: Data from 29 European...... countries covering the years 2000-2012 and concerning male and female standardized suicidal rates (according to WHO), economic variables (according World Bank) and climate variables were gathered. The statistical analysis included cluster and principal component analysis and categorical regression. RESULTS......: The derived models explained 62.4 % of the variability of male suicidal rates. Economic variables alone explained 26.9 % and climate variables 37.6 %. For females, the respective figures were 41.7, 11.5 and 28.1 %. Male suicides correlated with high unemployment rate in the frame of high growth rate and high...

Continuous measurement of heart rate variability following carbon ...

African Journals Online (AJOL)

Background: Previous studies of autonomic nervous system activity through analysis of heart rate variability (HRV) have demonstrated increased sympathetic activity during positive-pressure pneumoperitoneum. We employed an online, continuous method for rapid HRV analysis (MemCalc™, Tarawa, Suwa Trust, Tokyo, ...

Cardiovascular Reactivity and Heart Rate Variability in Panic Disorder

National Research Council Canada - National Science Library

Santiago, Helen T

1999-01-01

.... Because previous studies of cardiovascular reactivity and heart rate variability have been inconclusive, these factors were re-examined in panickers and controls during physiological challenge...

Derivation and application of hydraulic equation for variable-rate ...

African Journals Online (AJOL)

The variable-rate contour-controlled sprinkler (VRCS) for precision irrigation can throw water on a given shaped area and the flow rate is also varied with the throw distance of the sprinkler for the purpose of high uniformity irrigation. Much of past research work were concentrated on the mechanical availability of ...

[The influence of physical exercise on heart rate variability].

Science.gov (United States)

Gajek, Jacek; Zyśko, Dorota; Negrusz-Kawecka, Marta; Halawa, Bogumił

2003-03-01

Heart rate variability is controlled by the influence of autonomic nervous system, whereas one part of the system modulates the activity of the other. There is evidence of increased sympathetic activity in patients (pts) with essential hypertension. The aim of the study was to assess the persisting influence of increased sympathetic activity 30 min after moderate physical exercise on heart rate variability in patients with arterial hypertension. The study was performed in 19 patients (10 women, mean age 52.7 +/- 9.5 years and 9 men, mean age 37.7 +/- 8.8 years) with stage I (6 pts) and stage II (13 pts) arterial hypertension. All studied pts had sinus rhythm, were free of diabetes, coronary heart disease and congestive heart failure. 24-hour Holter monitoring was performed and for 30 min before the exercise test the pts stayed in supine rest. The exercise tests were performed between 10 and 11 a.m. Immediately after the exercise all pts stayed in supine position for 30 min. The heart rate variability parameters were studied using Holter monitoring system Medilog Optima Jet and were then analysed statistically. The mean energy expenditure during the exercise was 5.8 +/- 1.1 METs and the maximal heart rate was 148.1 +/- 20.3 bpm. All studied HRV parameters were significantly different in the assessed time period compared to the baseline values (p < 0.001). Significant correlation was found between the age of the studied patients and the mean RR interval, what can be considered as a hyperkinetic (hyperadrenergic) circulatory status and shorter RR interval in younger pts. Significant negative correlation between the age and SDNN parameter (r = -0.65, p < 0.001), 30 min after the exercise mirrors the prolonged adrenergic influence in older pts. The present study shows that the influence of moderate physical exercise on heart rate variability in pts with essential hypertension is extended over 30 min period after exercise and is more pronounced in older pts. The studies

Gender differences of heart rate variability in healthy volunteers

International Nuclear Information System (INIS)

Saleem, S.; Majeed, S.M.I.; Khan, M.A.

2012-01-01

Objective: To identify the basic values of heart rate variability in Pakistani population and to verify our hypothesis that there are gender differences in cardiovascular autonomic modulation. Methods: The descriptive cross sectional study based on convenience probability sampling was conducted at Armed Forces Institute of Cardiology/National Institute of Heart Diseases (AFIC/NIHD) Pakistan. The duration of the study was from December 2009 to July 2010. It involved 24-hour holter monitoring of 45 healthy individuals using holter electrocardiography (ECG) recorder. Heart rate variability was analysed in time (SDNN, SDANN, SDNNi, rMSSD, pNN50) and frequency domains (power, VLF, LF, and HF). Results: The time domain indices; SDNN (male=140 +- 36 ms vs. females=122 +- 33 ms; p =0.09), SDANN (male=123 +- 34 ms vs. females=111+- 34 ms; P= 0.23), SDNNi (male=64 +-19 ms vs. females=52 +- 14 ms; P= 0.03), and pNN50 (male=14 +- 10 ms vs. females=12 +- 7 ms; P= 0.43) were decreased in female volunteers when compared with males. Comparison of frequency domain indices; Total power (male=4041 +- 3150 ms/sup 2/ vs. females=2750 +- 1439 ms/sup 2/; P= 0.07), VLF (male=291 2675 ms/sup 2/ vs. females=1843 +- 928 ms/sup 2/; P= 0.06), LF (male=788 +- 397 ms/sup 2/ vs. females=556 +- 346 ms/sup 2/; P= 0.04) and HF (male=318 +- 251 ms/sup 2/ vs. females=31 277 ms/sup 2/; P= 0.94) amongst males and females showed attenuated heart rate variability in females. Of all the observed values, SDNNi and LF were found significantly (p <0.05) decreased in women. Conclusion: In healthy population, heart rate variability is low in women than men. It reflects sympathetic dominance in women in our population. (author)

Initial sociometric impressions of attention-deficit hyperactivity disorder and comparison boys: predictions from social behaviors and from nonbehavioral variables.

Science.gov (United States)

Erhardt, Drew; Hinshaw, Stephen P

1994-08-01

This study systematically compared the influence of naturalistic social behaviors and nonbehavioral variables on the development of peer status in 49 previously unfamiliar boys, aged 6-12 years, who attended a summer research program. Twenty-five boys with attention-deficit hyperactivity disorder (ADHD) and 24 comparison boys participated. Physical attractiveness, motor competence, intelligence, and academic achievement constituted the nonbehavioral variables; social behaviors included noncompliance, aggression, prosocial actions, and isolation, measured by live observations of classroom and playground interactions. As early as the first day of interaction, ADHD and comparison boys displayed clear differences in social behaviors, and the ADHD youngsters were overwhelmingly rejected. Whereas prosocial behavior independently predicted friendship ratings during the first week, the magnitude of prediction was small. In contrast, the boys' aggression (or noncompliance) strongly predicted negative nominations, even with nonbehavioral factors, group status (ADHD versus comparison), and other social behaviors controlled statistically. Implications for understanding and remediating negative peer reputations are discussed.

Effect of atrioventricular conduction on heart rate variability

KAUST Repository

Ahmad, Talha Jamal

2011-08-01

This paper discusses the effect of atrioventricular conduction time (AVCT) on the short-term Heart Rate Variability (HRV) by computing HRV parameters using intervals between the onsets of successive P waves (PP time series) for three groups: normal, arrhythmia and sudden cardiac death (SCD) patients. A very precise wavelet transform based ECG delineator was developed to detect PP, PR and RR time series. Mean PR variation in arrhythmia and SCD group was found to be significantly high as compared to the normal group. It was observed that when PR variations in arrhythmia and SCD cases crossed a certain threshold, RR variability no longer provided a very accurate estimate of HRV. In such cases, PP variability was able to provide a better assessment of HRV. © 2011 IEEE.

Continuous measurement of heart rate variability following carbon ...

African Journals Online (AJOL)

2010-07-16

Jul 16, 2010 ... Power spectral analysis of the electrocardiographic R-R interval [heart rate variability: (HRV)] is a well known, non- invasive method for assessing autonomic nervous activity.1. Studies using HRV analysis during positive-pressure pneumoperitoneum (PPP) have demonstrated increased sympathetic ...

Predictive value of clinical and laboratory variables for vesicoureteral reflux in children.

Science.gov (United States)

Soylu, Alper; Kasap, Belde; Demir, Korcan; Türkmen, Mehmet; Kavukçu, Salih

2007-06-01

We aimed to determine the predictability of clinical and laboratory variables for vesicoureteral reflux (VUR) in children with urinary tract infection (UTI). Data of children with febrile UTI who underwent voiding cystoureterography between 2002 and 2005 were evaluated retrospectively for clinical (age, gender, fever > or = 38.5 degrees C, recurrent UTI), laboratory [leukocytosis, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), pyuria, serum creatinine (S(Cr))] and imaging (renal ultrasonography) variables. Children with VUR (group 1) vs. no VUR (group 2) and children with high-grade (III-V) VUR (group 3) vs. no or low-grade (I-II) VUR (group 4) were compared. Among 88 patients (24 male), 38 had VUR and 21 high-grade VUR. Fever > or = 38.5 degrees C was associated with VUR [odds ratio (OR): 7.5]. CRP level of 50 mg/l was the best cut-off level for predicting high-grade VUR (OR 15.5; discriminative ability 0.89 +/- 0.05). Performing voiding cystourethrography based on this CRP level would result in failure to notice 9% of patients with high-grade VUR, whereas 69% of children with no/low-grade VUR would be spared from this invasive test. In conclusion, fever > or = 38 degrees C and CRP > 50 mg/l seem to be potentially useful clinical predictors of VUR and high-grade VUR, respectively, in pediatric patients with UTI. Further validation of these findings could limit unnecessary voiding cystourethrography.

A validated disease specific prediction equation for resting metabolic rate in underweight patients with COPD

Directory of Open Access Journals (Sweden)

Anita Nordenson

2010-09-01

Full Text Available Anita Nordenson2, Anne Marie Grönberg1,2, Lena Hulthén1, Sven Larsson2, Frode Slinde11Department of Clinical Nutrition, Sahlgrenska Academy at University of Gothenburg, Göteborg, Sweden; 2Department of Internal Medicine/Respiratory Medicine and Allergology, Sahlgrenska Academy at University of Gothenburg, SwedenAbstract: Malnutrition is a serious condition in chronic obstructive pulmonary disease (COPD. Successful dietary intervention calls for calculations of resting metabolic rate (RMR. One disease-specific prediction equation for RMR exists based on mainly male patients. To construct a disease-specific equation for RMR based on measurements in underweight or weight-losing women and men with COPD, RMR was measured by indirect calorimetry in 30 women and 11 men with a diagnosis of COPD and body mass index <21 kg/m2. The following variables, possibly influencing RMR were measured: length, weight, middle upper arm circumference, triceps skinfold, body composition by dual energy x-ray absorptiometry and bioelectrical impedance, lung function, and markers of inflammation. Relations between RMR and measured variables were studied using univariate analysis according to Pearson. Gender and variables that were associated with RMR with a P value <0.15 were included in a forward multiple regression analysis. The best-fit multiple regression equation included only fat-free mass (FFM: RMR (kJ/day = 1856 + 76.0 FFM (kg. To conclude, FFM is the dominating factor influencing RMR. The developed equation can be used for prediction of RMR in underweight COPD patients.Keywords: pulmonary disease, chronic obstructive, basal metabolic rate, malnutrition, body composition

Relative Contributions of Socio-Cultural Variables to the Prediction ...

African Journals Online (AJOL)

Erah

the Prediction of Maternal Mortality in Edo South. Senatorial ... variables across the two locations (rural and urban) was early marriage/early child bearing (R2 = 0.200;. F = 401.40 ... severe bleeding, infections, obstructed or prolonged .... Analytical System (SAS) mode. Descriptive .... incontinence of urine and faeces due to.

Heart rate variability in patients with systemic lupus erythematosus: a systematic review and methodological considerations.

Science.gov (United States)

Matusik, P S; Matusik, P T; Stein, P K

2018-07-01

Aim The aim of this review was to summarize current knowledge about the scientific findings and potential clinical utility of heart rate variability measures in patients with systemic lupus erythematosus. Methods PubMed, Embase and Scopus databases were searched for the terms associated with systemic lupus erythematosus and heart rate variability, including controlled vocabulary, when appropriate. Articles published in English and available in full text were considered. Finally, 11 publications were selected, according to the systematic review protocol and were analyzed. Results In general, heart rate variability, measured in the time and frequency domains, was reported to be decreased in patients with systemic lupus erythematosus compared with controls. In some systemic lupus erythematosus studies, heart rate variability was found to correlate with inflammatory markers and albumin levels. A novel heart rate variability measure, heart rate turbulence onset, was shown to be increased, while heart rate turbulence slope was decreased in systemic lupus erythematosus patients. Reports of associations of changes in heart rate variability parameters with increasing systemic lupus erythematosus activity were inconsistent, showing decreasing heart rate variability or no relationship. However, the low/high frequency ratio was, in some studies, reported to increase with increasing disease activity or to be inversely correlated with albumin levels. Conclusions Patients with systemic lupus erythematosus have abnormal heart rate variability, which reflects cardiac autonomic dysfunction and may be related to inflammatory cytokines but not necessarily to disease activity. Thus measurement of heart rate variability could be a useful clinical tool for monitoring autonomic dysfunction in systemic lupus erythematosus, and may potentially provide prognostic information.

Predicting farm-level animal populations using environmental and socioeconomic variables.

Science.gov (United States)

van Andel, Mary; Jewell, Christopher; McKenzie, Joanna; Hollings, Tracey; Robinson, Andrew; Burgman, Mark; Bingham, Paul; Carpenter, Tim

2017-09-15

Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species. Copyright © 2017 Elsevier B.V. All rights reserved.

Infant breathing rate counter based on variable resistor for pneumonia

Science.gov (United States)

Sakti, Novi Angga; Hardiyanto, Ardy Dwi; La Febry Andira R., C.; Camelya, Kesa; Widiyanti, Prihartini

2016-03-01

Pneumonia is one of the leading causes of death in new born baby in Indonesia. According to WHO in 2002, breathing rate is very important index to be the symptom of pneumonia. In the Community Health Center, the nurses count with a stopwatch for exactly one minute. Miscalculation in Community Health Center occurs because of long time concentration and focus on two object at once. This calculation errors can cause the baby who should be admitted to the hospital only be attended at home. Therefore, an accurate breathing rate counter at Community Health Center level is necessary. In this work, resistance change of variable resistor is made to be breathing rate counter. Resistance change in voltage divider can produce voltage change. If the variable resistance moves periodically, the voltage will change periodically too. The voltage change counted by software in the microcontroller. For the every mm shift at the variable resistor produce average 0.96 voltage change. The software can count the number of wave generated by shifting resistor.

HEART RATE VARIABILITY AND BODY COMPOSITION AS VO2MAX DETERMINANTS

Directory of Open Access Journals (Sweden)

Henry Humberto León-Ariza

Full Text Available ABSTRACT Introduction: The maximum oxygen consumption (VO2max is the gold standard in the cardiorespiratory endurance assessment. Objective: This study aimed to develop a mathematical model that contains variables to determine the VO2max of sedentary people. Methods: Twenty participants (10 men and 10 women with a mean age of 19.8±1.77 years were included. For each participant, body composition (percentage of fat and muscle, heart rate variability (HRV at rest (supine and standing, and VO2max were evaluated through an indirect test on a cycloergometer. A multivariate linear regression model was developed from the data obtained, and the model assumptions were verified. Results: Using the data obtained, including percentage of fat (F, percentage of muscle (M, percentage of power at very low frequency (VLF, α-value of the detrended fluctuation analysis (DFAα1, heart rate (HR in the resting standing position, and age of the participants, a model was established for men, which was expressed as VO2max = 4.216 + (Age*0.153 + (F*0.110 - (M*0.053 - (VLF*0.649 - (DFAα1*2.441 - (HR*0.014, with R2 = 0.965 and standard error = 0.146 L/min. For women, the model was expressed as VO2max = 1.947 - (Age*0.047 + (F*0.024 + (M*0.054 + (VLF*1.949 - (DFAα1*0.424 - (HR*0.019, with R2 = 0.987 and standard error = 0.077 L/min. Conclusion: The obtained model demonstrated the influence exerted by body composition, the autonomic nervous system, and age in the prediction of VO2max.

Modifiable variables in physical therapy education programs associated with first-time and three-year National Physical Therapy Examination pass rates in the United States

Directory of Open Access Journals (Sweden)

Chad Cook

2015-09-01

Full Text Available Purpose: This study aimed to examine the modifiable programmatic characteristics reflected in the Commission on Accreditation in Physical Therapy Education (CAPTE Annual Accreditation Report for all accredited programs that reported pass rates on the National Physical Therapist Examination, and to build a predictive model for first-time and three-year ultimate pass rates. Methods: This observational study analyzed programmatic information from the 185 CAPTE-accredited physical therapy programs in the United States and Puerto Rico out of a total of 193 programs that provided the first-time and three-year ultimate pass rates in 2011. Fourteen predictive variables representing student selection and composition, clinical education length and design, and general program length and design were analyzed against first-time pass rates and ultimate pass rates on the NPTE. Univariate and multivariate multinomial regression analysis for first-time pass rates and logistic regression analysis for three-year ultimate pass rates were performed. Results: The variables associated with the first-time pass rate in the multivariate analysis were the mean undergraduate grade point average (GPA and the average age of the cohort. Multivariate analysis showed that mean undergraduate GPA was associated with the three-year ultimate pass rate. Conclusions: Mean undergraduate GPA was found to be the only modifiable predictor for both first-time and three-year pass rates among CAPTE-accredited physical therapy programs.

Do high fetal catecholamine levels affect heart rate variability and ...

African Journals Online (AJOL)

Objectives. To deternrine the relationship between Umbilical arterial catecholamine levels and fetal heart rate variability and meconium passage. Study design. A prospective descriptive study was perfonned. Umbilical artery catecholamine levels were measured in 55 newborns and correlated with fetal heart rate before ...

THE APPLICATION OF RISK BASED BANK RATING ON BANKRUPTCY PREDICTION OF BANKS IN INDONESIA

Directory of Open Access Journals (Sweden)

Evi Sistiyarini

2017-04-01

Full Text Available The increase of banking products and services which is more complex will increase the risk to the banks. Therefore, to anticipate the rise of financial difficulties in a bank, the early warning system. This study aimed to find the influence RBBR (Risk Based Bank Rating ratio’s to predict bankruptcy of conventional Banks in Indonesia. Ratio of RBBR consisted of risk profile, Good Corporate Governance, profitability and capital. Independent variables used were NPL, PDN, LDR, GCG, ROA and NIM, and CAR. Dependent variable was bank bankruptcy using dummy variable. The population of this study was all of the conventional banks in Indonesia. The data was a secondary data taken form financial report of conventional bank 2011-2015. Technical sampling used was a purposive sampling method with some criteria. The analysis of this study used logistic regression.The result of the study showed that NPL, PDN, LDR, GCG, ROA and NIM, and CARhad no significant influence to bankruptcy of the bank.

Variable selection for mixture and promotion time cure rate models.

Science.gov (United States)

Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

2016-11-16

Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

Endogenous Pain Modulation: Association with Resting Heart Rate Variability and Negative Affectivity.

Science.gov (United States)

Van Den Houte, Maaike; Van Oudenhove, Lukas; Bogaerts, Katleen; Van Diest, Ilse; Van den Bergh, Omer

2017-07-21

Several chronic pain syndromes are characterized by deficient endogenous pain modulation as well as elevated negative affectivity and reduced resting heart rate variability. In order to elucidate the relationships between these characteristics, we investigated whether negative affectivity and heart rate variability are associated with endogenous pain modulation in a healthy population. An offset analgesia paradigm with noxious thermal stimulation calibrated to the individual's pain threshold was used to measure endogenous pain modulation magnitude in 63 healthy individuals. Pain ratings during constant noxious heat stimulation to the arm (15 seconds) were compared with ratings during noxious stimulation comprising a 1 °C rise and return of temperature to the initial level (offset trials, 15 seconds). Offset analgesia was defined as the reduction in pain following the 1 °C decrease relative to pain at the same time point during continuous heat stimulation. Evidence for an offset analgesia effect could only be found when noxious stimulation intensity (and, hence, the individual's pain threshold) was intermediate (46 °C or 47 °C). Offset analgesia magnitude was also moderated by resting heart rate variability: a small but significant offset effect was found in participants with high but not low heart rate variability. Negative affectivity was not related to offset analgesia magnitude. These results indicate that resting heart rate variability (HRV) is related to endogenous pain modulation (EPM) in a healthy population. Future research should focus on clarifying the causal relationship between HRV and EPM and chronic pain by using longitudinal study designs. © 2017 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Marine heatwaves off eastern Tasmania: Trends, interannual variability, and predictability

Science.gov (United States)

Oliver, Eric C. J.; Lago, Véronique; Hobday, Alistair J.; Holbrook, Neil J.; Ling, Scott D.; Mundy, Craig N.

2018-02-01

Surface waters off eastern Tasmania are a global warming hotspot. Here, mean temperatures have been rising over several decades at nearly four times the global average rate, with concomitant changes in extreme temperatures - marine heatwaves. These changes have recently caused the marine biodiversity, fisheries and aquaculture industries off Tasmania's east coast to come under stress. In this study we quantify the long-term trends, variability and predictability of marine heatwaves off eastern Tasmania. We use a high-resolution ocean model for Tasmania's eastern continental shelf. The ocean state over the 1993-2015 period is hindcast, providing daily estimates of the three-dimensional temperature and circulation fields. Marine heatwaves are identified at the surface and subsurface from ocean temperature time series using a consistent definition. Trends in marine heatwave frequency are positive nearly everywhere and annual marine heatwave days and penetration depths indicate significant positive changes, particularly off southeastern Tasmania. A decomposition into modes of variability indicates that the East Australian Current is the dominant driver of marine heatwaves across the domain. Self-organising maps are used to identify 12 marine heatwave types, each with its own regionality, seasonality, and associated large-scale oceanic and atmospheric circulation patterns. The implications of this work for marine ecosystems and their management were revealed through review of past impacts and stakeholder discussions regarding use of these data.

Nationwide Macroeconomic Variables and the Growth Rate of Bariatric Surgeries in Brazil.

Science.gov (United States)

Cazzo, Everton; Ramos, Almino Cardoso; Pareja, José Carlos; Chaim, Elinton Adami

2018-06-06

The effect of nationwide economic issues on the necessary expansion in the number of bariatric procedures remains unclear. This study aims to determine whether there are correlations between the growth rate in the number of bariatric surgeries and the major macroeconomic variables over time in Brazil. It is a nationwide analysis regarding the number of bariatric surgeries in Brazil and the main national macroeconomic variables from 2003 through 2016: gross domestic product (GDP), inflation rate, and the unemployment rate, as well as the evolution in the number of registered bariatric surgeons. There were significant positive correlations of the growth rate of surgeries with the early variations of the GDP (R = 0.5558; p = 0.04863) and of the overall health expenditure per capita (R = 0.78322; p = 0.00259). The growth rate of the number of bariatric surgeries was not correlated with the unemployment and inflation rates, as well as with the growth rate of available bariatric surgeons. There were direct relationships between the growth rate of bariatric surgeries and the evolutions of the GDP and health care expenditure per capita. These variables appear to influence the nationwide offer of bariatric surgery.

Heart rate variability based on risk stratification for type 2 diabetes mellitus.

