Subjective Life Expectancy Among College Students.

PubMed

Rodemann, Alyssa E; Arigo, Danielle

2017-09-14

Establishing healthy habits in college is important for long-term health. Despite existing health promotion efforts, many college students fail to meet recommendations for behaviors such as healthy eating and exercise, which may be due to low perceived risk for health problems. The goals of this study were to examine: (1) the accuracy of life expectancy predictions, (2) potential individual differences in accuracy (i.e., gender and conscientiousness), and (3) potential change in accuracy after inducing awareness of current health behaviors. College students from a small northeastern university completed an electronic survey, including demographics, initial predictions of their life expectancy, and their recent health behaviors. At the end of the survey, participants were asked to predict their life expectancy a second time. Their health data were then submitted to a validated online algorithm to generate calculated life expectancy. Participants significantly overestimated their initial life expectancy, and neither gender nor conscientiousness was related to the accuracy of these predictions. Further, subjective life expectancy decreased from initial to final predictions. These findings suggest that life expectancy perceptions present a unique-and potentially modifiable-psychological process that could influence college students' self-care.

Life prediction and mechanical reliability of NT551 silicon nitride

NASA Astrophysics Data System (ADS)

Andrews, Mark Jay

The inert strength and fatigue performance of a diesel engine exhaust valve made from silicon nitride (Si3N4) ceramic were assessed. The Si3N4 characterized in this study was manufactured by Saint Gobain/Norton Industrial Ceramics and was designated as NT551. The evaluation was made utilizing a probabilistic life prediction algorithm that combined censored test specimen strength data with a Weibull distribution function and the stress field of the ceramic valve obtained from finite element analysis. The major assumptions of the life prediction algorithm are that the bulk ceramic material is isotropic and homogeneous and that the strength-limiting flaws are uniformly distributed. The results from mechanical testing indicated that NT551 was not a homogeneous ceramic and that its strength were functions of temperature, loading rate, and machining orientation. Fractographic analysis identified four different failure modes; 2 were identified as inhomogeneities that were located throughout the bulk of NT551 and were due to processing operations. The fractographic analysis concluded that the strength degradation of NT551 observed from the temperature and loading rate test parameters was due to a change of state that occurred in its secondary phase. Pristine and engine-tested valves made from NT551 were loaded to failure and the inert strengths were obtained. Fractographic analysis of the valves identified the same four failure mechanisms as found with the test specimens. The fatigue performance and the inert strength of the Si3N 4 valves were assessed from censored and uncensored test specimen strength data, respectively. The inert strength failure probability predictions were compared to the inert strength of the Si3N4 valves. The inert strength failure probability predictions were more conservative than the strength of the valves. The lack of correlation between predicted and actual valve strength was due to the nonuniform distribution of inhomogeneities present in NT551. For the same reasons, the predicted and actual fatigue performance did not correlate well. The results of this study should not be considered a limitation of the life prediction algorithm but emphasize the requirement that ceramics be homogeneous and strength-limiting flaws uniformly distributed as a perquisite for accurate life prediction and reliability analyses.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

[Research on engine remaining useful life prediction based on oil spectrum analysis and particle filtering].

PubMed

Sun, Lei; Jia, Yun-xian; Cai, Li-ying; Lin, Guo-yu; Zhao, Jin-song

2013-09-01

The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

PubMed

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Life Sciences Implications of Lunar Surface Operations

NASA Technical Reports Server (NTRS)

Chappell, Steven P.; Norcross, Jason R.; Abercromby, Andrew F.; Gernhardt, Michael L.

2010-01-01

The purpose of this report is to document preliminary, predicted, life sciences implications of expected operational concepts for lunar surface extravehicular activity (EVA). Algorithms developed through simulation and testing in lunar analog environments were used to predict crew metabolic rates and ground reaction forces experienced during lunar EVA. Subsequently, the total metabolic energy consumption, the daily bone load stimulus, total oxygen needed, and other variables were calculated and provided to Human Research Program and Exploration Systems Mission Directorate stakeholders. To provide context to the modeling, the report includes an overview of some scenarios that have been considered. Concise descriptions of the analog testing and development of the algorithms are also provided. This document may be updated to remain current with evolving lunar or other planetary surface operations, assumptions and concepts, and to provide additional data and analyses collected during the ongoing analog research program.

A dynamic multi-scale Markov model based methodology for remaining life prediction

NASA Astrophysics Data System (ADS)

Yan, Jihong; Guo, Chaozhong; Wang, Xing

2011-05-01

The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov model, experimental results illustrate the effectiveness of the methodology.

Predicting Flood in Perlis Using Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Nadia Sabri, Syaidatul; Saian, Rizauddin

2017-06-01

Flood forecasting is widely being studied in order to reduce the effect of flood such as loss of property, loss of life and contamination of water supply. Usually flood occurs due to continuous heavy rainfall. This study used a variant of Ant Colony Optimization (ACO) algorithm named the Ant-Miner to develop the classification prediction model to predict flood. However, since Ant-Miner only accept discrete data, while rainfall data is a time series data, a pre-processing steps is needed to discretize the rainfall data initially. This study used a technique called the Symbolic Aggregate Approximation (SAX) to convert