Science.gov (United States)

Silva-E-Oliveira, Julia; Amélio, Pâmela Marina; Abranches, Isabela Lopes Laguardia; Damasceno, Dênis Derly; Furtado, Fabianne

2017-01-01

To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus. The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rate variability, a heart-rate monitor Polar S810i® was employed to obtain RR series for each individual, at rest, for 5 minutes, followed by analysis of linear and nonlinear indexes. The groups at higher risk of type 2 diabetes mellitus had significantly lower linear and nonlinear heart rate variability indexes. The individuals at high risk for type 2 diabetes mellitus have lower heart rate variability. Avaliar a variabilidade da frequência cardíaca em adultos com diferentes níveis de risco para diabetes mellitus tipo 2. O grau de risco para diabetes mellitus tipo 2 de 130 participantes (41 homens) foi avaliado pelo questionário Finnish Diabetes Risk Score. Os participantes foram classificados em baixo risco (n=26), risco levemente elevado (n=41), risco moderado (n=27) e alto risco (n=32). Para medir a variabilidade da frequência cardíaca, utilizou-se o frequencímetro Polar S810i® para obter séries de intervalo RR para cada indivíduo, em repouso, durante 5 minutos; posteriormente, realizou-se análise por meio de índices lineares e não-lineares. O grupo com maior risco para diabetes mellitus tipo 2 teve uma diminuição significante nos índices lineares e não-lineares da variabilidade da frequência cardíaca. Os resultados apontam que indivíduos com risco alto para diabetes mellitus tipo 2 tem menor variabilidade da frequência cardíaca. To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus. The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score

Correlating multidimensional fetal heart rate variability analysis with acid-base balance at birth

International Nuclear Information System (INIS)

Frasch, Martin G; Durosier, Lucien D; Xu, Yawen; Wang, Xiaogang; Gao, Xin; Stampalija, Tamara; Herry, Christophe; Seely, Andrew JE; Casati, Daniela; Ferrazzi, Enrico; Alfirevic, Zarko

2014-01-01

Fetal monitoring during labour currently fails to accurately detect acidemia. We developed a method to assess the multidimensional properties of fetal heart rate variability (fHRV) from trans-abdominal fetal electrocardiogram (fECG) during labour. We aimed to assess this novel bioinformatics approach for correlation between fHRV and neonatal pH or base excess (BE) at birth. We enrolled a prospective pilot cohort of uncomplicated singleton pregnancies at 38–42 weeks’ gestation in Milan, Italy, and Liverpool, UK. Fetal monitoring was performed by standard cardiotocography. Simultaneously, with fECG (high sampling frequency) was recorded. To ensure clinician blinding, fECG information was not displayed. Data from the last 60 min preceding onset of second-stage labour were analyzed using clinically validated continuous individualized multiorgan variability analysis (CIMVA) software in 5 min overlapping windows. CIMVA allows simultaneous calculation of 101 fHRV measures across five fHRV signal analysis domains. We validated our mathematical prediction model internally with 80:20 cross-validation split, comparing results to cord pH and BE at birth. The cohort consisted of 60 women with neonatal pH values at birth ranging from 7.44 to 6.99 and BE from −0.3 to −18.7 mmol L −1 . Our model predicted pH from 30 fHRV measures (R 2 = 0.90, P < 0.001) and BE from 21 fHRV measures (R 2 = 0.77, P < 0.001). Novel bioinformatics approach (CIMVA) applied to fHRV derived from trans-abdominal fECG during labor correlated well with acid-base balance at birth. Further refinement and validation in larger cohorts are needed. These new measurements of fHRV might offer a new opportunity to predict fetal acid-base balance at birth. (fast track communication)

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

Directory of Open Access Journals (Sweden)

Alejandro Baldominos Gómez

2016-03-01

Full Text Available This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.

A model for predicting wear rates in tooth enamel.

Science.gov (United States)

Borrero-Lopez, Oscar; Pajares, Antonia; Constantino, Paul J; Lawn, Brian R

2014-09-01

It is hypothesized that wear of enamel is sensitive to the presence of sharp particulates in oral fluids and masticated foods. To this end, a generic model for predicting wear rates in brittle materials is developed, with specific application to tooth enamel. Wear is assumed to result from an accumulation of elastic-plastic micro-asperity events. Integration over all such events leads to a wear rate relation analogous to Archard׳s law, but with allowance for variation in asperity angle and compliance. The coefficient K in this relation quantifies the wear severity, with an arbitrary distinction between 'mild' wear (low K) and 'severe' wear (high K). Data from the literature and in-house wear-test experiments on enamel specimens in lubricant media (water, oil) with and without sharp third-body particulates (silica, diamond) are used to validate the model. Measured wear rates can vary over several orders of magnitude, depending on contact asperity conditions, accounting for the occurrence of severe enamel removal in some human patients (bruxing). Expressions for the depth removal rate and number of cycles to wear down occlusal enamel in the low-crowned tooth forms of some mammals are derived, with tooth size and enamel thickness as key variables. The role of 'hard' versus 'soft' food diets in determining evolutionary paths in different hominin species is briefly considered. A feature of the model is that it does not require recourse to specific material removal mechanisms, although processes involving microplastic extrusion and microcrack coalescence are indicated. Published by Elsevier Ltd.

Separating the effect of respiration from the heart rate variability for cases of constant harmonic breathing

Directory of Open Access Journals (Sweden)

Kircher Michael

2015-09-01

Full Text Available Heart Rate Variability studies are a known measure for the autonomous control of the heart rate. In special situations, its interpretation can be ambiguous, since the respiration has a major influence on the heart rate variability. For this reason it has often been proposed to measure Heart Rate Variability, while the subjects are breathing at a constant respiration rate. That way the spectral influence of the respiration is known. In this work we propose to remove this constant respiratory influence from the heart rate and the Heart Rate Variability parameters to gain respiration free autonomous controlled heart rate signal. The spectral respiratory component in the heart rate signal is detected and characterized. Subsequently the respiratory effect on Heart Rate Variability is removed using spectral filtering approaches, such as the Notch filter or the Raised Cosine filter. As a result new decoupled Heart Variability parameters are gained, which could lead to new additional interpretations of the autonomous control of the heart rate.

US Climate Variability and Predictability Project

Energy Technology Data Exchange (ETDEWEB)

Patterson, Mike [University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)

2017-11-14

The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year support of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.

Variables that predict academic procrastination behavior in prospective primary school teachers

Directory of Open Access Journals (Sweden)

Asuman Seda SARACALOĞLU

2016-04-01

Full Text Available This study aimed to examine the variables predicting academic procrastination behavior of prospective primary school teachers and is conducted using the correlational survey model. The study group is composed of 294 undergraduate students studying primary school teaching programs in faculties of education at Adnan Menderes, Pamukkale, and Muğla Sıtkı Koçman Universities in Turkey. The data collection instruments used were the Procrastination Assessment Scale Students (PASS, Academic Self-Efficacy Scale (ASES, and Academic Motivation Scale (AMS. While analyzing the gathered data, descriptive analysis techniques were utilized. Moreover, while analyzing the data, power of variables namely reasons of academic procrastination, academic motivation, and academic efficacy to predict prospective primary school teachers’ academic procrastination tendencies were tested. For that purpose, stepwise regression analysis was employed. It was found that nearly half of the prospective primary school teachers displayed no academic procrastination behavior. Participants’ reasons for procrastination were fear of failure, laziness, taking risks, and rebellion against control. An average level significant correlation was found between participants’ academic procrastination and other variables. As a result, it was identified that prospective primary school teachers had less academic procrastination than reported in literature and laziness, fear of failure, academic motivation predicted academic procrastination.

Ambulatory ECG and analysis of heart rate variability in Parkinson's disease.

Science.gov (United States)

Haapaniemi, T H; Pursiainen, V; Korpelainen, J T; Huikuri, H V; Sotaniemi, K A; Myllylä, V V

2001-03-01

Cardiovascular reflex tests have shown both sympathetic and parasympathetic failure in Parkinson's disease. These tests, however, describe the autonomic responses during a restricted time period and have great individual variability, providing a limited view of the autonomic cardiac control mechanisms. Thus, they do not reflect tonic autonomic regulation. The aim was to examine tonic autonomic cardiovascular regulation in untreated patients with Parkinson's disease. 24 Hour ambulatory ECG was recorded in 54 untreated patients with Parkinson's disease and 47 age matched healthy subjects. In addition to the traditional spectral (very low frequency, VLF; low frequency, LF; high frequency, HF) and non-spectral components of heart rate variability, instantaneous beat to beat variability (SD1) and long term continuous variability (SD2) derived from Poincaré plots, and the slope of the power law relation were analysed. All spectral components (plaw relation (pParkinson's disease than in the control subjects. The Unified Parkinson's disease rating scale total and motor scores had a negative correlation with VLF and LF power spectrum values and the power law relation slopes. Patients with mild hypokinesia had higher HF values than patients with more severe hypokinesia. Tremor and rigidity were not associated with the HR variability parameters. Parkinson's disease causes dysfunction of the diurnal autonomic cardiovascular regulation as demonstrated by the spectral measures of heart rate variability and the slope of the power law relation. This dysfunction seems to be more profound in patients with more severe Parkinson's disease.

Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because...... of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we...... choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

Science.gov (United States)

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

Interobserver variability of sonography for prediction of placenta accreta.

Science.gov (United States)

Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

2014-12-01

The sensitivity of sonography to predict accreta has been reported as higher than 90%. However, most studies are from single expert investigators. Our objective was to analyze interobserver variability of sonography for prediction of placenta accreta. Patients with previa with and without accreta were ascertained, and images with placental views were collected, deidentified, and placed in random sequence. Three radiologists and 3 maternal-fetal medicine specialists interpreted each study for the presence of accreta and specific findings reported to be associated with its diagnosis. Investigator-specific sensitivity, specificity, and accuracy were calculated. κ statistics were used to assess variability between individuals and types of investigators. A total of 229 sonographic studies from 55 patients with accreta and 56 control patients were examined. Accuracy ranged from 55.9% to 76.4%. Of imaging studies yielding diagnoses, sensitivity ranged from 53.4% to 74.4%, and specificity ranged from 70.8% to 94.8%. Overall interobserver agreement was moderate (mean κ ± SD = 0.47 ± 0.12). κ values between pairs of investigators ranged from 0.32 (fair agreement) to 0.73 (substantial agreement). Average individual agreement ranged from fair (κ = 0.35) to moderate (κ = 0.53). Blinded from clinical data, sonography has significant interobserver variability for the diagnosis of placenta accreta. © 2013 by the American Institute of Ultrasound in Medicine.

A brief review and clinical application of heart rate variability biofeedback in sports, exercise, and rehabilitation medicine.

Science.gov (United States)

Prinsloo, Gabriell E; Rauch, H G Laurie; Derman, Wayne E

2014-05-01

An important component of the effective management of chronic noncommunicable disease is the assessment and management of psychosocial stress. The measurement and modulation of heart rate variability (HRV) may be valuable in this regard. To describe the measurement and physiological control of HRV; to describe the impact of psychosocial stress on cardiovascular disease, metabolic syndrome, and chronic respiratory disease, and the relationship between these diseases and changes in HRV; and to describe the influence of biofeedback and exercise on HRV and the use of HRV biofeedback in the management of chronic disease. The PubMed, Medline, and Embase databases were searched (up to August 2013). Additional articles were obtained from the reference lists of relevant articles and reviews. Articles were individually selected for further review based on the quality and focus of the study, and the population studied. Heart rate variability is reduced in stress and in many chronic diseases, and may even predict the development and prognosis of some diseases. Heart rate variability can be increased with both exercise and biofeedback. Although the research on the effect of exercise is conflicting, there is evidence that aerobic training may increase HRV and cardiac vagal tone both in healthy individuals and in patients with disease. Heart rate variability biofeedback is also an effective method of increasing HRV and cardiac vagal tone, and has been shown to decrease stress and reduce the morbidity and mortality of disease. The assessment and management of psychosocial stress is a challenging but important component of effective comprehensive lifestyle interventions for the management of noncommunicable disease. It is, therefore, important for the sports and exercise physician to have an understanding of the therapeutic use of HRV modulation, both in the reduction of stress and in the management of chronic disease.

Predicting online ratings based on the opinion spreading process

Science.gov (United States)

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

2015-10-01

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

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

Science.gov (United States)

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

Prediction of pipeline corrosion rate based on grey Markov models

International Nuclear Information System (INIS)

Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

2009-01-01

Based on the model that combined by grey model and Markov model, the prediction of corrosion rate of nuclear power pipeline was studied. Works were done to improve the grey model, and the optimization unbiased grey model was obtained. This new model was used to predict the tendency of corrosion rate, and the Markov model was used to predict the residual errors. In order to improve the prediction precision, rolling operation method was used in these prediction processes. The results indicate that the improvement to the grey model is effective and the prediction precision of the new model combined by the optimization unbiased grey model and Markov model is better, and the use of rolling operation method may improve the prediction precision further. (authors)

Uncertainty in wave energy resource assessment. Part 2: Variability and predictability

International Nuclear Information System (INIS)

Mackay, Edward B.L.; Bahaj, AbuBakr S.; Challenor, Peter G.

2010-01-01

The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit high levels of interannual variability. Moreover, many previous studies have demonstrated longer-term decadal changes in wave climate. The effect of interannual and climatic changes in wave climate on the predictability of long-term mean WEC power is examined for an area off the north coast of Scotland. In this location anomalies in mean WEC power are strongly correlated with the North Atlantic Oscillation (NAO) index. This link enables the results of many previous studies on the variability of the NAO and its sensitivity to climate change to be applied to WEC power levels. It is shown that the variability in 5, 10 and 20 year mean power levels is greater than if annual power anomalies were uncorrelated noise. It is also shown that the change in wave climate from anthropogenic climate change over the life time of a wave farm is likely to be small in comparison to the natural level of variability. Finally, it is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield. (author)

Joint variable frame rate and length analysis for speech recognition under adverse conditions

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Kraljevski, Ivan

2014-01-01

This paper presents a method that combines variable frame length and rate analysis for speech recognition in noisy environments, together with an investigation of the effect of different frame lengths on speech recognition performance. The method adopts frame selection using an a posteriori signal......-to-noise (SNR) ratio weighted energy distance and increases the length of the selected frames, according to the number of non-selected preceding frames. It assigns a higher frame rate and a normal frame length to a rapidly changing and high SNR region of a speech signal, and a lower frame rate and an increased...... frame length to a steady or low SNR region. The speech recognition results show that the proposed variable frame rate and length method outperforms fixed frame rate and length analysis, as well as standalone variable frame rate analysis in terms of noise-robustness....

Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network

Directory of Open Access Journals (Sweden)

Zhipeng Fang

2014-01-01

Full Text Available Most classical search engines choose and rank advertisements (ads based on their click-through rates (CTRs. To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method.

Dynamic Variables Fail to Predict Fluid Responsiveness in an Animal Model With Pericardial Effusion.

Science.gov (United States)

Broch, Ole; Renner, Jochen; Meybohm, Patrick; Albrecht, Martin; Höcker, Jan; Haneya, Assad; Steinfath, Markus; Bein, Berthold; Gruenewald, Matthias

2016-10-01

The reliability of dynamic and volumetric variables of fluid responsiveness in the presence of pericardial effusion is still elusive. The aim of the present study was to investigate their predictive power in a porcine model with hemodynamic relevant pericardial effusion. A single-center animal investigation. Twelve German domestic pigs. Pigs were studied before and during pericardial effusion. Instrumentation included a pulmonary artery catheter and a transpulmonary thermodilution catheter in the femoral artery. Hemodynamic variables like cardiac output (COPAC) and stroke volume (SVPAC) derived from pulmonary artery catheter, global end-diastolic volume (GEDV), stroke volume variation (SVV), and pulse-pressure variation (PPV) were obtained. At baseline, SVV, PPV, GEDV, COPAC, and SVPAC reliably predicted fluid responsiveness (area under the curve 0.81 [p = 0.02], 0.82 [p = 0.02], 0.74 [p = 0.07], 0.74 [p = 0.07], 0.82 [p = 0.02]). After establishment of pericardial effusion the predictive power of dynamic variables was impaired and only COPAC and SVPAC and GEDV allowed significant prediction of fluid responsiveness (area under the curve 0.77 [p = 0.04], 0.76 [p = 0.05], 0.83 [p = 0.01]) with clinically relevant changes in threshold values. In this porcine model, hemodynamic relevant pericardial effusion abolished the ability of dynamic variables to predict fluid responsiveness. COPAC, SVPAC, and GEDV enabled prediction, but their threshold values were significantly changed. Copyright © 2016 Elsevier Inc. All rights reserved.

Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

OpenAIRE

Cheng, Zhiyong; Ding, Ying; Zhu, Lei; Kankanhalli, Mohan

2018-01-01

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect...

Tracking variable sedimentation rates in orbitally forced paleoclimate proxy series

Science.gov (United States)

Li, M.; Kump, L. R.; Hinnov, L.

2017-12-01

This study addresses two fundamental issues in cyclostratigraphy: quantitative testing of orbital forcing in cyclic sedimentary sequences and tracking variable sedimentation rates. The methodology proposed here addresses these issues as an inverse problem, and estimates the product-moment correlation coefficient between the frequency spectra of orbital solutions and paleoclimate proxy series over a range of "test" sedimentation rates. It is inspired by the ASM method (1). The number of orbital parameters involved in the estimation is also considered. The method relies on the hypothesis that orbital forcing had a significant impact on the paleoclimate proxy variations, and thus is also tested. The null hypothesis of no astronomical forcing is evaluated using the Beta distribution, for which the shape parameters are estimated using a Monte Carlo simulation approach. We introduce a metric to estimate the most likely sedimentation rate using the product-moment correlation coefficient, H0 significance level, and the number of contributing orbital parameters, i.e., the CHO value. The CHO metric is applied with a sliding window to track variable sedimentation rates along the paleoclimate proxy series. Two forward models with uniform and variable sedimentation rates are evaluated to demonstrate the robustness of the method. The CHO method is applied to the classical Late Triassic Newark depth rank series; the estimated sedimentation rates match closely with previously published sedimentation rates and provide a more highly time-resolved estimate (2,3). References: (1) Meyers, S.R., Sageman, B.B., Amer. J. Sci., 307, 773-792, 2007; (2) Kent, D.V., Olsen, P.E., Muttoni, G., Earth-Sci. Rev.166, 153-180, 2017; (3) Li, M., Zhang, Y., Huang, C., Ogg, J., Hinnov, L., Wang, Y., Zou, Z., Li, L., 2017. Earth Plant. Sc. Lett. doi:10.1016/j.epsl.2017.07.015

Performance evaluation of a center pivot variable rate irrigation system

Science.gov (United States)

Variable Rate Irrigation (VRI) for center pivots offers potential to match specific application rates to non-uniform soil conditions along the length of the lateral. The benefit of such systems is influenced by the areal extent of these variations and the smallest scale to which the irrigation syste...

Do Assault-Related Variables Predict Response to Cognitive Behavioral Treatment for PTSD?

Science.gov (United States)

Hembree, Elizabeth A.; Street, Gordon P.; Riggs, David S.; Foa, Edna B.

2004-01-01

This study examined the hypothesis that variables such as history of prior trauma, assault severity, and type of assault, previously found to be associated with natural recovery, would also predict treatment outcome. Trauma-related variables were examined as predictors of posttreatment posttraumatic stress disorder (PTSD) severity in a sample of…

What variables are important in predicting bovine viral diarrhea virus? A random forest approach.

Science.gov (United States)

Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo

2015-07-24

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.

Determination of heart rate variability with an electronic stethoscope.

Science.gov (United States)

Kamran, Haroon; Naggar, Isaac; Oniyuke, Francisca; Palomeque, Mercy; Chokshi, Priya; Salciccioli, Louis; Stewart, Mark; Lazar, Jason M

2013-02-01

Heart rate variability (HRV) is widely used to characterize cardiac autonomic function by measuring beat-to-beat alterations in heart rate. Decreased HRV has been found predictive of worse cardiovascular (CV) outcomes. HRV is determined from time intervals between QRS complexes recorded by electrocardiography (ECG) for several minutes to 24 h. Although cardiac auscultation with a stethoscope is performed routinely on patients, the human ear cannot detect heart sound time intervals. The electronic stethoscope digitally processes heart sounds, from which cardiac time intervals can be obtained. Accordingly, the objective of this study was to determine the feasibility of obtaining HRV from electronically recorded heart sounds. We prospectively studied 50 subjects with and without CV risk factors/disease and simultaneously recorded single lead ECG and heart sounds for 2 min. Time and frequency measures of HRV were calculated from R-R and S1-S1 intervals and were compared using intra-class correlation coefficients (ICC). The majority of the indices were strongly correlated (ICC 0.73-1.0), while the remaining indices were moderately correlated (ICC 0.56-0.63). In conclusion, we found HRV measures determined from S1-S1 are in agreement with those determined by single lead ECG, and we demonstrate and discuss differences in the measures in detail. In addition to characterizing cardiac murmurs and time intervals, the electronic stethoscope holds promise as a convenient low-cost tool to determine HRV in the hospital and outpatient settings as a practical extension of the physical examination.

The Effect of Exchange Rate Variability on U.S. Shareholder Wealth

NARCIS (Netherlands)

Müller, Aline; Verschoor, W.F.C.

2009-01-01

We examine the relationship between financial crisis exchange rate variability and equity return volatility for US multinationals. Empirical analysis of the major financial crises of the last decades reveals that stock return variability increases significantly in the aftermath of a crisis, even

A physical probabilistic model to predict failure rates in buried PVC pipelines

International Nuclear Information System (INIS)

Davis, P.; Burn, S.; Moglia, M.; Gould, S.

2007-01-01

For older water pipeline materials such as cast iron and asbestos cement, future pipe failure rates can be extrapolated from large volumes of existing historical failure data held by water utilities. However, for newer pipeline materials such as polyvinyl chloride (PVC), only limited failure data exists and confident forecasts of future pipe failures cannot be made from historical data alone. To solve this problem, this paper presents a physical probabilistic model, which has been developed to estimate failure rates in buried PVC pipelines as they age. The model assumes that under in-service operating conditions, crack initiation can occur from inherent defects located in the pipe wall. Linear elastic fracture mechanics theory is used to predict the time to brittle fracture for pipes with internal defects subjected to combined internal pressure and soil deflection loading together with through-wall residual stress. To include uncertainty in the failure process, inherent defect size is treated as a stochastic variable, and modelled with an appropriate probability distribution. Microscopic examination of fracture surfaces from field failures in Australian PVC pipes suggests that the 2-parameter Weibull distribution can be applied. Monte Carlo simulation is then used to estimate lifetime probability distributions for pipes with internal defects, subjected to typical operating conditions. As with inherent defect size, the 2-parameter Weibull distribution is shown to be appropriate to model uncertainty in predicted pipe lifetime. The Weibull hazard function for pipe lifetime is then used to estimate the expected failure rate (per pipe length/per year) as a function of pipe age. To validate the model, predicted failure rates are compared to aggregated failure data from 17 UK water utilities obtained from the United Kingdom Water Industry Research (UKWIR) National Mains Failure Database. In the absence of actual operating pressure data in the UKWIR database, typical

Effect of Heart Rate Variability Biofeedback on Sport Performance, a Systematic Review.

Science.gov (United States)

Jiménez Morgan, Sergio; Molina Mora, José Arturo

2017-09-01

Aim is to determine if the training with heart rate variability biofeedback allows to improve performance in athletes of different disciplines. Methods such as database search on Web of Science, SpringerLink, EBSCO Academic Search Complete, SPORTDiscus, Pubmed/Medline, and PROQUEST Academic Research Library, as well as manual reference registration. The eligibility criteria were: (a) published scientific articles; (b) experimental studies, quasi-experimental, or case reports; (c) use of HRV BFB as main treatment; (d) sport performance as dependent variable; (e) studies published until October 2016; (f) studies published in English, Spanish, French or Portuguese. The guidelines of the PRISMA statement were followed. Out of the 451 records found, seven items were included. All studies had a small sample size (range from 1 to 30 participants). In 85.71% of the studies (n = 6) the athletes enhanced psychophysiological variables that allowed them to improve their sport performance thanks to training with heart rate variability biofeedback. Despite the limited amount of experimental studies in the field to date, the findings suggest that heart rate variability biofeedback is an effective, safe, and easy-to-learn and apply method for both athletes and coaches in order to improve sport performance.

Prediction of autoignition in a lifted methane/air flame using an unsteady flamelet/progress variable model

Energy Technology Data Exchange (ETDEWEB)

Ihme, Matthias; See, Yee Chee [Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 (United States)

2010-10-15

An unsteady flamelet/progress variable (UFPV) model has been developed for the prediction of autoignition in turbulent lifted flames. The model is a consistent extension to the steady flamelet/progress variable (SFPV) approach, and employs an unsteady flamelet formulation to describe the transient evolution of all thermochemical quantities during the flame ignition process. In this UFPV model, all thermochemical quantities are parameterized by mixture fraction, reaction progress parameter, and stoichiometric scalar dissipation rate, eliminating the explicit dependence on a flamelet time scale. An a priori study is performed to analyze critical modeling assumptions that are associated with the population of the flamelet state space. For application to LES, the UFPV model is combined with a presumed PDF closure to account for subgrid contributions of mixture fraction and reaction progress variable. The model was applied in LES of a lifted methane/air flame. Additional calculations were performed to quantify the interaction between turbulence and chemistry a posteriori. Simulation results obtained from these calculations are compared with experimental data. Compared to the SFPV results, the unsteady flamelet/progress variable model captures the autoignition process, and good agreement with measurements is obtained for mixture fraction, temperature, and species mass fractions. From the analysis of scatter data and mixture fraction-conditional results it is shown that the turbulence/chemistry interaction delays the ignition process towards lower values of scalar dissipation rate, and a significantly larger region in the flamelet state space is occupied during the ignition process. (author)

Prediction of gamma exposure rates in large nuclear craters

Energy Technology Data Exchange (ETDEWEB)

Tami, Thomas M; Day, Walter C [U.S. Army Engineer Nuclear Cratering Group, Lawrence Radiation Laboratory, Livermore, CA (United States)

1970-05-15

In many civil engineering applications of nuclear explosives there is the need to reenter the crater and lip area as soon as possible after the detonation to carry out conventional construction activities. These construction activities, however, must be delayed until the gamma dose rate, or exposure rate, in and around the crater decays to acceptable levels. To estimate the time of reentry for post-detonation construction activities, the exposure rate in the crater and lip areas must be predicted as a function of time after detonation. An accurate prediction permits a project planner to effectively schedule post-detonation activities.

Variability and predictability of decadal mean temperature and precipitation over China in the CCSM4 last millennium simulation

Science.gov (United States)

Ying, Kairan; Frederiksen, Carsten S.; Zheng, Xiaogu; Lou, Jiale; Zhao, Tianbao

2018-02-01

The modes of variability that arise from the slow-decadal (potentially predictable) and intra-decadal (unpredictable) components of decadal mean temperature and precipitation over China are examined, in a 1000 year (850-1850 AD) experiment using the CCSM4 model. Solar variations, volcanic aerosols, orbital forcing, land use, and greenhouse gas concentrations provide the main forcing and boundary conditions. The analysis is done using a decadal variance decomposition method that identifies sources of potential decadal predictability and uncertainty. The average potential decadal predictabilities (ratio of slow-to-total decadal variance) are 0.62 and 0.37 for the temperature and rainfall over China, respectively, indicating that the (multi-)decadal variations of temperature are dominated by slow-decadal variability, while precipitation is dominated by unpredictable decadal noise. Possible sources of decadal predictability for the two leading predictable modes of temperature are the external radiative forcing, and the combined effects of slow-decadal variability of the Arctic oscillation (AO) and the Pacific decadal oscillation (PDO), respectively. Combined AO and PDO slow-decadal variability is associated also with the leading predictable mode of precipitation. External radiative forcing as well as the slow-decadal variability of PDO are associated with the second predictable rainfall mode; the slow-decadal variability of Atlantic multi-decadal oscillation (AMO) is associated with the third predictable precipitation mode. The dominant unpredictable decadal modes are associated with intra-decadal/inter-annual phenomena. In particular, the El Niño-Southern Oscillation and the intra-decadal variability of the AMO, PDO and AO are the most important sources of prediction uncertainty.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

Science.gov (United States)

Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology

Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

Science.gov (United States)

Lau, K. M.; Kim, K. M.; Li, J. Y.

2001-01-01

In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

Science.gov (United States)

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Variable Rate, Adaptive Transform Tree Coding Of Images

Science.gov (United States)

Pearlman, William A.

1988-10-01

A tree code, asymptotically optimal for stationary Gaussian sources and squared error distortion [2], is used to encode transforms of image sub-blocks. The variance spectrum of each sub-block is estimated and specified uniquely by a set of one-dimensional auto-regressive parameters. The expected distortion is set to a constant for each block and the rate is allowed to vary to meet the given level of distortion. Since the spectrum and rate are different for every block, the code tree differs for every block. Coding simulations for target block distortion of 15 and average block rate of 0.99 bits per pel (bpp) show that very good results can be obtained at high search intensities at the expense of high computational complexity. The results at the higher search intensities outperform a parallel simulation with quantization replacing tree coding. Comparative coding simulations also show that the reproduced image with variable block rate and average rate of 0.99 bpp has 2.5 dB less distortion than a similarly reproduced image with a constant block rate equal to 1.0 bpp.

Heart rate variability is reduced during acute uncomplicated diverticulitis

DEFF Research Database (Denmark)

Huang, Chenxi; Alamili, Mahdi; Rosenberg, Jacob

2016-01-01

BACKGROUND: The aim of the present study was to report the trajectory of heart rate variability (HRV) indices during a low-grade acute inflammation and their associations to biomarkers for infection. METHODS: Twelve patients with uncomplicated acute diverticulitis completed this observational study...

Intelligent Online Marketing : Predicting Conversion Rate Of New Keywords

OpenAIRE

Engström, Tommy

2013-01-01

This thesis looks at the problem of predicting conversion rate of keywords in Google Adwords where little or no data for the keyword is available. Several methods are investigated and tested on data belonging to three different real world clients. The methods try to predict the conversion rate only given the keyword text. All methods are compared, using two different evaluation methods, with results showing good potential. Finally further improvements are suggested that could have a big impac...

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising.

Science.gov (United States)

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

Directory of Open Access Journals (Sweden)

Jaime Guixeres

2017-10-01

Full Text Available The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking. Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy and estimate the number of online views (mean error of 0.199. The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

Science.gov (United States)

Guixeres, Jaime; Bigné, Enrique; Ausín Azofra, Jose M.; Alcañiz Raya, Mariano; Colomer Granero, Adrián; Fuentes Hurtado, Félix; Naranjo Ornedo, Valery

2017-01-01

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube. PMID:29163251

Error analysis in predictive modelling demonstrated on mould data.

Science.gov (United States)

Baranyi, József; Csernus, Olívia; Beczner, Judit

2014-01-17

The purpose of this paper was to develop a predictive model for the effect of temperature and water activity on the growth rate of Aspergillus niger and to determine the sources of the error when the model is used for prediction. Parallel mould growth curves, derived from the same spore batch, were generated and fitted to determine their growth rate. The variances of replicate ln(growth-rate) estimates were used to quantify the experimental variability, inherent to the method of determining the growth rate. The environmental variability was quantified by the variance of the respective means of replicates. The idea is analogous to the "within group" and "between groups" variability concepts of ANOVA procedures. A (secondary) model, with temperature and water activity as explanatory variables, was fitted to the natural logarithm of the growth rates determined by the primary model. The model error and the experimental and environmental errors were ranked according to their contribution to the total error of prediction. Our method can readily be applied to analysing the error structure of predictive models of bacterial growth models, too. © 2013.

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.

Science.gov (United States)

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.

Making oneself predictable: Reduced temporal variability facilitates joint action coordination

DEFF Research Database (Denmark)

Vesper, Cordula; van der Wel, Robrecht; Knoblich, Günther

2011-01-01

Performing joint actions often requires precise temporal coordination of individual actions. The present study investigated how people coordinate their actions at discrete points in time when continuous or rhythmic information about others’ actions is not available. In particular, we tested...... the hypothesis that making oneself predictable is used as a coordination strategy. Pairs of participants were instructed to coordinate key presses in a two-choice reaction time task, either responding in synchrony (Experiments 1 and 2) or in close temporal succession (Experiment 3). Across all experiments, we...... found that coactors reduced the variability of their actions in the joint context compared with the same task performed individually. Correlation analyses indicated that the less variable the actions were, the better was interpersonal coordination. The relation between reduced variability and improved...

Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

Science.gov (United States)

Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated. Copyright © 2018 Elsevier Inc. All rights reserved.

Resting Heart Rate Predicts Depression and Cognition Early after Ischemic Stroke: A Pilot Study.

Science.gov (United States)

Tessier, Arnaud; Sibon, Igor; Poli, Mathilde; Audiffren, Michel; Allard, Michèle; Pfeuty, Micha

2017-10-01

Early detection of poststroke depression (PSD) and cognitive impairment (PSCI) remains challenging. It is well documented that the function of autonomic nervous system is associated with depression and cognition. However, their relationship has never been investigated in the early poststroke phase. This pilot study aimed at determining whether resting heart rate (HR) parameters measured in early poststroke phase (1) are associated with early-phase measures of depression and cognition and (2) could be used as new tools for early objective prediction of PSD or PSCI, which could be applicable to patients unable to answer usual questionnaires. Fifty-four patients with first-ever ischemic stroke, without cardiac arrhythmia, were assessed for resting HR and heart rate variability (HRV) within the first week after stroke and for depression and cognition during the first week and at 3 months after stroke. Multiple regression analyses controlled for age, gender, and stroke severity revealed that higher HR, lower HRV, and higher sympathovagal balance (low-frequency/high-frequency ratio of HRV) were associated with higher severity of depressive symptoms within the first week after stroke. Furthermore, higher sympathovagal balance in early phase predicted higher severity of depressive symptoms at the 3-month follow-up, whereas higher HR and lower HRV in early phase predicted lower global cognitive functioning at the 3-month follow-up. Resting HR measurements obtained in early poststroke phase could serve as an objective tool, applicable to patients unable to complete questionnaires, to help in the early prediction of PSD and PSCI. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

A Latent-Variable Causal Model of Faculty Reputational Ratings.

Science.gov (United States)

King, Suzanne; Wolfle, Lee M.

A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…

Analysis of variability and predictability challenges of wind and solar power

NARCIS (Netherlands)

Haan, de J.E.S.; Virag, A.; Kling, W.L.

2013-01-01

In power systems, reserves are essential to ensure system security, certainly when challenges of predictability (inaccurate forecast) and variability (imperfect correlation of renewable generation and system load) are causing power imbalances. Different techniques can be used to size and allocate

Who uses physician-rating websites? Differences in sociodemographic variables, psychographic variables, and health status of users and nonusers of physician-rating websites.

Science.gov (United States)

Terlutter, Ralf; Bidmon, Sonja; Röttl, Johanna

2014-03-31

The number of physician-rating websites (PRWs) is rising rapidly, but usage is still poor. So far, there has been little discussion about what kind of variables influence usage of PRWs. We focused on sociodemographic variables, psychographic variables, and health status of PRW users and nonusers. An online survey of 1006 randomly selected German patients was conducted in September 2012. We analyzed the patients' knowledge and use of online PRWs. We also analyzed the impact of sociodemographic variables (gender, age, and education), psychographic variables (eg, feelings toward the Internet, digital literacy), and health status on use or nonuse as well as the judgment of and behavior intentions toward PRWs. The survey instrument was based on existing literature and was guided by several research questions. A total of 29.3% (289/986) of the sample knew of a PRW and 26.1% (257/986) had already used a PRW. Younger people were more prone than older ones to use PRWs (t967=2.27, P=.02). Women used them more than men (χ(2) 1=9.4, P=.002), the more highly educated more than less educated people (χ(2) 4=19.7, P=.001), and people with chronic diseases more than people without (χ(2) 1=5.6, P=.02). No differences were found between users and nonusers in their daily private Internet use and in their use of the Internet for health-related information. Users had more positive feelings about the Internet and other Web-based applications in general (t489=3.07, P=.002) than nonusers, and they had higher digital literacy (t520=4.20, PUsers ascribed higher usefulness to PRWs than nonusers (t612=11.61, Pusers trusted information on PRWs to a greater degree than nonusers (t559=11.48, PUsers were also more likely to rate a physician on a PRW in the future (t367=7.63, Pincrease use of PRWs in the future.

Measurement of semiochemical release rates with a dedicated environmental control system

Science.gov (United States)

Heping Zhu; Harold W. Thistle; Christopher M. Ranger; Hongping Zhou; Brian L. Strom

2015-01-01

Insect semiochemical dispensers are commonly deployed under variable environmental conditions over a specified period. Predictions of their longevity are hampered by a lack of methods to accurately monitor and predict how primary variables affect semiochemical release rate. A system was constructed to precisely determine semiochemical release rates under...

Stock price change rate prediction by utilizing social network activities.

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

Shangkun Deng

2014-01-01

Full Text Available 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.

Heart Rate Variability in Patients with Chronic Obstructive Pulmonary Disease Treated by Noninvasive Mechanic Ventilation

Directory of Open Access Journals (Sweden)

Zekeriya Küçükdurmaz

2011-08-01

Full Text Available Aims: This study aimed to investigate heart rate variability (HRV of patients with severe COPD who are treated by noninvasive mechanic ventilation (NIMV.Patients and Method: Twenty-seven patient (58±8 years, 9 F with severe COPD treated by nocturnal NIMV at home and 23 sex and age matched volunteers (56±8 years, 11 F who has not dyspnea as a control group recruited in the study. Subjects underwent spirometry, blood gas analysis, transthoracic echocardiography, 24 hours ambulatory ECG analysis. Time domain HRV analysis performed from ambulatory ECG records. Results: 52% of patients at NYHA functional class II, 36% at class III, and 12% at class IV when they have been treated by NIMV. Groups were similar for age and sex (p>0.05 for both. Heart rates of patients were higher significantly than controls’ (p0.05. But, systolic pulmonary pressures were higher of COPD group (p<0.01. 24 hours heart rate was higher, and standard deviation of normal R-R intervals (SDNN 24 hours, SDNN night, SDNN day, SDNN index (SDNNI and standard deviation of mean R-R intervals (SDANNI values were lower in COPD group significantly. SDNN was inversely correlated with duration of daily NIMV usage, intensive care unit administration and entubation rate and PaCO2. SDNNI was inversely correlated with functional class, duration of daily NIMV usage, intensive care unit administration rate and PaCO2. Else, SDNNI was correlated with predicted forced vital capacity % (FVC% and predicted forced expiratory volume at 1 second % (FEV1%.Conclusion: Time domain HRV decreases in patients with severe COPD. Decrease is correlated with severity of disease, and it presents in despite of the chronic nocturnal NIMV application. These patients have high risk for cardiovascular morbidity and mortality and should be monitored and manegement for cardiovascular events.

It's the People, Stupid: The Role of Personality and Situational Variable in Predicting Decisionmaker Behavior

National Research Council Canada - National Science Library

Sticha, Paul J; Buede, Dennis M; Rees, Richard L

2006-01-01

.... to identity assumptions and determinant variables, and to quantify each variable's relative contribution to the prediction, producing a graphical representation of the analysis with explicit levels of uncertainty...

Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

Directory of Open Access Journals (Sweden)

Ziyang Cao

2014-11-01

Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

Heart Rate Variability and Drawing Impairment in Hypoxemic COPD

Science.gov (United States)

Incalzi, Raffaele Antonelli; Corsonello, Andrea; Trojano, Luigi; Pedone, Claudio; Acanfora, Domenico; Spada, Aldo; D'Addio, Gianni; Maestri, Roberto; Rengo, Franco; Rengo, Giuseppe

2009-01-01

We studied 54 patients with hypoxemic chronic obstructive pulmonary disease (COPD). The Mini Mental State Examination and the Mental Deterioration Battery were used for neuropsychological assessment. Heart rate variability (HRV) was assessed based on 24-h Holter ECG recording. Mann-Whitney test was used to compare HRV parameters of patients…

Discrete rate and variable power adaptation for underlay cognitive networks

KAUST Repository

Abdallah, Mohamed M.; Salem, Ahmed H.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

2010-01-01

We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power

THE RELATIVE IMPORTANCE OF FINANCIAL RATIOS AND NONFINANCIAL VARIABLES IN PREDICTING OF INSOLVENCY

Directory of Open Access Journals (Sweden)

Ivica Pervan

2013-02-01

Full Text Available One of the most important decisions in every bank is approving loans to firms, which is based on evaluated credit risk and collateral. Namely, it is necessary to evaluate the risk that client will be unable to repay the obligations according to the contract. After Beaver's (1967 and Altman's (1968 seminal papers many authors extended the initial research by changing the methodology, samples, countries, etc. But majority of business failure papers as predictors use financial ratios, while in the real life banks combine financial and nonfinancial variables. In order to test predictive power of nonfinancial variables authors in the paper compare two insolvency prediction models. The first model that used financial rations resulted with classification accuracy of 82.8%, while the combined model with financial and nonfinancial variables resulted with classification accuracy of 88.1%.

Evaluation of sympathetic nerve system activity with MIBG. Comparison with heart rate variability

International Nuclear Information System (INIS)

Kurata, Chinori; Wakabayashi, Yasushi; Shouda, Sakae; Mikami, Tadashi; Tawarahara, Kei; Sugiyama, Tsuyoshi; Nakano, Tomoyasu; Suzuki, Toshihiko.

1997-01-01

Authors attempted to elucidate the relations of plasma concentration of norepinephrine (pNE) and findings of heart rate variability and MIBG myocardial scintigraphy and evaluated cardiac autonomic nervous activity in chronic renal failure. Subjects were 211 patients with various heart diseases (coronary artery lesion, cardiomyopathy, hypertension, diabetes mellitus, renal failure and so on), 60 patients with artificial kidney due to chronic renal failure, 13 of whom were found to have coronary arterial disease by Tl myocardial scintigraphy, and 14 normal volunteers. ECG was recorded with the portable recorder for heart rate variability. Together with collection of blood for pNE measurement, myocardial scintigraphy was done at 15 and 150 min after intravenous administration of 111 MBq of MIBG for acquisition of early and delayed, respectively, images of the frontal breast. Accumulation at and elimination during the time points of MIBG were computed in cps unit. Variability of heart rate was found to have the correlation positive with MIBG delayed accumulation and negative with the elimination, and pNE, negative with heart rate variability and the delayed accumulation and positive with the elimination. Thus cardiac autonomic nervous abnormality was suggested to occur before uremic cardiomyopathy. (K.H.)

Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands

Science.gov (United States)

Braun, Jean; Gemignani, Lorenzo; van der Beek, Peter

2018-03-01

One of the main purposes of detrital thermochronology is to provide constraints on the regional-scale exhumation rate and its spatial variability in actively eroding mountain ranges. Procedures that use cooling age distributions coupled with hypsometry and thermal models have been developed in order to extract quantitative estimates of erosion rate and its spatial distribution, assuming steady state between tectonic uplift and erosion. This hypothesis precludes the use of these procedures to assess the likely transient response of mountain belts to changes in tectonic or climatic forcing. Other methods are based on an a priori knowledge of the in situ distribution of ages to interpret the detrital age distributions. In this paper, we describe a simple method that, using the observed detrital mineral age distributions collected along a river, allows us to extract information about the relative distribution of erosion rates in an eroding catchment without relying on a steady-state assumption, the value of thermal parameters or an a priori knowledge of in situ age distributions. The model is based on a relatively low number of parameters describing lithological variability among the various sub-catchments and their sizes and only uses the raw ages. The method we propose is tested against synthetic age distributions to demonstrate its accuracy and the optimum conditions for it use. In order to illustrate the method, we invert age distributions collected along the main trunk of the Tsangpo-Siang-Brahmaputra river system in the eastern Himalaya. From the inversion of the cooling age distributions we predict present-day erosion rates of the catchments along the Tsangpo-Siang-Brahmaputra river system, as well as some of its tributaries. We show that detrital age distributions contain dual information about present-day erosion rate, i.e., from the predicted distribution of surface ages within each catchment and from the relative contribution of any given catchment to the

Extracting information on the spatial variability in erosion rate stored in detrital cooling age distributions in river sands

Directory of Open Access Journals (Sweden)

J. Braun

2018-03-01

Full Text Available One of the main purposes of detrital thermochronology is to provide constraints on the regional-scale exhumation rate and its spatial variability in actively eroding mountain ranges. Procedures that use cooling age distributions coupled with hypsometry and thermal models have been developed in order to extract quantitative estimates of erosion rate and its spatial distribution, assuming steady state between tectonic uplift and erosion. This hypothesis precludes the use of these procedures to assess the likely transient response of mountain belts to changes in tectonic or climatic forcing. Other methods are based on an a priori knowledge of the in situ distribution of ages to interpret the detrital age distributions. In this paper, we describe a simple method that, using the observed detrital mineral age distributions collected along a river, allows us to extract information about the relative distribution of erosion rates in an eroding catchment without relying on a steady-state assumption, the value of thermal parameters or an a priori knowledge of in situ age distributions. The model is based on a relatively low number of parameters describing lithological variability among the various sub-catchments and their sizes and only uses the raw ages. The method we propose is tested against synthetic age distributions to demonstrate its accuracy and the optimum conditions for it use. In order to illustrate the method, we invert age distributions collected along the main trunk of the Tsangpo–Siang–Brahmaputra river system in the eastern Himalaya. From the inversion of the cooling age distributions we predict present-day erosion rates of the catchments along the Tsangpo–Siang–Brahmaputra river system, as well as some of its tributaries. We show that detrital age distributions contain dual information about present-day erosion rate, i.e., from the predicted distribution of surface ages within each catchment and from the relative contribution of

General anesthesia suppresses normal heart rate variability in humans

Science.gov (United States)

Matchett, Gerald; Wood, Philip

2014-06-01

The human heart normally exhibits robust beat-to-beat heart rate variability (HRV). The loss of this variability is associated with pathology, including disease states such as congestive heart failure (CHF). The effect of general anesthesia on intrinsic HRV is unknown. In this prospective, observational study we enrolled 100 human subjects having elective major surgical procedures under general anesthesia. We recorded continuous heart rate data via continuous electrocardiogram before, during, and after anesthesia, and we assessed HRV of the R-R intervals. We assessed HRV using several common metrics including Detrended Fluctuation Analysis (DFA), Multifractal Analysis, and Multiscale Entropy Analysis. Each of these analyses was done in each of the four clinical phases for each study subject over the course of 24 h: Before anesthesia, during anesthesia, early recovery, and late recovery. On average, we observed a loss of variability on the aforementioned metrics that appeared to correspond to the state of general anesthesia. Following the conclusion of anesthesia, most study subjects appeared to regain their normal HRV, although this did not occur immediately. The resumption of normal HRV was especially delayed on DFA. Qualitatively, the reduction in HRV under anesthesia appears similar to the reduction in HRV observed in CHF. These observations will need to be validated in future studies, and the broader clinical implications of these observations, if any, are unknown.

Fabrication and evaluation of variable rate fertilizer system

Directory of Open Access Journals (Sweden)

A Damirchi

2015-09-01

Full Text Available Introduction: In conventional farming, the soil and crop are considered uniform in different locations of the farm and the fertilizers are applied according to the average of soil needs with an additional percentage for safety (Loghavi, 2003. Non-essential chemical fertilizers in the field have harmful effects and social, economic and environmental concerns will increase. Many fertilizers go into the surface waters and ground waters and cause poisoning and environmental pollution without being absorbed by the plants. In variable rate technology, the soil fertilizer needs a map of all parts of the farm which is prepared with the GIS system. This map is uploaded on the computer before variable rate fertilizer machine starts. The computer continually controls the fertilizing rate for each part of the farm using a fertilizing map and global positioning system. The purpose of this study is to construct and evaluate a map-based variable rate fertilizer system that can be installed on a common fertilizer in Iran to be used as a variable rate system. Materials and methods: In common variable rate fertilizers, the rotational speed change of the distributor shaft is used to apply fertilizers. In this way, a DC motor is assembled on the main shaft of all distributors, which reduces the fertilizing accuracy. The reason for this is that there is no separation for units along the width of the fertilizer. Therefore, we used one DC motor for each distributor and another motor to rotate the agitator in the tank. System Set up: To design and select a suitable engine, the required torque for the rotation distributor shaft was measured by a torque meter and the amount of 2.1 Nm was acquired for that. With regard to the maximum rate of nitrogen fertilizer for land and tractor speed at the time of fertilizing, the order of 350 kg per hectare and 8 km per hour, the maximum distributor shaft speed and power required to rotate distributor shaft were calculated to be 55

Classification of acute stress using linear and non-linear heart rate variability analysis derived from sternal ECG

DEFF Research Database (Denmark)

Tanev, George; Saadi, Dorthe Bodholt; Hoppe, Karsten

2014-01-01

Chronic stress detection is an important factor in predicting and reducing the risk of cardiovascular disease. This work is a pilot study with a focus on developing a method for detecting short-term psychophysiological changes through heart rate variability (HRV) features. The purpose of this pilot...... study is to establish and to gain insight on a set of features that could be used to detect psychophysiological changes that occur during chronic stress. This study elicited four different types of arousal by images, sounds, mental tasks and rest, and classified them using linear and non-linear HRV...

Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

International Nuclear Information System (INIS)

Westphal, T; Nijssen, R P L

2014-01-01

The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort

Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

Science.gov (United States)

Westphal, T.; Nijssen, R. P. L.

2014-12-01

The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort.

Developing models for the prediction of hospital healthcare waste generation rate.

Science.gov (United States)

Tesfahun, Esubalew; Kumie, Abera; Beyene, Abebe

2016-01-01

An increase in the number of health institutions, along with frequent use of disposable medical products, has contributed to the increase of healthcare waste generation rate. For proper handling of healthcare waste, it is crucial to predict the amount of waste generation beforehand. Predictive models can help to optimise healthcare waste management systems, set guidelines and evaluate the prevailing strategies for healthcare waste handling and disposal. However, there is no mathematical model developed for Ethiopian hospitals to predict healthcare waste generation rate. Therefore, the objective of this research was to develop models for the prediction of a healthcare waste generation rate. A longitudinal study design was used to generate long-term data on solid healthcare waste composition, generation rate and develop predictive models. The results revealed that the healthcare waste generation rate has a strong linear correlation with the number of inpatients (R(2) = 0.965), and a weak one with the number of outpatients (R(2) = 0.424). Statistical analysis was carried out to develop models for the prediction of the quantity of waste generated at each hospital (public, teaching and private). In these models, the number of inpatients and outpatients were revealed to be significant factors on the quantity of waste generated. The influence of the number of inpatients and outpatients treated varies at different hospitals. Therefore, different models were developed based on the types of hospitals. © The Author(s) 2015.

Variable mutation rates as an adaptive strategy in replicator populations.

Directory of Open Access Journals (Sweden)

Michael Stich

2010-06-01

Full Text Available For evolving populations of replicators, there is much evidence that the effect of mutations on fitness depends on the degree of adaptation to the selective pressures at play. In optimized populations, most mutations have deleterious effects, such that low mutation rates are favoured. In contrast to this, in populations thriving in changing environments a larger fraction of mutations have beneficial effects, providing the diversity necessary to adapt to new conditions. What is more, non-adapted populations occasionally benefit from an increase in the mutation rate. Therefore, there is no optimal universal value of the mutation rate and species attempt to adjust it to their momentary adaptive needs. In this work we have used stationary populations of RNA molecules evolving in silico to investigate the relationship between the degree of adaptation of an optimized population and the value of the mutation rate promoting maximal adaptation in a short time to a new selective pressure. Our results show that this value can significantly differ from the optimal value at mutation-selection equilibrium, being strongly influenced by the structure of the population when the adaptive process begins. In the short-term, highly optimized populations containing little variability respond better to environmental changes upon an increase of the mutation rate, whereas populations with a lower degree of optimization but higher variability benefit from reducing the mutation rate to adapt rapidly. These findings show a good agreement with the behaviour exhibited by actual organisms that replicate their genomes under broadly different mutation rates.

Decreased heart rate variability in surgeons during night shifts

DEFF Research Database (Denmark)

Amirian, Ilda; Toftegård Andersen, Lærke; Rosenberg, Jacob

2014-01-01

BACKGROUND: Heart rate variability (HRV) has been used as a measure of stress and mental strain in surgeons. Low HRV has been associated with death and increased risk of cardiac events in the general population. The aim of this study was to clarify the effect of a 17-hour night shift on surgeons'...

Predicting High Frequency Exchange Rates using Machine Learning

OpenAIRE

Palikuca, Aleksandar; Seidl,, Timo

2016-01-01

This thesis applies a committee of Artificial Neural Networks and Support Vector Machines on high-dimensional, high-frequency EUR/USD exchange rate data in an effort to predict directional market movements on up to a 60 second prediction horizon. The study shows that combining multiple classifiers into a committee produces improved precision relative to the best individual committee members and outperforms previously reported results. A trading simulation implementing the committee classifier...

Heart-rate variability and precompetitive anxiety in swimmers

OpenAIRE

Cervantes Blásquez, Julio César

2009-01-01

The aim of this study was to test the utility of heart-rate variability (HRV) analyses as a noninvasive means of quantifying cardiac autonomic regulation during precompetitive anxiety situations in swimmers. Psychophysiological state evaluation of 10 volunteer «master» swimmers (6 women and 4 men) was obtained by comparing baseline training condition (TC) with competition condition (CC). Self-evaluation of precompetitive somatic anxiety measured by CSAI-2 showed significant increase from the ...

Increased heart rate variability during nondirective meditation.

Science.gov (United States)

Nesvold, Anders; Fagerland, Morten W; Davanger, Svend; Ellingsen, Øyvind; Solberg, Erik E; Holen, Are; Sevre, Knut; Atar, Dan

2012-08-01

Meditation practices are in use for relaxation and stress reduction. Some studies indicate beneficial cardiovascular health effects of meditation. The effects on the autonomous nervous system seem to vary among techniques. The purpose of the present study was to identify autonomic nerve activity changes during nondirective meditation. Heart rate variability (HRV), blood pressure variability (BPV), and baroreflex sensitivity (BRS) were monitored in 27 middle-aged healthy participants of both genders, first during 20 min regular rest with eyes closed, thereafter practising Acem meditation for 20 min. Haemodynamic and autonomic data were collected continuously (beat-to-beat) and non-invasively. HRV and BPV parameters were estimated by power spectral analyses, computed by an autoregressive model. Spontaneous activity of baroreceptors were determined by the sequence method. Primary outcomes were changes in HRV, BPV, and BRS between rest and meditation. HRV increased in the low-frequency (LF) and high-frequency (HF) bands during meditation, compared with rest (p = 0.014, 0.013, respectively). Power spectral density of the RR-intervals increased as well (p = 0.012). LF/HF ratio decreased non-significantly, and a reduction of LF-BPV power was observed during meditation (p < 0.001). There was no significant difference in BRS. Respiration and heart rates remained unchanged. Blood pressure increased slightly during meditation. There is an increased parasympathetic and reduced sympathetic nerve activity and increased overall HRV, while practising the technique. Hence, nondirective meditation by the middle aged may contribute towards a reduction of cardiovascular risk.

Effect of flow rate on environmental variables and phytoplankton dynamics: results from field enclosures

Science.gov (United States)

Zhang, Haiping; Chen, Ruihong; Li, Feipeng; Chen, Ling

2015-03-01

To investigate the effects of flow rate on phytoplankton dynamics and related environment variables, a set of enclosure experiments with different flow rates were conducted in an artificial lake. We monitored nutrients, temperature, dissolved oxygen, pH, conductivity, turbidity, chlorophyll- a and phytoplankton levels. The lower biomass in all flowing enclosures showed that flow rate significantly inhibited the growth of phytoplankton. A critical flow rate occurred near 0.06 m/s, which was the lowest relative inhibitory rate. Changes in flow conditions affected algal competition for light, resulting in a dramatic shift in phytoplankton composition, from blue-green algae in still waters to green algae in flowing conditions. These findings indicate that critical flow rate can be useful in developing methods to reduce algal bloom occurrence. However, flow rate significantly enhanced the inter-relationships among environmental variables, in particular by inducing higher water turbidity and vegetative reproduction of periphyton ( Spirogyra). These changes were accompanied by a decrease in underwater light intensity, which consequently inhibited the photosynthetic intensity of phytoplankton. These results warn that a universal critical flow rate might not exist, because the effect of flow rate on phytoplankton is interlinked with many other environmental variables.

Prediction of waste glass melt rates

International Nuclear Information System (INIS)

Lee, L.

1987-01-01

Under contract to the Department of Energy, the Du Pont Company has begun construction of a Defense Waste Processing Facility to immobilize radioactive wastes now stored as liquids at the Department of Energy's Savannah River Plant. The immobilization process solidifies waste sludge by vitrification into a leach-resistant borosilicate glass. Development of this process has been the responsibility of the Savannah River Laboratory. As part of the development, a simple model was developed to predict the melt rates for the waste glass melter. This model is based on an energy balance for the cold cap and gives very good agreement with melt rate data obtained from experimental campaigns in smaller scale waste glass melters

The effect of metaprolol alone and metaprolol plus bromazepam on heart rate and heart rate variability during multislice computed tomography angiography

International Nuclear Information System (INIS)

Tuyyab, F.; Naeem, M.Y.; Maken, G.R.; Najfi, M.H.; Hassan, F.

2012-01-01

Objective: The purpose of this study was to determine the effect of metaprolol alone and metaprolol plus bromazepam on heart rate and heart rate variability during multi slice computed tomography (MSCT) angiography. Methodology: This was a Double blind randomized controlled trial was conducted at AFIC/NIHD, Rawalpindi, from May 2011 to November 2011. Patients undergoing first MSCT angiography meeting inclusion criteria with heart rates (HR) more than 80 beats/min were included. Patients were randomized in to two groups using random numbers table. Group 1 was administered metaprolol plus placebo while group 2 was administered metaprolol plus bromazepam one hour before the scan. Both groups had scans under strictly similar conditions. HR before and during scan along with heart rate variability (HRV) were recorded. Results: A total of 80 patients were included. Patients mean age was 49 + 13, 57 % were males while 43 % were females. Risk factor profile was similar in both groups. HR reduction in group 1 was 15+ 6.0 and in group 2, was 21+9.0 (p= 0.002). HRV in group 1 was 3.9 + 1.32 and in group 2 was 2.3 + 1.0 (p= 0.003). Group 2 had significantly lower HR and significantly less HRV as compared with group 1. Conclusion: Combination of bromazepam and metaprolol results in significant and further reduction in heart rate and heart rate variability than metaprolol alone. Both drugs can be used together for a better control of heart rate and heart rate variability during MSCT angiography for improving the quality of images. (author)

Predictive coding of dynamical variables in balanced spiking networks.

Science.gov (United States)

Boerlin, Martin; Machens, Christian K; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

CREME96 and Related Error Rate Prediction Methods

Science.gov (United States)

Adams, James H., Jr.

2012-01-01

Predicting the rate of occurrence of single event effects (SEEs) in space requires knowledge of the radiation environment and the response of electronic devices to that environment. Several analytical models have been developed over the past 36 years to predict SEE rates. The first error rate calculations were performed by Binder, Smith and Holman. Bradford and Pickel and Blandford, in their CRIER (Cosmic-Ray-Induced-Error-Rate) analysis code introduced the basic Rectangular ParallelePiped (RPP) method for error rate calculations. For the radiation environment at the part, both made use of the Cosmic Ray LET (Linear Energy Transfer) spectra calculated by Heinrich for various absorber Depths. A more detailed model for the space radiation environment within spacecraft was developed by Adams and co-workers. This model, together with a reformulation of the RPP method published by Pickel and Blandford, was used to create the CR ME (Cosmic Ray Effects on Micro-Electronics) code. About the same time Shapiro wrote the CRUP (Cosmic Ray Upset Program) based on the RPP method published by Bradford. It was the first code to specifically take into account charge collection from outside the depletion region due to deformation of the electric field caused by the incident cosmic ray. Other early rate prediction methods and codes include the Single Event Figure of Merit, NOVICE, the Space Radiation code and the effective flux method of Binder which is the basis of the SEFA (Scott Effective Flux Approximation) model. By the early 1990s it was becoming clear that CREME and the other early models needed Revision. This revision, CREME96, was completed and released as a WWW-based tool, one of the first of its kind. The revisions in CREME96 included improved environmental models and improved models for calculating single event effects. The need for a revision of CREME also stimulated the development of the CHIME (CRRES/SPACERAD Heavy Ion Model of the Environment) and MACREE (Modeling and

Prediction of hospital mortality by changes in the estimated glomerular filtration rate (eGFR).

LENUS (Irish Health Repository)

Berzan, E

2015-03-01

Deterioration of physiological or laboratory variables may provide important prognostic information. We have studied whether a change in estimated glomerular filtration rate (eGFR) value calculated using the (Modification of Diet in Renal Disease (MDRD) formula) over the hospital admission, would have predictive value. An analysis was performed on all emergency medical hospital episodes (N = 61964) admitted between 1 January 2002 and 31 December 2011. A stepwise logistic regression model examined the relationship between mortality and change in renal function from admission to discharge. The fully adjusted Odds Ratios (OR) for 5 classes of GFR deterioration showed a stepwise increased risk of 30-day death with OR\\'s of 1.42 (95% CI: 1.20, 1.68), 1.59 (1.27, 1.99), 2.71 (2.24, 3.27), 5.56 (4.54, 6.81) and 11.9 (9.0, 15.6) respectively. The change in eGFR during a clinical episode, following an emergency medical admission, powerfully predicts the outcome.

Potential of on-line visible and near infrared spectroscopy for measurement of pH for deriving variable rate lime recommendations.

Science.gov (United States)

Tekin, Yücel; Kuang, Boyan; Mouazen, Abdul M

2013-08-08

This paper aims at exploring the potential of visible and near infrared (vis-NIR) spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52), as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14). On-line predicted pH of all points (e.g., 2,160) resulted in the largest overall field virtual lime requirement (1.404 t), as compared to those obtained with 16 validation points off-line prediction (0.28 t), on-line prediction (0.14 t) and laboratory reference measurement (0.48 t). The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Potential of On-Line Visible and Near Infrared Spectroscopy for Measurement of pH for Deriving Variable Rate Lime Recommendations

Directory of Open Access Journals (Sweden)

Yücel Tekin

2013-08-01

Full Text Available This paper aims at exploring the potential of visible and near infrared (vis-NIR spectroscopy for on-line measurement of soil pH, with the intention to produce variable rate lime recommendation maps. An on-line vis-NIR soil sensor set up to a frame was used in this study. Lime application maps, based on pH predicted by vis-NIR techniques, were compared with maps based on traditional lab-measured pH. The validation of the calibration model using off-line spectra provided excellent prediction accuracy of pH (R2 = 0.85, RMSEP = 0.18 and RPD = 2.52, as compared to very good accuracy obtained with the on-line measured spectra (R2 = 0.81, RMSEP = 0.20 and RPD = 2.14. On-line predicted pH of all points (e.g., 2,160 resulted in the largest overall field virtual lime requirement (1.404 t, as compared to those obtained with 16 validation points off-line prediction (0.28 t, on-line prediction (0.14 t and laboratory reference measurement (0.48 t. The conclusion is that the vis-NIR spectroscopy can be successfully used for the prediction of soil pH and for deriving lime recommendations. The advantage of the on-line sensor over sampling with limited number of samples is that more detailed information about pH can be obtained, which is the reason for a higher but precise calculated lime recommendation rate.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

Science.gov (United States)

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Activity, exposure rate and spectrum prediction with Java programming

International Nuclear Information System (INIS)

Sahin, D.; Uenlue, K.

2009-01-01

In order to envision the radiation exposure during Neutron Activation Analysis (NAA) experiments, a software called Activity Predictor is developed using Java TM programming language. The Activity Predictor calculates activities, exposure rates and gamma spectra of activated samples for NAA experiments performed at Radiation Science and Engineering Center (RSEC), Penn State Breazeale Reactor (PSBR). The calculation procedure for predictions involves both analytical and Monte Carlo methods. The Activity Predictor software is validated with a series of activation experiments. It has been found that Activity Predictor software calculates the activities and exposure rates precisely. The software also predicts gamma spectrum for each measurement. The predicted spectra agreed partially with measured spectra. The error in net photo peak areas varied from 4.8 to 51.29%, which is considered to be due to simplistic modeling, statistical fluctuations and unknown contaminants in the samples. (author)

Variability, Predictability, and Race Factors Affecting Performance in Elite Biathlon.

Science.gov (United States)

Skattebo, Øyvind; Losnegard, Thomas

2018-03-01

To investigate variability, predictability, and smallest worthwhile performance enhancement in elite biathlon sprint events. In addition, the effects of race factors on performance were assessed. Data from 2005 to 2015 including >10,000 and >1000 observations for each sex for all athletes and annual top-10 athletes, respectively, were included. Generalized linear mixed models were constructed based on total race time, skiing time, shooting time, and proportions of targets hit. Within-athlete race-to-race variability was expressed as coefficient of variation of performance times and standard deviation (SD) in proportion units (%) of targets hit. The models were adjusted for random and fixed effects of subject identity, season, event identity, and race factors. The within-athlete variability was independent of sex and performance standard of athletes: 2.5-3.2% for total race time, 1.5-1.8% for skiing time, and 11-15% for shooting times. The SD of the proportion of hits was ∼10% in both shootings combined (meaning ±1 hit in 10 shots). The predictability in total race time was very high to extremely high for all athletes (ICC .78-.84) but trivial for top-10 athletes (ICC .05). Race times during World Championships and Olympics were ∼2-3% faster than in World Cups. Moreover, race time increased by ∼2% per 1000 m of altitude, by ∼5% per 1% of gradient, by 1-2% per 1 m/s of wind speed, and by ∼2-4% on soft vs hard tracks. Researchers and practitioners should focus on strategies that improve biathletes' performance by at least 0.8-0.9%, corresponding to the smallest worthwhile enhancement (0.3 × within-athlete variability).

Heart-Rate Variability-More than Heart Beats?

Science.gov (United States)

Ernst, Gernot

2017-01-01

Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa . But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion-cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.

Attempt to determine radon entry rate and air exchange rate variable in time from the time course of indoor radon concentration

Energy Technology Data Exchange (ETDEWEB)

Thomas, J [State Office for Nuclear Protection, Prague (Czech Republic)

1996-12-31

For radon diagnosis in houses the `ventilation experiment` was used as a standard method. After removal of indoor radon by draught the build-up of radon concentration a(t) [Bq/m{sup 3}] was measured continuously and from the time course the constant radon entry rate A [Bq/h] and the exchange rate k [h{sup -1}] was calculated by regression analysis using model relation a(t) A(1-e{sup -kt})/kV with V [m{sup 3}] for volume of the room. The conditions have to be stable for several hours so that the assumption of constant A and k was justified. During the day both quantities were independently (?) changing, therefore a method to determine variable entry rate A(t) and exchange rate k(t) is needed for a better understanding of the variability of the indoor radon concentration. Two approaches are given for the determination of variable in time radon entry rates and air exchange rates from continuously measured indoor radon concentration - numerical solution of the equivalent difference equations in deterministic or statistic form. The approaches are not always successful. Failures giving a right ration for the searched rates but not of the rates them self could not be explained.

Predicting umbilical artery pH during labour: Development and validation of a nomogram using fetal heart rate patterns.

Science.gov (United States)

Ramanah, Rajeev; Omar, Sikiyah; Guillien, Alicia; Pugin, Aurore; Martin, Alain; Riethmuller, Didier; Mottet, Nicolas

2018-06-01

Nomograms are statistical models that combine variables to obtain the most accurate and reliable prediction for a particular risk. Fetal heart rate (FHR) interpretation alone has been found to be poorly predictive for fetal acidosis while other clinical risk factors exist. The aim of this study was to create and validate a nomogram based on FHR patterns and relevant clinical parameters to provide a non-invasive individualized prediction of umbilical artery pH during labour. A retrospective observational study was conducted on 4071 patients in labour presenting singleton pregnancies at >34 gestational weeks and delivering vaginally. Clinical characteristics, FHR patterns and umbilical cord gas of 1913 patients were used to construct a nomogram predicting an umbilical artery (Ua) pH <7.18 (10th centile of the study population) after an univariate and multivariate stepwise logistic regression analysis. External validation was obtained from an independent cohort of 2158 patients. Area under the receiver operating characteristics (ROC) curve, sensitivity, specificity, positive and negative predictive values of the nomogram were determined. Upon multivariate analysis, parity (p < 0.01), induction of labour (p = 0.01), a prior uterine scar (p = 0.02), maternal fever (p = 0.02) and the type of FHR (p < 0.01) were significantly associated with an Ua pH <7.18 (p < 0.05). Apgar score at 1, 5 and 10 min were significantly lower in the group with an Ua pH <7.18 (p < 0.01). The nomogram constructed had a Concordance Index of 0.75 (area under the curve) with a sensitivity of 57%, a specificity of 91%, a negative predictive value of 5% and a positive predictive value of 99%. Calibration found no difference between the predicted probabilities and the observed rate of Ua pH <7.18 (p = 0.63). The validation set had a Concordance Index of 0.72 and calibration with a p < 0.77. We successfully developed and validated a nomogram to predict Ua pH by

Rate-Based Model Predictive Control of Turbofan Engine Clearance

Science.gov (United States)

DeCastro, Jonathan A.

2006-01-01

An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.

Relative influence of age, resting heart rate and sedentary life style in short-term analysis of heart rate variability

OpenAIRE

E.R. Migliaro; P. Contreras; S. Bech; A. Etxagibel; M. Castro; R. Ricca; K. Vicente

2001-01-01

In order to assess the relative influence of age, resting heart rate (HR) and sedentary life style, heart rate variability (HRV) was studied in two different groups. The young group (YG) consisted of 9 sedentary subjects aged 15 to 20 years (YG-S) and of 9 nonsedentary volunteers (YG-NS) also aged 15 to 20. The elderly sedentary group (ESG) consisted of 16 sedentary subjects aged 39 to 82 years. HRV was assessed using a short-term procedure (5 min). R-R variability was calculated in the time-...

Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements

NARCIS (Netherlands)

Muñoz Venegas, Loretto; van Roon, Arie; Riese, Harriette; Thio, Chris; Oostenbroek, Emma; Westrik, Iris; de Geus, Eco J. C.; Gansevoort, Ron; Lefrandt, Joop; Nolte, Ilja M.; Snieder, Harold

2015-01-01

Objectives In order to investigate the applicability of routine 10s electrocardiogram (ECG) recordings for time-domain heart rate variability (HRV) calculation we explored to what extent these (ultra-)short recordings capture the "actual" HRV. Methods The standard deviation of normal-to-normal

Individual variability in heart rate recovery after standardized submaximal exercise

NARCIS (Netherlands)

van der Does, Hendrike; Brink, Michel; Visscher, Chris; Lemmink, Koen

2012-01-01

To optimize performance, coaches and athletes are always looking for the right balance between training load and recovery. Therefore, closely monitoring of athletes is important. Heart rate recovery (HRR) after standardized sub maximal exercise has been proposed as a useful variable to monitor

Exchange rate predictability and state-of-the-art models

OpenAIRE

Yeșin, Pınar

2016-01-01

This paper empirically evaluates the predictive performance of the International Monetary Fund's (IMF) exchange rate assessments with respect to future exchange rate movements. The assessments of real trade-weighted exchange rates were conducted from 2006 to 2011, and were based on three state-of-the-art exchange rate models with a medium-term focus which were developed by the IMF. The empirical analysis using 26 advanced and emerging market economy currencies reveals that the "diagnosis" of ...

The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

Science.gov (United States)

Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

2014-05-01

Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

Statistical analysis of nuclear power plant pump failure rate variability: some preliminary results

International Nuclear Information System (INIS)

Martz, H.F.; Whiteman, D.E.

1984-02-01

In-Plant Reliability Data System (IPRDS) pump failure data on over 60 selected pumps in four nuclear power plants are statistically analyzed using the Failure Rate Analysis Code (FRAC). A major purpose of the analysis is to determine which environmental, system, and operating factors adequately explain the variability in the failure data. Catastrophic, degraded, and incipient failure severity categories are considered for both demand-related and time-dependent failures. For catastrophic demand-related pump failures, the variability is explained by the following factors listed in their order of importance: system application, pump driver, operating mode, reactor type, pump type, and unidentified plant-specific influences. Quantitative failure rate adjustments are provided for the effects of these factors. In the case of catastrophic time-dependent pump failures, the failure rate variability is explained by three factors: reactor type, pump driver, and unidentified plant-specific influences. Finally, point and confidence interval failure rate estimates are provided for each selected pump by considering the influential factors. Both types of estimates represent an improvement over the estimates computed exclusively from the data on each pump

Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2013-01-14

The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

Hemodynamic variables predict outcome of emergency thoracotomy in the pediatric trauma population.

Science.gov (United States)

Wyrick, Deidre L; Dassinger, Melvin S; Bozeman, Andrew P; Porter, Austin; Maxson, R Todd

2014-09-01

Limited data exist regarding indications for resuscitative emergency thoracotomy (ETR) in the pediatric population. We attempt to define the presenting hemodynamic parameters that predict survival for pediatric patients undergoing ETR. We reviewed all pediatric patients (age <18years), entered into the National Trauma Data Bank from 2007 to 2010, who underwent ETR within one hour of ED arrival. Mechanism of injury and hemodynamics were analyzed using Chi squared and Wilcoxon tests. 316 children (70 blunt, 240 penetrating) underwent ETR, 31% (98/316) survived to discharge. Less than 5% of patients survived when presenting SBP was ≤50mmHg or heart rate was ≤70bpm. For blunt injuries there were no survivors with a pulse ≤80bpm or SBP ≤60mmHg. When survivors were compared to nonsurvivors, blood pressure, pulse, and injury type were statistically significant when treated as independent variables and in a logistic regression model. When ETR was performed for SBP ≤50mmHg or for heart rate ≤70bpm less than 5% of patients survived. There were no survivors of blunt trauma when SBP was ≤60mmHg or pulse was ≤80bpm. This review suggests that ETR may have limited benefit in these patients. Copyright © 2014 Elsevier Inc. All rights reserved.

Variable beam dose rate and DMLC IMRT to moving body anatomy

International Nuclear Information System (INIS)

Papiez, Lech; Abolfath, Ramin M.

2008-01-01

Derivation of formulas relating leaf speeds and beam dose rates for delivering planned intensity profiles to static and moving targets in dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is presented. The analysis of equations determining algorithms for DMLC IMRT delivery under a variable beam dose rate reveals a multitude of possible delivery strategies for a given intensity map and for any given target motion patterns. From among all equivalent delivery strategies for DMLC IMRT treatments specific subclasses of strategies can be selected to provide deliveries that are particularly suitable for clinical applications providing existing delivery devices are used. Special attention is devoted to the subclass of beam dose rate variable DMLC delivery strategies to moving body anatomy that generalize existing techniques of such deliveries in Varian DMLC irradiation methodology to static body anatomy. Few examples of deliveries from this subclass of DMLC IMRT irradiations are investigated to illustrate the principle and show practical benefits of proposed techniques.

Variable beam dose rate and DMLC IMRT to moving body anatomy

Energy Technology Data Exchange (ETDEWEB)

Papiez, Lech; Abolfath, Ramin M. [Department of Radiation Oncology, UTSouthwestern Medical Center, Dallas, Texas 75390 (United States)

2008-11-15

Derivation of formulas relating leaf speeds and beam dose rates for delivering planned intensity profiles to static and moving targets in dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is presented. The analysis of equations determining algorithms for DMLC IMRT delivery under a variable beam dose rate reveals a multitude of possible delivery strategies for a given intensity map and for any given target motion patterns. From among all equivalent delivery strategies for DMLC IMRT treatments specific subclasses of strategies can be selected to provide deliveries that are particularly suitable for clinical applications providing existing delivery devices are used. Special attention is devoted to the subclass of beam dose rate variable DMLC delivery strategies to moving body anatomy that generalize existing techniques of such deliveries in Varian DMLC irradiation methodology to static body anatomy. Few examples of deliveries from this subclass of DMLC IMRT irradiations are investigated to illustrate the principle and show practical benefits of proposed techniques.

Wide Variability in Emergency Physician Admission Rates: A Target to Reduce Costs Without Compromising Quality

Directory of Open Access Journals (Sweden)

Jeffrey J. Guterman

2016-09-01

Full Text Available Introduction: Attending physician judgment is the traditional standard of care for emergency department (ED admission decisions. The extent to which variability in admission decisions affect cost and quality is not well understood. We sought to determine the impact of variability in admission decisions on cost and quality. Methods: We performed a retrospective observational study of patients presenting to a university-affiliated, urban ED from October 1, 2007, through September 30, 2008. The main outcome measures were admission rate, fiscal indicators (Medicaid-denied payment days, and quality indicators (15- and 30-day ED returns; delayed hospital admissions. We asked each Attending to estimate their inpatient admission rate and correlated their personal assessment with actual admission rates. Results: Admission rates, even after adjusting for known confounders, were highly variable (15.2%-32.0% and correlated with Medicaid denied-payment day rates (p=0.038. There was no correlation with quality outcome measures (30-day ED return or delayed hospital admission. There was no significant correlation between actual and self-described admission rate; the range of mis-estimation was 0% to 117%. Conclusion: Emergency medicine attending admission rates at this institution are highly variable, unexplained by known confounding variables, and unrelated to quality of care, as measured by 30-day ED return or delayed hospital admission. Admission optimization represents an important untapped potential for cost reduction through avoidable hospitalizations, with no apparent adverse effects on quality.

The role of socio-cognitive variables in predicting learning satisfaction in smart schools

Directory of Open Access Journals (Sweden)

Mohammad Reza Firoozi

2017-03-01

Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

Directory of Open Access Journals (Sweden)

Mohammad Reza FIROOZI

2017-03-01

Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

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

International Nuclear Information System (INIS)

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

2009-01-01

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 2 were used to evaluate performance 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 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.

Harnessing atomistic simulations to predict the rate at which dislocations overcome obstacles

Science.gov (United States)

Saroukhani, S.; Nguyen, L. D.; Leung, K. W. K.; Singh, C. V.; Warner, D. H.

2016-05-01

Predicting the rate at which dislocations overcome obstacles is key to understanding the microscopic features that govern the plastic flow of modern alloys. In this spirit, the current manuscript examines the rate at which an edge dislocation overcomes an obstacle in aluminum. Predictions were made using different popular variants of Harmonic Transition State Theory (HTST) and compared to those of direct Molecular Dynamics (MD) simulations. The HTST predictions were found to be grossly inaccurate due to the large entropy barrier associated with the dislocation-obstacle interaction. Considering the importance of finite temperature effects, the utility of the Finite Temperature String (FTS) method was then explored. While this approach was found capable of identifying a prominent reaction tube, it was not capable of computing the free energy profile along the tube. Lastly, the utility of the Transition Interface Sampling (TIS) approach was explored, which does not need a free energy profile and is known to be less reliant on the choice of reaction coordinate. The TIS approach was found capable of accurately predicting the rate, relative to direct MD simulations. This finding was utilized to examine the temperature and load dependence of the dislocation-obstacle interaction in a simple periodic cell configuration. An attractive rate prediction approach combining TST and simple continuum models is identified, and the strain rate sensitivity of individual dislocation obstacle interactions is predicted.

Heart rate variability changes in physicians working on night call.

Science.gov (United States)

Malmberg, Birgitta; Persson, Roger; Flisberg, Per; Ørbaek, Palle

2011-03-01

Adverse effects by night-call duty have become an important occupational health issue. The aim of this study was to investigate whether the heart rate variability (HRV) differed during recovery from day work and night-call duty between distinct physician specialities. We studied the impact of a 16-h night-call duty on autonomic balance, measured by HRV, among two physician groups differing with respect to having to deal with life-threatening conditions while on call. Nineteen anaesthesiologists (ANEST) and 16 paediatricians and ear, nose and throat surgeons (PENT) were monitored by ambulatory digital Holter electrocardiogram (ECG). Heart rate variability was analysed between 21:00 and 22:00 after an ordinary workday, on night call and in the evening post-call. Absolute and normalized high-frequency power (HF, HFnu) were the main outcome variables, expressing parasympathetic influence on the heart. ANEST had lower HF power than PENT while on night call and post-daytime work (p work compared with post-night-call duty (p balance and did not differ between specialities. However, the less dynamic HRV after daytime work and during night-call duty in the ANEST group may indicate a higher physiological stress level. These results may contribute to the improvement of night-call schedules within the health care sector.

Predicting Atomic Decay Rates Using an Informational-Entropic Approach

Science.gov (United States)

Gleiser, Marcelo; Jiang, Nan

2018-06-01

We show that a newly proposed Shannon-like entropic measure of shape complexity applicable to spatially-localized or periodic mathematical functions known as configurational entropy (CE) can be used as a predictor of spontaneous decay rates for one-electron atoms. The CE is constructed from the Fourier transform of the atomic probability density. For the hydrogen atom with degenerate states labeled with the principal quantum number n, we obtain a scaling law relating the n-averaged decay rates to the respective CE. The scaling law allows us to predict the n-averaged decay rate without relying on the traditional computation of dipole matrix elements. We tested the predictive power of our approach up to n = 20, obtaining an accuracy better than 3.7% within our numerical precision, as compared to spontaneous decay tables listed in the literature.

Predicting Atomic Decay Rates Using an Informational-Entropic Approach

Science.gov (United States)

Gleiser, Marcelo; Jiang, Nan

2018-02-01

We show that a newly proposed Shannon-like entropic measure of shape complexity applicable to spatially-localized or periodic mathematical functions known as configurational entropy (CE) can be used as a predictor of spontaneous decay rates for one-electron atoms. The CE is constructed from the Fourier transform of the atomic probability density. For the hydrogen atom with degenerate states labeled with the principal quantum number n, we obtain a scaling law relating the n-averaged decay rates to the respective CE. The scaling law allows us to predict the n-averaged decay rate without relying on the traditional computation of dipole matrix elements. We tested the predictive power of our approach up to n = 20, obtaining an accuracy better than 3.7% within our numerical precision, as compared to spontaneous decay tables listed in the literature.

[Prediction of mathematics achievement: effect of personal, socioeducational and contextual variables].

Science.gov (United States)

Rosário, Pedro; Lourenço, Abílio; Paiva, Olímpia; Rodrigues, Adriana; Valle, Antonio; Tuero-Herrero, Ellián

2012-05-01

Based upon the self-regulated learning theoretical framework this study examined to what extent students' Math school achievement (fifth to ninth graders from compulsory education) can be explained by different cognitive-motivational, social, educational, and contextual variables. A sample of 571 students (10 to 15 year old) enrolled in the study. Findings suggest that Math achievement can be predicted by self-efficacy in Math, school success and self-regulated learning and that these same variables can be explained by other motivational (ej., achievement goals) and contextual variables (school disruption) stressing this way the main importance of self-regulated learning processes and the role context can play in the promotion of school success. The educational implications of the results to the school levels taken are also discussed in the present paper.

Neonatal heart rate prediction.

Science.gov (United States)

Abdel-Rahman, Yumna; Jeremic, Aleksander; Tan, Kenneth

2009-01-01

Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.

[Sports medical aspects in cardiac risk stratification--heart rate variability and exercise capacity].

Science.gov (United States)

Banzer, W; Lucki, K; Bürklein, M; Rosenhagen, A; Vogt, L

2006-12-01

The present study investigates the association of the predicted CHD-risk (PROCAM) with the individual endurance capacity and heart rate variability (HRV) in a population-based sample of sedentary elderly. After stratification, in 57 men (48.1+/-9.5 yrs.) with an overall PROCAM-risk or =10% (50.8+/-5.6 points) cycle ergometries and short-term HRV analysis of time (RRMEAN, SDNN, RMSSD) and frequency domain parameters (LF, HF, TP, LF/HF) were conducted. Additionally the autonomic stress index (SI) was calculated. Nonparametric tests were used for statistical correlation analysis (Spearman rho) and group comparisons (Mann-Whitney). For endurance capacity [W/kg] (r=-0.469, pHRV analysis in risk stratification and outline the interrelation of a decreased exercise capacity and autonomic function with a raised individual 10-year cardiac risk. As an independent parameter of the vegetative regulatory state the stress index may contribute to an increased practical relevance of short-time HRV analysis.

The impact of macroeconomic variables on the evolution of the credit risk rate

Directory of Open Access Journals (Sweden)

Luminița Gabriela Istrate

2018-03-01

Full Text Available The dynamics of the real economy is a major driver of the evolution of arrears at the level of the pool of loans granted to non-financial companies, completed by the financial pressure induced by the monetary conditions. Lending allows on the one hand providing resources for companies that need financing for investment projects, on the other hand, it supports the fund holders to place resources for obtaining profit. The role of the lending policy in the activity of commercial banks is very important, as it may influence both the cost of credits and the loan portfolio quality in the future. The purpose of this research is to find the macroeconomic variables that significantly influence credit risk and to develop a statistical model for predicting the doubtful and non-performing loans rate. Thus, it is envisaged the research of mechanisms by which the dynamics of the real economy and the money market conditions influence the evolution of the credit risk in different business sectors.

Dynamics of a seismogenic fault subject to variable strain rate

OpenAIRE

M. Dragoni; A. Piombo

2011-01-01

The behaviour of seismogenic faults is generally investigated under the assumption that they are subject to a constant strain rate. We consider the effect of a slowly variable strain rate on the recurrence times of earthquakes generated by a single fault. To this aim a spring-block system is employed as a low-order analog of the fault. Two cases are considered: a sinusoidal oscillation in the driver velocity and a monotonic change from one velocity value to another. In the f...

State-related differences in heart rate variability in bipolar disorder

DEFF Research Database (Denmark)

Faurholt-Jepsen, Maria; Brage, Søren; Kessing, Lars Vedel

2017-01-01

Heart rate variability (HRV) is a validated measure of sympato-vagal balance in the autonomic nervous system. HRV appears decreased in patients with bipolar disorder (BD) compared with healthy individuals, but the extent of state-related alterations has been sparingly investigated. The present...... bipolar disorder and could...

Combinatorial effect of nicotine and black tea on heart rate variability: Useful or harmful?

Science.gov (United States)

Joukar, S; Sheibani, M

2017-06-01

The effect of nicotine on heart rate variability (HRV) is controversial. Autonomic nervous system is the main regulator of heart rhythm, and heart rate variability is an appropriate index to assessment of the effects of the autonomic system on heart. In this study, the combination effect of nicotine and black tea consumption on sympatho-vagal balance and heart rate variability was investigated in rats. Male Wistar rats were randomized into four groups as control, tea (2.5 g/100 cc, daily), nicotine (2 mg/kg/d) and tea plus nicotine groups which treated for 28 days, and in the 29th day, their electrocardiograms (lead II) were recorded. The mean of high-frequency power (HF) in tea, nicotine and tea plus nicotine groups was significantly more than control group (P nicotine and tea + nicotine groups was significantly less than control group (P nicotine and tea + nicotine groups in comparison with control group (P nicotine or their combination with dosages used in this study can increase the heart rate variability and improve the sympatho-vagal balance in rat. © 2017 John Wiley & Sons Ltd.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

Science.gov (United States)

Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

2017-10-01

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

Word-serial Architectures for Filtering and Variable Rate Decimation

Directory of Open Access Journals (Sweden)

Eugene Grayver

2002-01-01

Full Text Available A new flexible architecture is proposed for word-serial filtering and variable rate decimation/interpolation. The architecture is targeted for low power applications requiring medium to low data rate and is ideally suited for implementation on either an ASIC or an FPGA. It combines the small size and low power of an ASIC with the programmability and flexibility of a DSP. An efficient memory addressing scheme eliminates the need for power hungry shift registers and allows full reconfiguration. The decimation ratio, filter length and filter coefficients can all be changed in real time. The architecture takes advantage of coefficient symmetries in linear phase filters and in polyphase components.

Predicting rates of inbreeding in populations undergoing selection

NARCIS (Netherlands)

Woolliams, J.A.; Bijma, P.

2000-01-01

Tractable forms of predicting rates of inbreeding (F) in selected populations with general indices, nonrandom mating, and overlapping generations were developed, with the principal results assuming a period of equilibrium in the selection process. An existing theorem concerning the relationship

Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions

International Nuclear Information System (INIS)

Gil, E; Orini, M; Bailón, R; Laguna, P; Vergara, J M; Mainardi, L

2010-01-01

In this paper we assessed the possibility of using the pulse rate variability (PRV) extracted from the photoplethysmography signal as an alternative measurement of the HRV signal in non-stationary conditions. The study is based on analysis of the changes observed during a tilt table test in the heart rate modulation of 17 young subjects. First, the classical indices of HRV analysis were compared to the indices from PRV in intervals where stationarity was assumed. Second, the time-varying spectral properties of both signals were compared by time-frequency (TF) and TF coherence analysis. Third, the effect of replacing PRV with HRV in the assessment of the changes of the autonomic modulation of the heart rate was considered. Time-invariant HRV and PRV indices showed no statistically significant differences (p > 0.05) and high correlation (>0.97). Time-frequency analysis revealed that the TF spectra of both signals were highly correlated (0.99 ± 0.01); the difference between the instantaneous power, in the LF and HF bands, obtained from HRV and PRV was small (<10 −3 s −2 ) and their temporal patterns were highly correlated (0.98 ± 0.04 and 0.95 ± 0.06 in the LF and HF bands, respectively) and TF coherence in the LF and HF bands was high (0.97 ± 0.04 and 0.89 ± 0.08, respectively). Finally, the instantaneous power in the LF band was observed to significantly increase during head-up tilt by both HRV and PRV analysis. These results suggest that although some differences in the time-varying spectral indices extracted from HRV and PRV exist, mainly in the HF band associated with respiration, PRV could be used as a surrogate of HRV during non-stationary conditions, at least during the tilt table test

Heart rate and flow velocity variability as determined from umbilical Doppler velocimetry at 10-20 weeks of gestation.

Science.gov (United States)

Ursem, N T; Struijk, P C; Hop, W C; Clark, E B; Keller, B B; Wladimiroff, J W

1998-11-01

1. The aim of this study was to define from umbilical artery flow velocity waveforms absolute peak systolic and time-averaged velocity, fetal heart rate, fetal heart rate variability and flow velocity variability, and the relation between fetal heart rate and velocity variables in early pregnancy.2.A total of 108 women presenting with a normal pregnancy from 10 to 20 weeks of gestation consented to participate in a cross-sectional study design. Doppler ultrasound recordings were made from the free-floating loop of the umbilical cord.3. Umbilical artery peak systolic and time-averaged velocity increased at 10-20 weeks, whereas fetal heart rate decreased at 10-15 weeks of gestation and plateaued thereafter. Umbilical artery peak systolic velocity variability and fetal heart rate variability increased at 10-20 and 15-20 weeks respectively.4. The inverse relationship between umbilical artery flow velocity and fetal heart rate at 10-15 weeks of gestation suggests that the Frank-Starling mechanism regulates cardiovascular control as early as the late first and early second trimesters of pregnancy. A different underlying mechanism is suggested for the observed variability profiles in heart rate and umbilical artery peak systolic velocity. It is speculated that heart rate variability is mediated by maturation of the parasympathetic nervous system, whereas peak systolic velocity variability reflects the activation of a haemodynamic feedback mechanism.

Modeling and predicting historical volatility in exchange rate markets

Science.gov (United States)

Lahmiri, Salim

2017-04-01

Volatility modeling and forecasting of currency exchange rate is an important task in several business risk management tasks; including treasury risk management, derivatives pricing, and portfolio risk evaluation. The purpose of this study is to present a simple and effective approach for predicting historical volatility of currency exchange rate. The approach is based on a limited set of technical indicators as inputs to the artificial neural networks (ANN). To show the effectiveness of the proposed approach, it was applied to forecast US/Canada and US/Euro exchange rates volatilities. The forecasting results show that our simple approach outperformed the conventional GARCH and EGARCH with different distribution assumptions, and also the hybrid GARCH and EGARCH with ANN in terms of mean absolute error, mean of squared errors, and Theil's inequality coefficient. Because of the simplicity and effectiveness of the approach, it is promising for US currency volatility prediction tasks.

Prediction of interest rate using CKLS model with stochastic parameters

International Nuclear Information System (INIS)

Ying, Khor Chia; Hin, Pooi Ah

2014-01-01

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ (j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ (j) , we assume that φ (j) depends on φ (j−m) , φ (j−m+1) ,…, φ (j−1) and the interest rate r j+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r j+n+1 of the interest rate at the next time point when the value r j+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r j+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters

Prediction of interest rate using CKLS model with stochastic parameters

Energy Technology Data Exchange (ETDEWEB)

Ying, Khor Chia [Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Selangor (Malaysia); Hin, Pooi Ah [Sunway University Business School, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang Jaya, Selangor (Malaysia)

2014-06-19

The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.

Validation of Heart Rate Monitor Polar RS800 for Heart Rate Variability Analysis During Exercise.

Science.gov (United States)

Hernando, David; Garatachea, Nuria; Almeida, Rute; Casajús, Jose A; Bailón, Raquel

2018-03-01

Hernando, D, Garatachea, N, Almeida, R, Casajús, JA, and Bailón, R. Validation of heart rate monitor Polar RS800 for heart rate variability analysis during exercise. J Strength Cond Res 32(3): 716-725, 2018-Heart rate variability (HRV) analysis during exercise is an interesting noninvasive tool to measure the cardiovascular response to the stress of exercise. Wearable heart rate monitors are a comfortable option to measure interbeat (RR) intervals while doing physical activities. It is necessary to evaluate the agreement between HRV parameters derived from the RR series recorded by wearable devices and those derived from an electrocardiogram (ECG) during dynamic exercise of low to high intensity. Twenty-three male volunteers performed an exercise stress test on a cycle ergometer. Subjects wore a Polar RS800 device, whereas ECG was also recorded simultaneously to extract the reference RR intervals. A time-frequency spectral analysis was performed to extract the instantaneous mean heart rate (HRM), and the power of low-frequency (PLF) and high-frequency (PHF) components, the latter centered on the respiratory frequency. Analysis was done in intervals of different exercise intensity based on oxygen consumption. Linear correlation, reliability, and agreement were computed in each interval. The agreement between the RR series obtained from the Polar device and from the ECG is high throughout the whole test although the shorter the RR is, the more differences there are. Both methods are interchangeable when analyzing HRV at rest. At high exercise intensity, HRM and PLF still presented a high correlation (ρ > 0.8) and excellent reliability and agreement indices (above 0.9). However, the PHF measurements from the Polar showed reliability and agreement coefficients around 0.5 or lower when the level of the exercise increases (for levels of O2 above 60%).

Achievable Performance of Zero-Delay Variable-Rate Coding in Rate-Constrained Networked Control Systems with Channel Delay

DEFF Research Database (Denmark)

Barforooshan, Mohsen; Østergaard, Jan; Stavrou, Fotios

2017-01-01

This paper presents an upper bound on the minimum data rate required to achieve a prescribed closed-loop performance level in networked control systems (NCSs). The considered feedback loop includes a linear time-invariant (LTI) plant with single measurement output and single control input. Moreover......, in this NCS, a causal but otherwise unconstrained feedback system carries out zero-delay variable-rate coding, and control. Between the encoder and decoder, data is exchanged over a rate-limited noiseless digital channel with a known constant time delay. Here we propose a linear source-coding scheme...

Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different

Directory of Open Access Journals (Sweden)

Keisuke Yano

2014-05-01

Full Text Available We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the Fisher information matrix for the data and the Fisher information matrix for the target variables. We assume that the trace has a unique maximum point with respect to the parameter. We construct asymptotically constant-risk Bayesian predictive densities using a prior depending on the sample size. Further, we apply the theory to the subminimax estimator problem and the prediction based on the binary regression model.

Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

Science.gov (United States)

Ertekin, Erhan

2017-01-01

This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

Predictive Value of Beat-to-Beat QT Variability Index across the Continuum of Left Ventricular Dysfunction: Competing Risks of Non-cardiac or Cardiovascular Death, and Sudden or Non-Sudden Cardiac Death

Science.gov (United States)

Tereshchenko, Larisa G.; Cygankiewicz, Iwona; McNitt, Scott; Vazquez, Rafael; Bayes-Genis, Antoni; Han, Lichy; Sur, Sanjoli; Couderc, Jean-Philippe; Berger, Ronald D.; de Luna, Antoni Bayes; Zareba, Wojciech

2012-01-01

Background The goal of this study was to determine the predictive value of beat-to-beat QT variability in heart failure (HF) patients across the continuum of left ventricular dysfunction. Methods and Results Beat-to-beat QT variability index (QTVI), heart rate variance (LogHRV), normalized QT variance (QTVN), and coherence between heart rate variability and QT variability have been measured at rest during sinus rhythm in 533 participants of the Muerte Subita en Insuficiencia Cardiaca (MUSIC) HF study (mean age 63.1±11.7; males 70.6%; LVEF >35% in 254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance (IDEAL) database. During a median of 3.7 years of follow-up, 116 patients died, 52 from sudden cardiac death (SCD). In multivariate competing risk analyses, the highest QTVI quartile was associated with cardiovascular death [hazard ratio (HR) 1.67(95%CI 1.14-2.47), P=0.009] and in particular with non-sudden cardiac death [HR 2.91(1.69-5.01), P<0.001]. Elevated QTVI separated 97.5% of healthy individuals from subjects at risk for cardiovascular [HR 1.57(1.04-2.35), P=0.031], and non-sudden cardiac death in multivariate competing risk model [HR 2.58(1.13-3.78), P=0.001]. No interaction between QTVI and LVEF was found. QTVI predicted neither non-cardiac death (P=0.546) nor SCD (P=0.945). Decreased heart rate variability (HRV) rather than increased QT variability was the reason for increased QTVI in this study. Conclusions Increased QTVI due to depressed HRV predicts cardiovascular mortality and non-sudden cardiac death, but neither SCD nor excracardiac mortality in HF across the continuum of left ventricular dysfunction. Abnormally augmented QTVI separates 97.5% of healthy individuals from HF patients at risk. PMID:22730411

A new constitutive model for prediction of impact rates response of polypropylene

Directory of Open Access Journals (Sweden)

Buckley C.P.

2012-08-01

Full Text Available This paper proposes a new constitutive model for predicting the impact rates response of polypropylene. Impact rates, as used here, refer to strain rates greater than 1000 1/s. The model is a physically based, three-dimensional constitutive model which incorporates the contributions of the amorphous, crystalline, pseudo-amorphous and entanglement networks to the constitutive response of polypropylene. The model mathematics is based on the well-known Glass-Rubber model originally developed for glassy polymers but the arguments have herein been extended to semi-crystalline polymers. In order to predict the impact rates behaviour of polypropylene, the model exploits the well-known framework of multiple processes yielding of polymers. This work argues that two dominant viscoelastic relaxation processes – the alpha- and beta-processes – can be associated with the yield responses of polypropylene observed at low-rate-dominant and impact-rates dominant loading regimes. Compression test data on polypropylene have been used to validate the model. The study has found that the model predicts quite well the experimentally observed nonlinear rate-dependent impact response of polypropylene.

Kinetics of protein–ligand unbinding: Predicting pathways, rates, and rate-limiting steps

Science.gov (United States)

Tiwary, Pratyush; Limongelli, Vittorio; Salvalaglio, Matteo; Parrinello, Michele

2015-01-01

The ability to predict the mechanisms and the associated rate constants of protein–ligand unbinding is of great practical importance in drug design. In this work we demonstrate how a recently introduced metadynamics-based approach allows exploration of the unbinding pathways, estimation of the rates, and determination of the rate-limiting steps in the paradigmatic case of the trypsin–benzamidine system. Protein, ligand, and solvent are described with full atomic resolution. Using metadynamics, multiple unbinding trajectories that start with the ligand in the crystallographic binding pose and end with the ligand in the fully solvated state are generated. The unbinding rate koff is computed from the mean residence time of the ligand. Using our previously computed binding affinity we also obtain the binding rate kon. Both rates are in agreement with reported experimental values. We uncover the complex pathways of unbinding trajectories and describe the critical rate-limiting steps with unprecedented detail. Our findings illuminate the role played by the coupling between subtle protein backbone fluctuations and the solvation by water molecules that enter the binding pocket and assist in the breaking of the shielded hydrogen bonds. We expect our approach to be useful in calculating rates for general protein–ligand systems and a valid support for drug design. PMID:25605901

A quantitative prediction model of SCC rate for nuclear structure materials in high temperature water based on crack tip creep strain rate

International Nuclear Information System (INIS)

Yang, F.Q.; Xue, H.; Zhao, L.Y.; Fang, X.R.

2014-01-01

Highlights: • Creep is considered to be the primary mechanical factor of crack tip film degradation. • The prediction model of SCC rate is based on crack tip creep strain rate. • The SCC rate calculated at the secondary stage of creep is recommended. • The effect of stress intensity factor on SCC growth rate is discussed. - Abstract: The quantitative prediction of stress corrosion cracking (SCC) of structure materials is essential in safety assessment of nuclear power plants. A new quantitative prediction model is proposed by combining the Ford–Andresen model, a crack tip creep model and an elastic–plastic finite element method. The creep at the crack tip is considered to be the primary mechanical factor of protective film degradation, and the creep strain rate at the crack tip is suggested as primary mechanical factor in predicting the SCC rate. The SCC rates at secondary stage of creep are recommended when using the approach introduced in this study to predict the SCC rates of materials in high temperature water. The proposed approach can be used to understand the SCC crack growth in structural materials of light water reactors

Stochastic Prediction of Ventilation System Performance

DEFF Research Database (Denmark)

Haghighat, F.; Brohus, Henrik; Frier, Christian

The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables and param......The paper briefly reviews the existing techniques for predicting the airflow rate due to the random nature of forcing functions, e.g. wind speed. The effort is to establish the relationship between the statistics of the output of a system and the statistics of the random input variables...

Prediction of subjective ratings of emotional pictures by EEG features

Science.gov (United States)

McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

2017-02-01

Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

Removing batch effects for prediction problems with frozen surrogate variable analysis

Directory of Open Access Journals (Sweden)

Hilary S. Parker

2014-09-01

Full Text Available Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

Heart rate variability in normal-weight patients with polycystic ovary syndrome

OpenAIRE

Kilit, Celal; Kilit, T?rkan Pa?al?

2017-01-01

Objective: Polycystic ovary syndrome (PCOS) is an endocrine disease closely related to several risk factors of cardiovascular disease. Obese women with PCOS show altered autonomic modulation. The results of studies investigating cardiac autonomic functions of normal-weight women with PCOS are conflicting. The aim of the study was to assess the reactivity of cardiac sympathovagal balance in normal-weight women with PCOS by heart rate variability analysis. Methods: We examined the heart rate va...

How to predict a high rate of inappropriateness for upper endoscopy in an endoscopic centre?

Science.gov (United States)

Buri, L; Bersani, G; Hassan, C; Anti, M; Bianco, M A; Cipolletta, L; Di Giulio, E; Di Matteo, G; Familiari, L; Ficano, L; Loriga, P; Morini, S; Pietropaolo, V; Zambelli, A; Grossi, E; Intraligi, M; Tessari, F; Buscema, M

2010-09-01

Inappropriateness of upper endoscopy (EGD) indication causes decreased diagnostic yield. Our aim of was to identify predictors of appropriateness rate for EGD among endoscopic centres. A post-hoc analysis of two multicentre cross-sectional studies, including 6270 and 8252 patients consecutively referred to EGD in 44 (group A) and 55 (group B) endoscopic Italian centres in 2003 and 2007, respectively, was performed. A multiple forward stepwise regression was applied to group A, and independently validated in group B. A <70% threshold was adopted to define inadequate appropriateness rate clustered by centre. discrete variability of clustered appropriateness rates among the 44 group A centres was observed (median: 77%; range: 41-97%), and a <70% appropriateness rate was detected in 11 (25%). Independent predictors of centre appropriateness rate were: percentage of patients referred by general practitioners (GP), rate of urgent examinations, prevalence of relevant diseases, and academic status. For group B, sensitivity, specificity and area under receiver operating characteristic curve of the model in detecting centres with a <70% appropriateness rate were 54%, 93% and 0.72, respectively. A simple predictive rule, based on rate of patients referred by GPs, rate of urgent examinations, prevalence of relevant diseases and academic status, identified a small subset of centres characterised by a high rate of inappropriateness. These centres may be presumed to obtain the largest benefit from targeted educational programs. Copyright (c) 2010 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

What Predicts Method Effects in Child Behavior Ratings

Science.gov (United States)

Low, Justin A.; Keith, Timothy Z.; Jensen, Megan

2015-01-01

The purpose of this research was to determine whether child, parent, and teacher characteristics such as sex, socioeconomic status (SES), parental depressive symptoms, the number of years of teaching experience, number of children in the classroom, and teachers' disciplinary self-efficacy predict deviations from maternal ratings in a…

Performance evaluation of a newly developed variable rate sprayer for nursery liner applications

Science.gov (United States)

An experimental variable-rate sprayer designed for liner applications was tested by comparing its spray deposit, coverage, and droplet density inside canopies of six nursery liner varieties with constant-rate applications. Spray samplers, including water sensitive papers (WSP) and nylon screens, wer...

Resting and postexercise heart rate variability in professional handball players.

Science.gov (United States)

Kayacan, Yildirim; Yildiz, Sedat

2016-03-01

The aim of this study was to evaluate heart rate variability (HRV) in professional handball players during rest and following a 5 min mild jogging exercise. For that purpose, electrocardiogram (ECG) of male handball players (N.=12, mean age 25±3.95 years) and sedentary controls (N.=14, mean age 23.5±2.95 years) were recorded for 5 min at rest and just after 5 min of mild jogging. ECGs were recorded and following HRV parameters were calculated: time-domain variables such as heart rate (HR), average normal-to-normal RR intervals, standard deviation of normal-to-normal RR intervals, square root of the mean of the squares of differences between adjacent NN intervals, percentage of differences between adjacent NN intervals that are greater than 50 milliseconds (pNN50), and frequency-domain variables such as very low frequency, low (LF) and high frequency (HF) of the power and LF/HF ratio. Unpaired t-test was used to find out differences among groups while paired t-test was used for comparison of each group for pre- and postjogging HRV. Pearson correlations were carried out to find out the relationships between the parameters. Blood pressures were not different between handball players and sedentary controls but exercise increased systolic blood pressure (Phandball players (Phandball players (Phandball players in response to a mild, short-time (5 min) jogging exercise. However, in sedentary subjects, either the sympathetic regulation of the autonomous nervous system increased or vagal withdrawal occurred.

Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems.

Science.gov (United States)

Vucetich, John A; Hebblewhite, Mark; Smith, Douglas W; Peterson, Rolf O

2011-11-01

1. Predation rate (PR) and kill rate are both fundamental statistics for understanding predation. However, relatively little is known about how these statistics relate to one another and how they relate to prey population dynamics. We assess these relationships across three systems where wolf-prey dynamics have been observed for 41 years (Isle Royale), 19 years (Banff) and 12 years (Yellowstone). 2. To provide context for this empirical assessment, we developed theoretical predictions of the relationship between kill rate and PR under a broad range of predator-prey models including predator-dependent, ratio-dependent and Lotka-Volterra dynamics. 3. The theoretical predictions indicate that kill rate can be related to PR in a variety of diverse ways (e.g. positive, negative, unrelated) that depend on the nature of predator-prey dynamics (e.g. structure of the functional response). These simulations also suggested that the ratio of predator-to-prey is a good predictor of prey growth rate. That result motivated us to assess the empirical relationship between the ratio and prey growth rate for each of the three study sites. 4. The empirical relationships indicate that PR is not well predicted by kill rate, but is better predicted by the ratio of predator-to-prey. Kill rate is also a poor predictor of prey growth rate. However, PR and ratio of predator-to-prey each explained significant portions of variation in prey growth rate for two of the three study sites. 5. Our analyses offer two general insights. First, Isle Royale, Banff and Yellowstone are similar insomuch as they all include wolves preying on large ungulates. However, they also differ in species diversity of predator and prey communities, exploitation by humans and the role of dispersal. Even with the benefit of our analysis, it remains difficult to judge whether to be more impressed by the similarities or differences. This difficulty nicely illustrates a fundamental property of ecological

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

Science.gov (United States)

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

Protein construct storage: Bayesian variable selection and prediction with mixtures.

Science.gov (United States)

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Do physiological and pathological stresses produce different changes in heart rate variability?

Directory of Open Access Journals (Sweden)

Andrea eBravi

2013-07-01

Full Text Available Although physiological (e.g. exercise and pathological (e.g. infection stress affecting the cardiovascular system have both been documented to be associated with a reduction in overall heart rate variability (HRV, it remains unclear if loss of HRV is ubiquitously similar across different domains of variability analysis or if distinct patterns of altered HRV exist depending on the stressor. Using Continuous Individualized Multiorgan Variability Analysis (CIMVATM software, heart rate (HR and four selected measures of variability were measured over time (windowed analysis from two datasets, a set (n=13 of patients who developed systemic infection (i.e. sepsis after bone marrow transplant, and a matched set of healthy subjects undergoing physical exercise under controlled conditions. HR and the four HRV measures showed similar trends in both sepsis and exercise. The comparison through Wilcoxon sign-rank test of the levels of variability at baseline and during the stress (i.e. exercise or after days of sepsis development showed similar changes, except for LF/HF, ratio of power at low and high frequencies (associated with sympathovagal modulation, which was affected by exercise but did not show any change during sepsis. Furthermore, HRV measures during sepsis showed a lower level of correlation with each other, as compared to HRV during exercise. In conclusion, this exploratory study highlights similar responses during both exercise and infection, with differences in terms of correlation and inter-subject fluctuations, whose physiologic significance merits further investigation.

Precise predictions for inclusive semi-tauonic B decay rate

Energy Technology Data Exchange (ETDEWEB)

Mannel, Thomas; Shahriaran, Farnoush [University of Siegen (Germany)

2016-07-01

We get Standard Model prediction for the decay rate of B → X{sub c}τν transitions. The triple differential decay rate has been derived including the nonperturbative corrections of order Λ{sub QCD}{sup 3}/m{sub b}{sup 3} and the leading O(α{sub s}) corrections. The total decay width is obtained by numerical integration with an estimated uncertainty of roughly 5%. We compare our result to the sum of the rates of the exclusive B → Dτν, B → D*τν and B → D**τν decays.

High Altitude Affects Nocturnal Non-linear Heart Rate Variability: PATCH-HA Study

Directory of Open Access Journals (Sweden)

Christopher J. Boos

2018-04-01

Full Text Available Background: High altitude (HA exposure can lead to changes in resting heart rate variability (HRV, which may be linked to acute mountain sickness (AMS development. Compared with traditional HRV measures, non-linear HRV appears to offer incremental and prognostic data, yet its utility and relationship to AMS have been barely examined at HA. This study sought to examine this relationship at terrestrial HA.Methods: Sixteen healthy British military servicemen were studied at baseline (800 m, first night and over eight consecutive nights, at a sleeping altitude of up to 3600 m. A disposable cardiac patch monitor was used, to record the nocturnal cardiac inter-beat interval data, over 1 h (0200–0300 h, for offline HRV assessment. Non-linear HRV measures included Sample entropy (SampEn, the short (α1, 4–12 beats and long-term (α2, 13–64 beats detrend fluctuation analysis slope and the correlation dimension (D2. The maximal rating of perceived exertion (RPE, during daily exercise, was assessed using the Borg 6–20 RPE scale.Results: All subjects completed the HA exposure. The average age of included subjects was 31.4 ± 8.1 years. HA led to a significant fall in SpO2 and increase in heart rate, LLS and RPE. There were no significant changes in the ECG-derived respiratory rate or in any of the time domain measures of HRV during sleep. The only notable changes in frequency domain measures of HRV were an increase in LF and fall in HFnu power at the highest altitude. Conversely, SampEn, SD1/SD2 and D2 all fell, whereas α1 and α2 increased (p < 0.05. RPE inversely correlated with SD1/SD2 (r = -0.31; p = 0.002, SampEn (r = -0.22; p = 0.03, HFnu (r = -0.27; p = 0.007 and positively correlated with LF (r = 0.24; p = 0.02, LF/HF (r = 0.24; p = 0.02, α1 (r = 0.32; p = 0.002 and α2 (r = 0.21; p = 0.04. AMS occurred in 7/16 subjects (43.8% and was very mild in 85.7% of cases. HRV failed to predict AMS.Conclusion: Non-linear HRV is more sensitive to the

DISORDERS OF THE AUTONOMIC NERVOUS SYSTEM IN THE CARDIOLOGY PRACTICE: FOCUS ON THE ANALYSIS OF HEART RATE VARIABILITY

Directory of Open Access Journals (Sweden)

E. B. Akhmedova

2015-09-01

Full Text Available Heart rate variability (HRV in patients with ischemic heart disease, a life-threatening heart rhythm disorders, as well as diabetes mellitus (DM is considered. A significant association between the autonomic regulation of the cardiovascular system and death from cardiovascular causes is identified. The reactions of the autonomic nervous system (ANS can serve as a precipitating factor of arrhythmias in patients with heart disorders. Analysis of HRV at rest is the main and informative method for determination of the ANS disorders. HRV decreases greatly in patients with acute myocardial infarction, cardiac arrhythmia, and DM, predicting a high risk of death. The leading cause of death in diabetic patients is cardiac autonomic neuropathy, with the development of "silent" ischemia and painless myocardial infarction. Autonomic regulation of the heart rate should be assessed for early diagnosis and prevention of complications in the form of sudden death.

Buccal telomere length and its associations with cortisol, heart rate variability, heart rate, and blood pressure responses to an acute social evaluative stressor in college students.

Science.gov (United States)

Woody, Alex; Hamilton, Katrina; Livitz, Irina E; Figueroa, Wilson S; Zoccola, Peggy M

2017-05-01

Understanding the relationship between stress and telomere length (a marker of cellular aging) is of great interest for reducing aging-related disease and death. One important aspect of acute stress exposure that may underlie detrimental effects on health is physiological reactivity to the stressor. This study tested the relationship between buccal telomere length and physiological reactivity (salivary cortisol reactivity and total output, heart rate (HR) variability, blood pressure, and HR) to an acute psychosocial stressor in a sample of 77 (53% male) healthy young adults. Consistent with predictions, greater reductions in HR variability (HRV) in response to a stressor and greater cortisol output during the study session were associated with shorter relative buccal telomere length (i.e. greater cellular aging). However, the relationship between cortisol output and buccal telomere length became non-significant when adjusting for medication use. Contrary to past findings and study hypotheses, associations between cortisol, blood pressure, and HR reactivity and relative buccal telomere length were not significant. Overall, these findings may indicate there are limited and mixed associations between stress reactivity and telomere length across physiological systems.

US Intergroup Anal Carcinoma Trial: Tumor Diameter Predicts for Colostomy

Science.gov (United States)

Ajani, Jaffer A.; Winter, Kathryn A.; Gunderson, Leonard L.; Pedersen, John; Benson, Al B.; Thomas, Charles R.; Mayer, Robert J.; Haddock, Michael G.; Rich, Tyvin A.; Willett, Christopher G.

2009-01-01

Purpose The US Gastrointestinal Intergroup Radiation Therapy Oncology Group 98-11 anal carcinoma trial showed that cisplatin-based concurrent chemoradiotherapy resulted in a significantly higher rate of colostomy compared with mitomycin-based therapy. Established prognostic variables for patients with anal carcinoma include tumor diameter, clinical nodal status, and sex, but pretreatment variables that would predict the likelihood of colostomy are unknown. Methods A secondary analysis was performed by combining patients in the two treatment arms to evaluate whether new predictive and prognostic variables would emerge. Univariate and multivariate analyses were carried out to correlate overall survival (OS), disease-free survival, and time to colostomy (TTC) with pretreatment and treatment variables. Results Of 682 patients enrolled, 644 patients were assessable and analyzed. In the multivariate analysis, tumor-related prognosticators for poorer OS included node-positive cancer (P ≤ .0001), large (> 5 cm) tumor diameter (P = .01), and male sex (P = .016). In the treatment-related categories, cisplatin-based therapy was statistically significantly associated with a higher rate of colostomy (P = .03) than was mitomycin-based therapy. In the pretreatment variables category, only large tumor diameter independently predicted for TTC (P = .008). Similarly, the cumulative 5-year colostomy rate was statistically significantly higher for large tumor diameter than for small tumor diameter (Gray's test; P = .0074). Clinical nodal status and sex were not predictive of TTC. Conclusion The combined analysis of the two arms of RTOG 98-11, representing the largest prospective database, reveals that tumor diameter (irrespective of the nodal status) is the only independent pretreatment variable that predicts TTC and 5-year colostomy rate in patients with anal carcinoma. PMID:19139424

Heart Rate Variability: Effect of Exercise Intensity on Postexercise Response

Science.gov (United States)

James, David V. B.; Munson, Steven C.; Maldonado-Martin, Sara; De Ste Croix, Mark B. A.

2012-01-01

The purpose of the present study was to investigate the influence of two exercise intensities (moderate and severe) on heart rate variability (HRV) response in 16 runners 1 hr prior to (-1 hr) and at +1 hr, +24 hr, +48 hr, and +72 hr following each exercise session. Time domain indexes and a high frequency component showed a significant decrease…

Bounds on Rates of Variable-Basis and Neural-Network Approximation

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2001-01-01

Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001

Heart rate variability analysis in acute poisoning by cholinesterase inhibitors

OpenAIRE

JEONG, JINWOO; KIM, YONGIN

2017-01-01

Heart rate variability (HRV) has been associated with a variety of clinical situations. However, few studies have examined the association between HRV and acute poisoning. Organophosphate (OP) and carbamate inhibit esterase enzymes, particularly acetylcholinesterase, resulting in an accumulation of acetylcholine and thereby promoting excessive activation of corresponding receptors. Because diagnosis and treatment of OP and carbamate poisoning greatly depend on...

Modeling Short-Range Soil Variability and its Potential Use in Variable-Rate Treatment of Experimental Plots

Directory of Open Access Journals (Sweden)

A Moameni

2011-02-01

Full Text Available Abstract In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics

Nonlinear analysis of heart rate variability in patients with eating disorders

NARCIS (Netherlands)

Vigo, Daniel E.; Castro, Mariana N.; Dorpinghaus, Andrea; Weidema, Hylke; Cardinali, Daniel P.; Siri, Leonardo Nicola; Rovira, Bernardo; Fahrer, Rodolfo D.; Nogues, Martin; Leiguarda, Ramon C.; Guinjoan, Salvador M.

2008-01-01

Patients with anorexia nervosa or bulimia nervosa often have signs of autonomic dysfunction potentially deleterious to the heart. The aim of this study was to ascertain the nonlinear properties of heart rate variability in patients with eating disorders. A group of 33 women with eating disorders (14

Prediction of the dollar to the ruble rate. A system-theoretic approach

Science.gov (United States)

Borodachev, Sergey M.

2017-07-01

Proposed a simple state-space model of dollar rate formation based on changes in oil prices and some mechanisms of money transfer between monetary and stock markets. Comparison of predictions by means of input-output model and state-space model is made. It concludes that with proper use of statistical data (Kalman filter) the second approach provides more adequate predictions of the dollar rate.

Impact of vegetation variability on potential predictability and skill of EC-Earth simulations

Energy Technology Data Exchange (ETDEWEB)

Weiss, Martina; Hurk, Bart van den; Haarsma, Reindert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), De Bilt (Netherlands)

2012-12-15

Climate models often use a simplified and static representation of vegetation characteristics to determine fluxes of energy, momentum and water vapour between surface and lower atmosphere. In order to analyse the impact of short term variability in vegetation phenology, we use remotely-sensed leaf area index and albedo products to examine the role of vegetation in the coupled land-atmosphere system. Perfect model experiments are carried out to determine the impact of realistic temporal variability of vegetation on potential predictability of evaporation and temperature, as well as model skill of EC-Earth simulations. The length of the simulation period is hereby limited by the availability of satellite products to 2000-2010. While a realistic representation of vegetation positively influences the simulation of evaporation and its potential predictability, a positive impact on 2 m temperature is of smaller magnitude, regionally confined and more pronounced in climatically extreme years. (orig.)

Parent Ratings of Impulsivity and Inhibition Predict State Testing Scores

Directory of Open Access Journals (Sweden)

Rebecca A. Lundwall

2018-03-01

Full Text Available One principle of cognitive development is that earlier intervention for educational difficulties tends to improve outcomes such as future educational and career success. One possible way to help students who struggle is to determine if they process information differently. Such determination might lead to clues for interventions. For example, early information processing requires attention before the information can be identified, encoded, and stored. The aim of the present study was to investigate whether parent ratings of inattention, inhibition, and impulsivity, and whether error rate on a reflexive attention task could be used to predict child scores on state standardized tests. Finding such an association could provide assistance to educators in identifying academically struggling children who might require targeted educational interventions. Children (N = 203 were invited to complete a peripheral cueing task (which measures the automatic reorienting of the brain’s attentional resources from one location to another. While the children completed the task, their parents completed a questionnaire. The questionnaire gathered information on broad indicators of child functioning, including observable behaviors of impulsivity, inattention, and inhibition, as well as state academic scores (which the parent retrieved online from their school. We used sequential regression to analyze contributions of error rate and parent-rated behaviors in predicting six academic scores. In one of the six analyses (for science, we found that the improvement was significant from the simplified model (with only family income, child age, and sex as predictors to the full model (adding error rate and three parent-rated behaviors. Two additional analyses (reading and social studies showed near significant improvement from simplified to full models. Parent-rated behaviors were significant predictors in all three of these analyses. In the reading score analysis

Heart rate variability biofeedback improves cardiorespiratory resting function during sleep.

Science.gov (United States)

Sakakibara, Masahito; Hayano, Junichiro; Oikawa, Leo O; Katsamanis, Maria; Lehrer, Paul

2013-12-01

The present study was designed to examine the effect of heart rate variability (HRV) biofeedback on the cardiorespiratory resting function during sleep in daily life. Forty-five healthy young adults were randomly assigned to one of three groups: HRV biofeedback, Autogenic Training(AT), and no-treatment control. Participants in the HRV biofeedback were instructed to use a handheld HRV biofeedback device before their habitual bedtime, those in the AT were asked to listen to an audiotaped instruction before bedtime,and those in the control were asked to engage in their habitual activity before bedtime. Pulse wave signal during sleep at their own residences was measured continuously with a wrist watch-type transdermal photoelectric sensor for three time points. Baseline data were collected on the first night of measurements, followed by two successive nights for HRV biofeedback, AT, or control. Cardiorespiratory resting function was assessed quantitatively as the amplitude of high frequency(HF) component of pulse rate variability, a surrogate measure of respiratory sinus arrhythmia. HF component increased during sleep in the HRV biofeedback group,although it remained unchanged in the AT and control groups. These results suggest that HRV biofeedback before sleep may improve cardiorespiratory resting function during sleep.

Biographical and demographical variables as moderators in the prediction of turnover intentions

Directory of Open Access Journals (Sweden)

Janine du Plooy

2013-04-01

Full Text Available Orientation: The aim of the study was to explore the possible moderation effects of biographical and demographical variables on a prediction model of turnover intention (TI. Research purpose: The main purpose of the study was to determine how biographical and demographical variables have an impact on predictors of turnover intentions. Motivation for the study: Twenty-first century organisations face significant challenges in the management of talent and human capital. One in particular is voluntary employee turnover and the lack of appropriate business models to track this process. Research design, approach, and method: A secondary data analysis (SDA was performed in a quantitative research tradition on the cross-sectional survey sample (n = 2429. Data were collected from a large South African Information and Communication Technologies (ICT sector company (N = 23 134. Main findings: The results of the study confirmed significant moderation effects regarding race, age, and marital status in the prediction equations of TIs. Practical and managerial implications: Practical implications of the study suggested increased understanding of workforce diversity effects within the human resource (HR value chain, with resultant evidence-based, employee retention strategies and interventions. Issues concerning talent management could also be addressed. Contribution and value-add: The study described in this article took Industrial/Organisational (I/O psychological concepts and linked them in unique combinations to establish better predictive validity of a more comprehensive turnover intentions model.

Heart rate variability measured early in patients with evolving acute coronary syndrome and 1-year outcomes of rehospitalization and mortality.

Science.gov (United States)

Harris, Patricia R E; Stein, Phyllis K; Fung, Gordon L; Drew, Barbara J

2014-01-01

This study sought to examine the prognostic value of heart rate variability (HRV) measurement initiated immediately after emergency department presentation for patients with acute coronary syndrome (ACS). Altered HRV has been associated with adverse outcomes in heart disease, but the value of HRV measured during the earliest phases of ACS related to risk of 1-year rehospitalization and death has not been established. Twenty-four-hour Holter recordings of 279 patients with ACS were initiated within 45 minutes of emergency department arrival; recordings with ≥18 hours of sinus rhythm were selected for HRV analysis (number [N] =193). Time domain, frequency domain, and nonlinear HRV were examined. Survival analysis was performed. During the 1-year follow-up, 94 patients were event-free, 82 were readmitted, and 17 died. HRV was altered in relation to outcomes. Predictors of rehospitalization included increased normalized high frequency power, decreased normalized low frequency power, and decreased low/high frequency ratio. Normalized high frequency >42 ms(2) predicted rehospitalization while controlling for clinical variables (hazard ratio [HR] =2.3; 95% confidence interval [CI] =1.4-3.8, P=0.001). Variables significantly associated with death included natural logs of total power and ultra low frequency power. A model with ultra low frequency power 0.3 ng/mL (HR =4.0; 95% CI =1.3-12.1; P=0.016) revealed that each contributed independently in predicting mortality. Nonlinear HRV variables were significant predictors of both outcomes. HRV measured close to the ACS onset may assist in risk stratification. HRV cut-points may provide additional, incremental prognostic information to established assessment guidelines, and may be worthy of additional study.

A study of applying variable valve timing to highly rated diesel engines

Energy Technology Data Exchange (ETDEWEB)

Stone, C R; Leonard, H J [comps.; Brunel Univ., Uxbridge (United Kingdom); Charlton, S J [comp.; Bath Univ. (United Kingdom)

1992-10-01

The main objective of the research was to use Simulation Program for Internal Combustion Engines (SPICE) to quantify the potential offered by Variable Valve Timing (VVT) in improving engine performance. A model has been constructed of a particular engine using SPICE. The model has been validated with experimental data, and it has been shown that accurate predictions are made when the valve timing is changed. (author)

Influence of heavy cigarette smoking on heart rate variability and heart rate turbulence parameters

DEFF Research Database (Denmark)

Cagirci, Goksel; Cay, Serkan; Karakurt, Ozlem

2009-01-01

BACKGROUND: Cigarette smoking increases the risk of cardiovascular events related with several mechanisms. The most suggested mechanism is increased activity of sympathetic nervous system. Heart rate variability (HRV) and heart rate turbulence (HRT) has been shown to be independent and powerful......, 69 subjects and nonsmokers 74 subjects (control group) were enrolled in this study. HRV and HRT analyses [turbulence onset (TO) and turbulence slope (TS)] were assessed from 24-hour Holter recordings. RESULTS: The values of TO were significantly higher in heavy cigarette smokers than control group...... (-1.150 +/- 4.007 vs -2.454 +/- 2.796, P = 0.025, respectively), but values of TS were not statistically different between two groups (10.352 +/- 7.670 vs 9.613 +/- 7.245, P = 0.555, respectively). Also, the number of patients who had abnormal TO was significantly higher in heavy cigarette smokers...

Metaiodobenzylguanidine and heart rate variability in heart failure

International Nuclear Information System (INIS)

Kurata, Chinori; Shouda, Sakae; Mikami, Tadashi; Uehara, Akihiko; Ishikawa, Keiko; Tawarahara, Kei; Nakano, Tomoyasu; Matoh, Fumitaka; Takeuchi, Kazuhiko

1998-01-01

It is assumed that the low-frequency power (LF) of heart rate variability (HRV) increases with progress of congestive heart failure (CHF), therefore positively correlating with cardiac 123 I-metaiodobenzylguanidine (MIBG) washout. It is demonstrated here that HRV, including normalized LF, correlated inversely with MIBG washout and positively with the ratio of heart-to-mediastinum MIBG activity in controls and CHF patients, whereas these correlations were not observed within CHF patients. Thus MIBG washout may increase and HRV including normalized LF may decrease with CHF, although the HRV and MIBG measures may not similarly change in proportion to the severity of the cardiac autonomic dysfunction in CHF. (author)

Metaiodobenzylguanidine and heart rate variability in heart failure

Energy Technology Data Exchange (ETDEWEB)

Kurata, Chinori; Shouda, Sakae; Mikami, Tadashi; Uehara, Akihiko; Ishikawa, Keiko [Hamamatsu Univ., Shizuoka (Japan). School of Medicine; Tawarahara, Kei; Nakano, Tomoyasu; Matoh, Fumitaka; Takeuchi, Kazuhiko

1998-10-01

It is assumed that the low-frequency power (LF) of heart rate variability (HRV) increases with progress of congestive heart failure (CHF), therefore positively correlating with cardiac {sup 123}I-metaiodobenzylguanidine (MIBG) washout. It is demonstrated here that HRV, including normalized LF, correlated inversely with MIBG washout and positively with the ratio of heart-to-mediastinum MIBG activity in controls and CHF patients, whereas these correlations were not observed within CHF patients. Thus MIBG washout may increase and HRV including normalized LF may decrease with CHF, although the HRV and MIBG measures may not similarly change in proportion to the severity of the cardiac autonomic dysfunction in CHF. (author)

Students' Ways of Thinking about Two-Variable Functions and Rate of Change in Space

Science.gov (United States)

Weber, Eric David

2012-01-01

This dissertation describes an investigation of four students' ways of thinking about functions of two variables and rate of change of those two-variable functions. Most secondary, introductory algebra, pre-calculus, and first and second semester calculus courses do not require students to think about functions of more than one variable. Yet…

Heart rate variability recovery after a skyrunning marathon and correlates of performance

Directory of Open Access Journals (Sweden)

Michaela Mertová

2017-12-01

Full Text Available Background: It is well known that vigorous physical activity induces functional changes in cardiac autonomic nervous system (ANS activity that is sustained several hours after exercise. However, data related to ANS recovery after more extreme endurance events, such as skyrunning marathons, are still lacking. Objective: The aims of this prospective cohort study were firstly, to determine the ANS response to a SkyMarathon, and secondly, to examine correlates of run performance. Methods: Ten male skyrunners aged 37.2 ± 9.2 years were recruited. The race was performed at a mean intensity 85.4 ± 3.7% of heart rate reserve, and lasted for 338 ± 38 min. Morning supine heart rate variability was measured at 10, 2 and 1 days before race, on the race day, at 5 min intervals for 30 min immediately post-race and then at 5 h and 30 h post. High-frequency power (HF, 0.15-0.50 Hz, low-frequency power (LF, 0.05-0.15 Hz, and square root of the mean of the squares of the successive differences (RMSSD were calculated and transformed by natural logarithm (Ln. Results: Sympathovagal balance (Ln LF/HF was most likely increased above baseline during the 30 min post-race and returned to baseline by 5 h. Vagal activity (Ln RMSSD and Ln HF was most likely decreased below baseline during the 30 min post-race and 5 h of post-race, and recovered to baseline by 30 h. Race time correlated with resting heart rate (r = .81, body mass index (r = .73, maximal power output (r = -.70, and maximal oxygen uptake (r = -.61. Conclusions: The SkyMarathon elicited disturbances in ANS activity, with relative sympathetic activity increased up to 5 h post-race and vagal activity recovering by 30 h. Resting heart rate, body mass index, maximal power output, and maximal oxygen uptake were associated with SkyMarathon performance prediction.

Are Changes in Heart Rate Variability During Hypoglycemia Confounded by the Presence of Cardiovascular Autonomic Neuropathy in Patients with Diabetes?

DEFF Research Database (Denmark)

Cichosz, Simon Lebech; Frystyk, Jan; Tarnow, Lise

2017-01-01

BACKGROUND: We have recently shown how the combination of information from continuous glucose monitor (CGM) and heart rate variability (HRV) measurements can be used to construct an algorithm for prediction of hypoglycemia in both bedbound and active patients with type 1 diabetes (T1D). Questions...... with CGM and a Holter device while they performed normal daily activities. CAN was diagnosed using two cardiac reflex tests: (1) deep breathing and (2) orthostatic hypotension and end organ symptoms. Early CAN was defined as the presence of one abnormal reflex test and severe CAN was defined as two...

Results of Propellant Mixing Variable Study Using Precise Pressure-Based Burn Rate Calculations

Science.gov (United States)

Stefanski, Philip L.

2014-01-01

A designed experiment was conducted in which three mix processing variables (pre-curative addition mix temperature, pre-curative addition mixing time, and mixer speed) were varied to estimate their effects on within-mix propellant burn rate variability. The chosen discriminator for the experiment was the 2-inch diameter by 4-inch long (2x4) Center-Perforated (CP) ballistic evaluation motor. Motor nozzle throat diameters were sized to produce a common targeted chamber pressure. Initial data analysis did not show a statistically significant effect. Because propellant burn rate must be directly related to chamber pressure, a method was developed that showed statistically significant effects on chamber pressure (either maximum or average) by adjustments to the process settings. Burn rates were calculated from chamber pressures and these were then normalized to a common pressure for comparative purposes. The pressure-based method of burn rate determination showed significant reduction in error when compared to results obtained from the Brooks' modification of the propellant web-bisector burn rate determination method. Analysis of effects using burn rates calculated by the pressure-based method showed a significant correlation of within-mix burn rate dispersion to mixing duration and the quadratic of mixing duration. The findings were confirmed in a series of mixes that examined the effects of mixing time on burn rate variation, which yielded the same results.

Validation of a new control system for Elekta accelerators facilitating continuously variable dose rate

DEFF Research Database (Denmark)

Bertelsen, Anders; Lorenzen, Ebbe L; Brink, Carsten

2011-01-01

) as well as BVDR. Using CVDR opposed to BVDR for VMAT has the potential of reducing the treatment time but may lead to lower dosimetric accuracy due to faster moving accelerator parts. Using D7 and a test version of Integrity, differences in ability to control the accelerator, treatment efficiency......Elekta accelerators controlled by the current clinically used accelerator control system, Desktop 7.01 (D7), uses binned variable dose rate (BVDR) for volumetric modulated arc therapy (VMAT). The next version of the treatment control system (Integrity) supports continuously variable dose rate (CVDR...

Reproducibility of heart rate variability, blood pressure variability and baroreceptor sensitivity during rest and head-up tilt

DEFF Research Database (Denmark)

Højgaard, Michael V; Agner, Erik; Kanters, Jørgen K

2005-01-01

OBJECTIVE: Previous studies have indicated moderate-to-poor reproducibility of heart rate variability (HRV) but the reproducibility of blood pressure variability (BPV) and spectral measures of baroreceptor sensitivity (BRS) are not well established. METHODS: We measured normal-to-normal heart beat...... pressures were extracted for the assessment of day-to-day and short-term reproducibility. Power spectrum analysis (Fourier) and transfer function analysis was performed. Reproducibility was assessed using the coefficient of variation (CV). The reproducibility of the mean RR interval, mean systolic......, diastolic and mean blood pressure was good (CVspectral parameters of HRV (CV range 18-36%) and BPV (16-44%) and moderate reproducibility of BRS (14-20%). CONCLUSION: Spectral estimates of BRS had only moderate reproducibility although...

Heart rate and heart rate variability modification in chronic insomnia patients.

Science.gov (United States)

Farina, Benedetto; Dittoni, Serena; Colicchio, Salvatore; Testani, Elisa; Losurdo, Anna; Gnoni, Valentina; Di Blasi, Chiara; Brunetti, Riccardo; Contardi, Anna; Mazza, Salvatore; Della Marca, Giacomo

2014-01-01

Chronic insomnia is highly prevalent in the general population, provoking personal distress and increased risk for psychiatric and medical disorders. Autonomic hyper-arousal could be a pathogenic mechanism of chronic primary insomnia. The aim of this study was to investigate autonomic activity in patients with chronic primary insomnia by means of heart rate variability (HRV) analysis. Eighty-five consecutive patients affected by chronic primary insomnia were enrolled (38 men and 47 women; mean age: 53.2 ± 13.6). Patients were compared with a control group composed of 55 healthy participants matched for age and gender (23 men and 32 women; mean age: 54.2 ± 13.9). Patients underwent an insomnia study protocol that included subjective sleep evaluation, psychometric measures, and home-based polysomnography with evaluation of HRV in wake before sleep, in all sleep stages, and in wake after final awakening. Patients showed modifications of heart rate and HRV parameters, consistent with increased sympathetic activity, while awake before sleep and during Stage-2 non-REM sleep. No significant differences between insomniacs and controls could be detected during slow-wave sleep, REM sleep, and post-sleep wake. These results are consistent with the hypothesis that autonomic hyper-arousal is a major pathogenic mechanism in primary insomnia, and confirm that this condition is associated with an increased cardiovascular risk.

Respiratory induced heart rate variability during slow mechanical ventilation Marker to exclude brain death patients

Czech Academy of Sciences Publication Activity Database

Jurák, Pavel; Halámek, Josef; Vondra, Vlastimil; Kružliak, P.; Šrámek, V.; Cundrle, I.; Leinveber, P.; Adamek, M.; Zvoníček, V.

2017-01-01

Roč. 129, 7-8 (2017), s. 251-258 ISSN 0043-5325 R&D Projects: GA ČR GAP103/11/0933; GA MŠk(CZ) LO1212; GA MŠk ED0017/01/01; GA MZd NS10105 Institutional support: RVO:68081731 Keywords : critical illness * sedation * brain death * respiratory rate variability * heart rate variability * mechanical ventilation Subject RIV: FS - Medical Facilities ; Equipment OBOR OECD: Medical engineering Impact factor: 0.974, year: 2016

Variable synaptic strengths controls the firing rate distribution in feedforward neural networks.

Science.gov (United States)

Ly, Cheng; Marsat, Gary

2018-02-01

Heterogeneity of firing rate statistics is known to have severe consequences on neural coding. Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population. We previously developed theoretical methods to understand how two attributes (synaptic and intrinsic heterogeneity) interact and alter the firing rate distribution in a population of integrate-and-fire neurons with random recurrent coupling. Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity observed in in-vivo recordings. We demonstrate how heterogeneous neural attributes alter firing rate heterogeneity, accounting for the effect with various sensory stimuli. The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks: the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) when firing rate heterogeneity is low and is negatively correlated with excitability with high firing rate heterogeneity. We also show how our theory can be used to predict effective neural architecture. We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and discuss how it can ultimately shape population codes.

Heart Rate Variability as a Measure of Airport Ramp-Traffic Controllers Workload

Science.gov (United States)

Hayashi, Miwa; Dulchinos, Victoria Lee

2016-01-01

Heart Rate Variability (HRV) has been reported to reflect the person's cognitive and emotional stress levels, and may offer an objective measure of human-operator's workload levels, which are recorded continuously and unobtrusively to the task performance. The present paper compares the HRV data collected during a human-in-the-loop simulation of airport ramp-traffic control operations with the controller participants' own verbal self-reporting ratings of their workload.

Factors that affect the variability in heart rate during endoscopic retrograde cholangiopancreatography

DEFF Research Database (Denmark)

Christensen, Merete; Reinert, Rebekka; Rasmussen, Verner

2002-01-01

OBJECTIVE: To find out if drugs, position, and endoscopic manipulation during endoscopic retrograde cholangiopancreatography (ERCP) influence the changes in the variability of heart rate. DESIGN: Single-blind randomised trial. SUBJECTS: 10 volunteers given butyscopolamine, glucagon, or saline...

On the predictability of land surface fluxes from meteorological variables

Science.gov (United States)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

Science.gov (United States)

Jaskierniak, D.; Kuczera, G.; Benyon, R.

2016-04-01

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

Science.gov (United States)

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Stress hormones predict hyperbolic time-discount rates six months later in adults.

Science.gov (United States)

Takahashi, Taiki; Shinada, Mizuho; Inukai, Keigo; Tanida, Shigehito; Takahashi, Chisato; Mifune, Nobuhiro; Takagishi, Haruto; Horita, Yutaka; Hashimoto, Hirofumi; Yokota, Kunihiro; Kameda, Tatsuya; Yamagishi, Toshio

2010-01-01

Stress hormones have been associated with temporal discounting. Although time-discount rate is shown to be stable over a long term, no study to date examines whether individual differences in stress hormones could predict individuals' time-discount rates in the relatively distant future (e.g., six month later), which is of interest in neuroeconomics of stress-addiction association. We assessed 87 participants' salivary stress hormone (cortisol, cortisone, and alpha-amylase) levels and hyperbolic discounting of delayed rewards consisting of three magnitudes, at the time-interval of six months. For salivary steroid assays, we employed a liquid chromatography/ mass spectroscopy (LC/MS) method. The correlations between the stress hormone levels and time-discount rates were examined. We observed that salivary alpha-amylase (sAA) levels were negatively associated with time-discount rates in never-smokers. Notably, salivary levels of stress steroids (i.e., cortisol and cortisone) negatively and positively related to time-discount rates in men and women, respectively, in never-smokers. Ever-smokers' discount rates were not predicted from these stress hormone levels. Individual differences in stress hormone levels predict impulsivity in temporal discounting in the future. There are sex differences in the effect of stress steroids on temporal discounting; while there was no sex defference in the relationship between sAA and temporal discounting.

Heart rate variability measured early in patients with evolving acute coronary syndrome and 1-year outcomes of rehospitalization and mortality

Directory of Open Access Journals (Sweden)

Harris PR

2014-08-01

Full Text Available Patricia R E Harris,1 Phyllis K Stein,2 Gordon L Fung,3 Barbara J Drew4 1Electrocardiographic Monitoring Research Laboratory, School of Nursing, Department of Physiological Nursing, University of California, San Francisco, CA, USA; 2Heart Rate Variability Laboratory, School of Medicine, Division of Cardiology, Washington University, St Louis, MO, USA; 3Cardiology Services, Mount Zion, Department of Medicine, Division of Cardiology, University of California, San Francisco, CA, USA; 4School of Nursing, Department of Physiological Nursing, Division of Cardiology, University of California, San Francisco, CA, USA Objective: This study sought to examine the prognostic value of heart rate variability (HRV measurement initiated immediately after emergency department presentation for patients with acute coronary syndrome (ACS. Background: Altered HRV has been associated with adverse outcomes in heart disease, but the value of HRV measured during the earliest phases of ACS related to risk of 1-year rehospitalization and death has not been established. Methods: Twenty-four-hour Holter recordings of 279 patients with ACS were initiated within 45 minutes of emergency department arrival; recordings with ≥18 hours of sinus rhythm were selected for HRV analysis (number [N] =193. Time domain, frequency domain, and nonlinear HRV were examined. Survival analysis was performed. Results: During the 1-year follow-up, 94 patients were event-free, 82 were readmitted, and 17 died. HRV was altered in relation to outcomes. Predictors of rehospitalization included increased normalized high frequency power, decreased normalized low frequency power, and decreased low/high frequency ratio. Normalized high frequency >42 ms2 predicted rehospitalization while controlling for clinical variables (hazard ratio [HR] =2.3; 95% confidence interval [CI] =1.4–3.8, P=0.001. Variables significantly associated with death included natural logs of total power and ultra low frequency

Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods.

Science.gov (United States)

Shahdoust, Maryam; Sadeghifar, Majid; Poorolajal, Jalal; Javanrooh, Niloofar; Amini, Payam

2015-01-01

Hepatitis B (HB) is a major global mortality. Accurately predicting the trend of the disease can provide an appropriate view to make health policy disease prevention. This paper aimed to apply three different to predict monthly incidence rates of HB. This historical cohort study was conducted on the HB incidence data of Hamadan Province, the west of Iran, from 2004 to 2012. Weighted Markov Chain (WMC) method based on Markov chain theory and two time series models including Holt Exponential Smoothing (HES) and SARIMA were applied on the data. The results of different applied methods were compared to correct percentages of predicted incidence rates. The monthly incidence rates were clustered into two clusters as state of Markov chain. The correct predicted percentage of the first and second clusters for WMC, HES and SARIMA methods was (100, 0), (84, 67) and (79, 47) respectively. The overall incidence rate of HBV is estimated to decrease over time. The comparison of results of the three models indicated that in respect to existing seasonality trend and non-stationarity, the HES had the most accurate prediction of the incidence rates.

Predicting Precession Rates from Secular Dynamics for Extra-solar Multi-planet Systems

Science.gov (United States)

Van Laerhoven, Christa

2015-12-01

Considering the secular dynamics of multi-planet systems provides substantial insight into the interactions between planets in those systems. Secular interactions are those that don't involve knowing where a planet is along its orbit, and they dominate when planets are not involved in mean motion resonances. These interactions exchange angular momentum among the planets, evolving their eccentricities and inclinations. To second order in the planets' eccentricities and inclinations, the eccentricity and inclination perturbations are decoupled. Given the right variable choice, the relevant differential equations are linear and thus the eccentricity and inclination behaviors can be described as a sum of eigenmodes. Since the underlying structure of the secular eigenmodes can be calculated using only the planets' masses and semi-major axes, one can elucidate the eccentricity and inclination behavior of planets in exoplanet systems even without knowing the planets' current eccentricities and inclinations. I have calculated both the eccentricity and inclination secular eigenmodes for the population of known multi-planet systems whose planets have well determined masses and periods and have used this to predict what range of pericenter precession (and nodal regression) rates the planets may have. One might have assumed that in any given system the planets with shorter periods would have faster precession rates, but I show that this is not necessarily the case. Planets that are 'loners' have narrow ranges of possible precession rates, while planets that are 'groupies' can have a wider range of possible precession rates. Several planets are expected to undergo significant precession on few-year timescales and many planets (though not the majority of planets) will undergo significant precession on decade timescales.

Heart rate variability alters cardiac repolarization and electromechanical dynamics.

Science.gov (United States)

Phadumdeo, Vrishti M; Weinberg, Seth H

2018-04-07

Heart rate continuously varies due to autonomic regulation, stochasticity in pacemaking, and circadian rhythm, collectively termed heart rate variability (HRV), during normal physiological conditions. Low HRV is clinically associated with an elevated risk of cardiac arrhythmias. Alternans, a beat-to-beat alternation in action potential duration (APD) and/or intracellular calcium (Ca) transient, is a well-known risk factor associated with cardiac arrhythmias that is typically studied under conditions of a constant pacing rate, i.e., the absence of HRV. In this study, we investigate the effects of HRV on the interplay between APD, Ca, and electromechanical properties, employing a nonlinear discrete-time map model that governs APD and intracellular Ca cycling with a stochastic pacing period. We find that HRV can decrease variation in APD and peak Ca at fast pacing rates for which alternans is present. Further, increased HRV typically disrupts the alternating pattern for both APD and peak Ca and weakens the correlation between APD and peak Ca, thus decoupling Ca-mediated instabilities from repolarization alternation. We find that the efficacy of these effects is regulated by the sarcoplasmic reticulum Ca uptake rate. Overall, these results demonstrate that HRV disrupts arrhythmogenic alternans and suggests that HRV may be a significant factor in preventing life-threatening arrhythmias. Copyright © 2018 Elsevier Ltd. All rights reserved.

Statistical variability and confidence intervals for planar dose QA pass rates

Energy Technology Data Exchange (ETDEWEB)

Bailey, Daniel W.; Nelms, Benjamin E.; Attwood, Kristopher; Kumaraswamy, Lalith; Podgorsak, Matthew B. [Department of Physics, State University of New York at Buffalo, Buffalo, New York 14260 (United States) and Department of Radiation Medicine, Roswell Park Cancer Institute, Buffalo, New York 14263 (United States); Canis Lupus LLC, Merrimac, Wisconsin 53561 (United States); Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, New York 14263 (United States); Department of Radiation Medicine, Roswell Park Cancer Institute, Buffalo, New York 14263 (United States); Department of Radiation Medicine, Roswell Park Cancer Institute, Buffalo, New York 14263 (United States); Department of Molecular and Cellular Biophysics and Biochemistry, Roswell Park Cancer Institute, Buffalo, New York 14263 (United States) and Department of Physiology and Biophysics, State University of New York at Buffalo, Buffalo, New York 14214 (United States)

2011-11-15

Purpose: The most common metric for comparing measured to calculated dose, such as for pretreatment quality assurance of intensity-modulated photon fields, is a pass rate (%) generated using percent difference (%Diff), distance-to-agreement (DTA), or some combination of the two (e.g., gamma evaluation). For many dosimeters, the grid of analyzed points corresponds to an array with a low areal density of point detectors. In these cases, the pass rates for any given comparison criteria are not absolute but exhibit statistical variability that is a function, in part, on the detector sampling geometry. In this work, the authors analyze the statistics of various methods commonly used to calculate pass rates and propose methods for establishing confidence intervals for pass rates obtained with low-density arrays. Methods: Dose planes were acquired for 25 prostate and 79 head and neck intensity-modulated fields via diode array and electronic portal imaging device (EPID), and matching calculated dose planes were created via a commercial treatment planning system. Pass rates for each dose plane pair (both centered to the beam central axis) were calculated with several common comparison methods: %Diff/DTA composite analysis and gamma evaluation, using absolute dose comparison with both local and global normalization. Specialized software was designed to selectively sample the measured EPID response (very high data density) down to discrete points to simulate low-density measurements. The software was used to realign the simulated detector grid at many simulated positions with respect to the beam central axis, thereby altering the low-density sampled grid. Simulations were repeated with 100 positional iterations using a 1 detector/cm{sup 2} uniform grid, a 2 detector/cm{sup 2} uniform grid, and similar random detector grids. For each simulation, %/DTA composite pass rates were calculated with various %Diff/DTA criteria and for both local and global %Diff normalization

Compact Web browsing profiles for click-through rate prediction

DEFF Research Database (Denmark)

Fruergaard, Bjarne Ørum; Hansen, Lars Kai

2014-01-01

In real time advertising we are interested in finding features that improve click-through rate prediction. One source of available information is the bipartite graph of websites previously engaged by identifiable users. In this work, we investigate three different decompositions of such a graph...

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation dissipation theorem

Science.gov (United States)

Frank, T. D.; Patanarapeelert, K.; Beek, P. J.

2008-05-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted.

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation-dissipation theorem

International Nuclear Information System (INIS)

Frank, T.D.; Patanarapeelert, K.; Beek, P.J.

2008-01-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted

Tempts to determine radon entry rate and air exchange rate variable in time from the time course of indoor radon concentration

International Nuclear Information System (INIS)

Thomas, J.

1996-01-01

For the study and explanation of the diurnal variability of the indoor radon concentration a(t) [Bq/m 3 ], which is proportional to the ratio of the radon entry rate A [Bq/h] and the air exchange rate k [1/h], it would be of advantage to know separately the diurnal variability of both determining quantities A(t) and k(t). To measure directly and continuously the radon entry rate A(t) is possible only in special studies (mostly in experimental rooms) and also continuous measuring of the air exchange rate k(t) is possible also only in special studies for a short time. But continuously measuring radon meters are now common, do not trouble people in normal living regime during day and night. The goal of this endeavour would be the evaluation of the time courses of both determining quantities from the time courses of the indoor radon concentration directly without additional experimental work and so a better utilisation of such measurements. (author)

Prediction of critical flow rates through power-operated relief valves

International Nuclear Information System (INIS)

Abdollahian, D.; Singh, A.

1983-01-01

Existing single-phase and two-phase critical flow models are used to predict the flow rates through the power-operated relief valves tested in the EPRI Safety and Relief Valve test program. For liquid upstream conditions, Homogeneous Equilibrium Model, Moody, Henry-Fauske and Burnell two-phase critical flow models are used for comparison with data. Under steam upstream conditions, the flow rates are predicted either by the single-phase isentropic equations or the Homogeneous Equilibrium Model, depending on the thermodynamic condition of the fluid at the choking plane. The results of the comparisons are used to specify discharge coefficients for different valves under steam and liquid upstream conditions and evaluate the existing approximate critical flow relations for a wide range of subcooled water and steam conditions

Reliability prediction system based on the failure rate model for electronic components

International Nuclear Information System (INIS)

Lee, Seung Woo; Lee, Hwa Ki

2008-01-01

Although many methodologies for predicting the reliability of electronic components have been developed, their reliability might be subjective according to a particular set of circumstances, and therefore it is not easy to quantify their reliability. Among the reliability prediction methods are the statistical analysis based method, the similarity analysis method based on an external failure rate database, and the method based on the physics-of-failure model. In this study, we developed a system by which the reliability of electronic components can be predicted by creating a system for the statistical analysis method of predicting reliability most easily. The failure rate models that were applied are MILHDBK- 217F N2, PRISM, and Telcordia (Bellcore), and these were compared with the general purpose system in order to validate the effectiveness of the developed system. Being able to predict the reliability of electronic components from the stage of design, the system that we have developed is expected to contribute to enhancing the reliability of electronic components

Analysis and prediction of translation rate based on sequence and functional features of the mRNA.

Directory of Open Access Journals (Sweden)

Tao Huang

Full Text Available Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1 integrating various sequence-derived and functional features, (2 applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3 being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.

Variability in the measurement of hospital-wide mortality rates.

Science.gov (United States)

Shahian, David M; Wolf, Robert E; Iezzoni, Lisa I; Kirle, Leslie; Normand, Sharon-Lise T

2010-12-23

Several countries use hospital-wide mortality rates to evaluate the quality of hospital care, although the usefulness of this metric has been questioned. Massachusetts policymakers recently requested an assessment of methods to calculate this aggregate mortality metric for use as a measure of hospital quality. The Massachusetts Division of Health Care Finance and Policy provided four vendors with identical information on 2,528,624 discharges from Massachusetts acute care hospitals from October 1, 2004, through September 30, 2007. Vendors applied their risk-adjustment algorithms and provided predicted probabilities of in-hospital death for each discharge and for hospital-level observed and expected mortality rates. We compared the numbers and characteristics of discharges and hospitals included by each of the four methods. We also compared hospitals' standardized mortality ratios and classification of hospitals with mortality rates that were higher or lower than expected, according to each method. The proportions of discharges that were included by each method ranged from 28% to 95%, and the severity of patients' diagnoses varied widely. Because of their discharge-selection criteria, two methods calculated in-hospital mortality rates (4.0% and 5.9%) that were twice the state average (2.1%). Pairwise associations (Pearson correlation coefficients) of discharge-level predicted mortality probabilities ranged from 0.46 to 0.70. Hospital-performance categorizations varied substantially and were sometimes completely discordant. In 2006, a total of 12 of 28 hospitals that had higher-than-expected hospital-wide mortality when classified by one method had lower-than-expected mortality when classified by one or more of the other methods. Four common methods for calculating hospital-wide mortality produced substantially different results. This may have resulted from a lack of standardized national eligibility and exclusion criteria, different statistical methods, or

Role of climate variability in the heatstroke death rates of Kanto region in Japan

Science.gov (United States)

Akihiko, Takaya; Morioka, Yushi; Behera, Swadhin K.

2014-07-01

The death toll by heatstroke in Japan, especially in Kanto region, has sharply increased since 1994 together with large interannual variability. The surface air temperature and humidity observed during boreal summers of 1980-2010 were examined to understand the role of climate in the death toll. The extremely hot days, when the daily maximum temperature exceeds 35°C, are more strongly associated with the death toll than the conventional Wet Bulb Globe Temperature index. The extremely hot days tend to be associated with El Niño/Southern Oscillation or the Indian Ocean Dipole, suggesting a potential link with tropical climate variability to the heatstroke related deaths. Also, the influence of these climate modes on the death toll has strengthened since 1994 probably related to global warming. It is possible to develop early warning systems based on seasonal climate predictions since recent climate models show excellent predictability skills for those climate modes.

Diminution of Heart Rate Variability in Bipolar Depression

Directory of Open Access Journals (Sweden)

Brandon Hage

2017-12-01

Full Text Available Autonomic nervous system (ANS dysregulation in depression is associated with symptoms associated with the ANS. The beat-to-beat pattern of heart rate defined as heart rate variability (HRV provides a noninvasive portal to ANS function and has been proposed to represent a means of quantifying resting vagal tone. We quantified HRV in bipolar depressed (BDD patients as a measure of ANS dysregulation seeking to establish HRV as a potential diagnostic and prognostic biomarker for treatment outcome. Forty-seven BDD patients were enrolled. They were randomized to receive either escitalopram–celecoxib or escitalopram-placebo over 8 weeks in a double-blind study design. Thirty-five patients completed the HRV studies. Thirty-six healthy subjects served as controls. HRV was assessed at pretreatment and end of study and compared with that of controls. HRV was quantified and corrected for artifacts using an algorithm that incorporates time and frequency domains to address non-stationarity of the beat-to-beat heart rate pattern. Baseline high frequency-HRV (i.e., respiratory sinus arrhythmia was lower in BDD patients than controls, although the difference did not reach significance. Baseline low-frequency HRV was significantly lower in BDD patients (ln4.20 than controls (ln = 5.50 (p < 0.01. Baseline heart period was significantly shorter (i.e., faster heart rate in BDD patients than controls. No significant change in HRV parameters were detected over the course of the study with either treatment. These findings suggest that components of HRV may be diminished in BDD patients.

Stream recession curves and storage variability in small watersheds

Directory of Open Access Journals (Sweden)

N. Y. Krakauer

2011-07-01

Full Text Available The pattern of streamflow recession after rain events offers clues about the relationship between watershed runoff (observable as river discharge and water storage (not directly observable and can help in water resource assessment and prediction. However, there have been few systematic assessments of how streamflow recession varies across flow rates and how it relates to independent assessments of terrestrial water storage. We characterized the streamflow recession pattern in 61 relatively undisturbed small watersheds (1–100 km² across the coterminous United States with multiyear records of hourly streamflow from automated gauges. We used the North American Regional Reanalysis to help identify periods where precipitation, snowmelt, and evaporation were small compared to streamflow. The order of magnitude of the recession timescale increases from 1 day at high flow rates (~1 mm h⁻¹ to 10 days at low flow rates (~0.01 mm h⁻¹, leveling off at low flow rates. There is significant variability in the recession timescale at a given flow rate between basins, which correlates with climate and geomorphic variables such as the ratio of mean streamflow to precipitation and soil water infiltration capacity. Stepwise multiple regression was used to construct a six-variable predictive model that explained some 80 % of the variance in recession timescale at high flow rates and 30–50 % at low flow rates. Seasonal and interannual variability in inferred storage shows similar time evolution to regional-scale water storage variability estimated from GRACE satellite gravity data and from land surface modeling forced by observed meteorology, but is up to a factor of 10 smaller. Study of this discrepancy in the inferred storage amplitude may provide clues to the range of validity of the recession curve approach to relating runoff and storage.

A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater

International Nuclear Information System (INIS)

Turkdogan-Aydinol, F. Ilter; Yetilmezsoy, Kaan

2010-01-01

A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R V ), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 (±3)% and an average volumetric TCOD removal rate of 6.87 (±3.93) kg TCOD removed /m 3 -day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98.

A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater

Energy Technology Data Exchange (ETDEWEB)

Turkdogan-Aydinol, F. Ilter, E-mail: aydin@yildiz.edu.tr [Yildiz Technical University, Faculty of Civil Engineering, Department of Environmental Engineering, 34220 Davutpasa, Esenler, Istanbul (Turkey); Yetilmezsoy, Kaan, E-mail: yetilmez@yildiz.edu.tr [Yildiz Technical University, Faculty of Civil Engineering, Department of Environmental Engineering, 34220 Davutpasa, Esenler, Istanbul (Turkey)

2010-10-15

A MIMO (multiple inputs and multiple outputs) fuzzy-logic-based model was developed to predict biogas and methane production rates in a pilot-scale 90-L mesophilic up-flow anaerobic sludge blanket (UASB) reactor treating molasses wastewater. Five input variables such as volumetric organic loading rate (OLR), volumetric total chemical oxygen demand (TCOD) removal rate (R{sub V}), influent alkalinity, influent pH and effluent pH were fuzzified by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 134 rules in the IF-THEN format. The product (prod) and the centre of gravity (COG, centroid) methods were employed as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two exponential non-linear regression models derived in this study. The UASB reactor showed a remarkable performance on the treatment of molasses wastewater, with an average TCOD removal efficiency of 93 ({+-}3)% and an average volumetric TCOD removal rate of 6.87 ({+-}3.93) kg TCOD{sub removed}/m{sup 3}-day, respectively. Findings of this study clearly indicated that, compared to non-linear regression models, the proposed MIMO fuzzy-logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting of both biogas and methane production rates with satisfactory determination coefficients over 0.98.

Stimulus variability and the phonetic relevance hypothesis: effects of variability in speaking style, fundamental frequency, and speaking rate on spoken word identification.

Science.gov (United States)

Sommers, Mitchell S; Barcroft, Joe

2006-04-01

Three experiments were conducted to examine the effects of trial-to-trial variations in speaking style, fundamental frequency, and speaking rate on identification of spoken words. In addition, the experiments investigated whether any effects of stimulus variability would be modulated by phonetic confusability (i.e., lexical difficulty). In Experiment 1, trial-to-trial variations in speaking style reduced the overall identification performance compared with conditions containing no speaking-style variability. In addition, the effects of variability were greater for phonetically confusable words than for phonetically distinct words. In Experiment 2, variations in fundamental frequency were found to have no significant effects on spoken word identification and did not interact with lexical difficulty. In Experiment 3, two different methods for varying speaking rate were found to have equivalent negative effects on spoken word recognition and similar interactions with lexical difficulty. Overall, the findings are consistent with a phonetic-relevance hypothesis, in which accommodating sources of acoustic-phonetic variability that affect phonetically relevant properties of speech signals can impair spoken word identification. In contrast, variability in parameters of the speech signal that do not affect phonetically relevant properties are not expected to affect overall identification performance. Implications of these findings for the nature and development of lexical representations are discussed.

The effects of short-term relaxation therapy on indices of heart rate variability and blood pressure in young adults.

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Pal, Gopal Krushna; Ganesh, Venkata; Karthik, Shanmugavel; Nanda, Nivedita; Pal, Pravati

2014-01-01

Assessment of short-term practice of relaxation therapy on autonomic and cardiovascular functions in first-year medical students. Case-control, interventional study. Medical college laboratory. Sixty-seven medical students, divided into two groups: study group (n = 35) and control group (n = 32). Study group subjects practiced relaxation therapy (shavasana with a soothing background music) daily 1 hour for 6 weeks. Control group did not practice relaxation techniques. Cardiovascular parameters and spectral indices of heart rate variability (HRV) were recorded before and after the 6-week practice of relaxation therapy. The data between the groups and the data before and after practice of relaxation techniques were analyzed by one-way analysis of variance and Student t-test. In the study group, prediction of low-frequency to high-frequency ratio (LF-HF) of HRV, the marker of sympathovagal balance, to blood pressure (BP) status was assessed by logistic regression. In the study group, there was significant reduction in heart rate (p = .0001), systolic (p = .0010) and diastolic (p = .0021) pressure, and rate pressure product (p linked to BP status in these individuals.

Heart rate variability: a tool to explore the sleeping brain?

OpenAIRE

Chouchou, Florian; Desseilles, Martin

2014-01-01

Sleep is divided into two main sleep stages: (1) non-rapid eye movement sleep (non-REMS), characterized among others by reduced global brain activity; and (2) rapid eye movement sleep (REMS), characterized by global brain activity similar to that of wakefulness. Results of heart rate variability (HRV) analysis, which is widely used to explore autonomic modulation, have revealed higher parasympathetic tone during normal non-REMS and a shift toward sympathetic predominance during normal REMS. M...

Statistical Dependence of Pipe Breaks on Explanatory Variables

Directory of Open Access Journals (Sweden)

Patricia Gómez-Martínez

2017-02-01

Full Text Available Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models.

A Comparison of the Predicted Tube Plugging Rate for Alloy 600HTMA Steam Generator

Energy Technology Data Exchange (ETDEWEB)

Boo, Myung Hwan; Kang, Yong Seok [Korea Hydro and Nuclear Power Co., Daejeon (Korea, Republic of)

2010-10-15

To manage components that are used in long term operations such as steam generation, it is important to know the tube plugging rate, which can cause the performance degradation. The life of components can be predicted by the method using determinism and probability theory. With a method using probability theory, damage prediction of tube is possible. In this study, damage prediction for steam generation (SG) tube is performed using Weibull distribution and predicted plugging rate (life) is compared with the simple sum plugging number and case by case (failure cause) plugging number

Confirmation of linear system theory prediction: Rate of change of Herrnstein's κ as a function of response-force requirement

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McDowell, J. J; Wood, Helena M.

1985-01-01

Four human subjects worked on all combinations of five variable-interval schedules and five reinforcer magnitudes (¢/reinforcer) in each of two phases of the experiment. In one phase the force requirement on the operandum was low (1 or 11 N) and in the other it was high (25 or 146 N). Estimates of Herrnstein's κ were obtained at each reinforcer magnitude. The results were: (1) response rate was more sensitive to changes in reinforcement rate at the high than at the low force requirement, (2) κ increased from the beginning to the end of the magnitude range for all subjects at both force requirements, (3) the reciprocal of κ was a linear function of the reciprocal of reinforcer magnitude for seven of the eight data sets, and (4) the rate of change of κ was greater at the high than at the low force requirement by an order of magnitude or more. The second and third findings confirm predictions made by linear system theory, and replicate the results of an earlier experiment (McDowell & Wood, 1984). The fourth finding confirms a further prediction of the theory and supports the theory's interpretation of conflicting data on the constancy of Herrnstein's κ. PMID:16812408

Confirmation of linear system theory prediction: Rate of change of Herrnstein's kappa as a function of response-force requirement.

Science.gov (United States)

McDowell, J J; Wood, H M

1985-01-01

Four human subjects worked on all combinations of five variable-interval schedules and five reinforcer magnitudes ( cent/reinforcer) in each of two phases of the experiment. In one phase the force requirement on the operandum was low (1 or 11 N) and in the other it was high (25 or 146 N). Estimates of Herrnstein's kappa were obtained at each reinforcer magnitude. The results were: (1) response rate was more sensitive to changes in reinforcement rate at the high than at the low force requirement, (2) kappa increased from the beginning to the end of the magnitude range for all subjects at both force requirements, (3) the reciprocal of kappa was a linear function of the reciprocal of reinforcer magnitude for seven of the eight data sets, and (4) the rate of change of kappa was greater at the high than at the low force requirement by an order of magnitude or more. The second and third findings confirm predictions made by linear system theory, and replicate the results of an earlier experiment (McDowell & Wood, 1984). The fourth finding confirms a further prediction of the theory and supports the theory's interpretation of conflicting data on the constancy of Herrnstein's kappa.

Variability of Basal Rate Profiles in Insulin Pump Therapy and Association with Complications in Type 1 Diabetes Mellitus.

Science.gov (United States)

Laimer, Markus; Melmer, Andreas; Mader, Julia K; Schütz-Fuhrmann, Ingrid; Engels, Heide-Rose; Götz, Gabriele; Pfeifer, Martin; Hermann, Julia M; Stettler, Christoph; Holl, Reinhard W

2016-01-01

Traditionally, basal rate profiles in continuous subcutaneous insulin infusion therapy are individually adapted to cover expected insulin requirements. However, whether this approach is indeed superior to a more constant BR profile has not been assessed so far. This study analysed the associations between variability of BR profiles and acute and chronic complications in adult type 1 diabetes mellitus. BR profiles of 3118 female and 2427 male patients from the "Diabetes-Patienten-Verlaufsdokumentation" registry from Germany and Austria were analysed. Acute and chronic complications were recorded 6 months prior and after the most recently documented basal rate. The "variability index" was calculated as variation of basal rate intervals in percent and describes the excursions of the basal rate intervals from the median basal rate. The variability Index correlated positively with severe hypoglycemia (r = .06; p1), hypoglycemic coma (r = .05; p = 0.002), and microalbuminuria (r = 0.05; p = 0.006). In addition, a higher variability index was associated with higher frequency of diabetic ketoacidosis (r = .04; p = 0.029) in male adult patients. Logistic regression analysis adjusted for age, gender, duration of disease and total basal insulin confirmed significant correlations of the variability index with severe hypoglycemia (β = 0.013; p1) and diabetic ketoacidosis (β = 0.012; p = 0.017). Basal rate profiles with higher variability are associated with an increased frequency of acute complications in adults with type 1 diabetes.

Sensitivity Analysis of Corrosion Rate Prediction Models Utilized for Reinforced Concrete Affected by Chloride

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Siamphukdee, Kanjana; Collins, Frank; Zou, Roger

2013-06-01

Chloride-induced reinforcement corrosion is one of the major causes of premature deterioration in reinforced concrete (RC) structures. Given the high maintenance and replacement costs, accurate modeling of RC deterioration is indispensable for ensuring the optimal allocation of limited economic resources. Since corrosion rate is one of the major factors influencing the rate of deterioration, many predictive models exist. However, because the existing models use very different sets of input parameters, the choice of model for RC deterioration is made difficult. Although the factors affecting corrosion rate are frequently reported in the literature, there is no published quantitative study on the sensitivity of predicted corrosion rate to the various input parameters. This paper presents the results of the sensitivity analysis of the input parameters for nine selected corrosion rate prediction models. Three different methods of analysis are used to determine and compare the sensitivity of corrosion rate to various input parameters: (i) univariate regression analysis, (ii) multivariate regression analysis, and (iii) sensitivity index. The results from the analysis have quantitatively verified that the corrosion rate of steel reinforcement bars in RC structures is highly sensitive to corrosion duration time, concrete resistivity, and concrete chloride content. These important findings establish that future empirical models for predicting corrosion rate of RC should carefully consider and incorporate these input parameters.

Error associated with model predictions of wildland fire rate of spread

Science.gov (United States)

Miguel G. Cruz; Martin E. Alexander

2015-01-01

How well can we expect to predict the spread rate of wildfires and prescribed fires? The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data (Alexander and Cruz 2013b#. We...

Air pollution and heart rate variability: effect modification by chronic lead exposure.

Science.gov (United States)

Park, Sung Kyun; O'Neill, Marie S; Vokonas, Pantel S; Sparrow, David; Wright, Robert O; Coull, Brent; Nie, Huiling; Hu, Howard; Schwartz, Joel

2008-01-01

Outdoor air pollution and lead exposure can disturb cardiac autonomic function, but the effects of both these exposures together have not been studied. We examined whether higher cumulative lead exposures, as measured by bone lead, modified cross-sectional associations between air pollution and heart rate variability among 384 elderly men from the Normative Aging Study. We used linear regression, controlling for clinical, demographic, and environmental covariates. We found graded, significant reductions in both high-frequency and low-frequency powers of heart rate variability in relation to ozone and sulfate across the quartiles of tibia lead. Interquartile range increases in ozone and sulfate were associated respectively, with 38% decrease (95% confidence interval = -54.6% to -14.9%) and 22% decrease (-40.4% to 1.6%) in high frequency, and 38% decrease (-51.9% to -20.4%) and 12% decrease (-28.6% to 9.3%) in low frequency, in the highest quartile of tibia lead after controlling for potential confounders. We observed similar but weaker effect modification by tibia lead adjusted for education and cumulative traffic (residuals of the regression of tibia lead on education and cumulative traffic). Patella lead modified only the ozone effect on heart rate variability. People with long-term exposure to higher levels of lead may be more sensitive to cardiac autonomic dysfunction on high air pollution days. Efforts to understand how environmental exposures affect the health of an aging population should consider both current levels of pollution and history of lead exposure as susceptibility factors.

A single dose of dark chocolate increases parasympathetic modulation and heart rate variability in healthy subjects

Directory of Open Access Journals (Sweden)

Ana Amélia Machado DUARTE

Full Text Available ABSTRACT Objective: The aim of this study was to investigate the acute effect of a single dose of dark chocolate (70% cocoa on blood pressure and heart rate variability. Methods: Thirty-one healthy subjects (aged 18-25 years; both sexes were divided into two groups: 10 subjects in the white chocolate (7.4 g group and 21 in the dark chocolate (10 g group; measurements were performed at the university's physiology lab. An electrocardiogram measured the sympathovagal balance by spectral and symbolic analysis. Results: A single dose of dark chocolate significantly reduced systolic blood pressure and heart rate. After consuming 10 g of dark chocolate, significant increases were observed for heart rate variability, standard deviation of RR intervals standard deviation of all NN intervals, square root of the mean squared differences between adjacent normal RR intervals root mean square of successive differences, and an increase in the high frequency component in absolute values, representing the parasympathetic modulation. Conclusion: In conclusion the importance of our results lies in the magnitude of the response provoked by a single dose of cocoa. Just 10 g of cocoa triggered a significant increase in parasympathetic modulation and heart rate variability. These combined effects can potentially increase life expectancy because a reduction in heart rate variability is associated with several cardiovascular diseases and higher mortality.

Impacts of Austrian Climate Variability on Honey Bee Mortality

Science.gov (United States)

Switanek, Matt; Brodschneider, Robert; Crailsheim, Karl; Truhetz, Heimo

2015-04-01

Global food production, as it is today, is not possible without pollinators such as the honey bee. It is therefore alarming that honey bee populations across the world have seen increased mortality rates in the last few decades. The challenges facing the honey bee calls into question the future of our food supply. Beside various infectious diseases, Varroa destructor is one of the main culprits leading to increased rates of honey bee mortality. Varroa destructor is a parasitic mite which strongly depends on honey bee brood for reproduction and can wipe out entire colonies. However, climate variability may also importantly influence honey bee breeding cycles and bee mortality rates. Persistent weather events affects vegetation and hence foraging possibilities for honey bees. This study first defines critical statistical relationships between key climate indicators (e.g., precipitation and temperature) and bee mortality rates across Austria, using 6 consecutive years of data. Next, these leading indicators, as they vary in space and time, are used to build a statistical model to predict bee mortality rates and the respective number of colonies affected. Using leave-one-out cross validation, the model reduces the Root Mean Square Error (RMSE) by 21% with respect to predictions made with the mean mortality rate and the number of colonies. Furthermore, a Monte Carlo test is used to establish that the model's predictions are statistically significant at the 99.9% confidence level. These results highlight the influence of climate variables on honey bee populations, although variability in climate, by itself, cannot fully explain colony losses. This study was funded by the Austrian project 'Zukunft Biene'.

Modelling and prediction of pig iron variables in the blast furnace

Energy Technology Data Exchange (ETDEWEB)

Saxen, H.; Laaksonen, M.; Waller, M. [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

1996-12-31

The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.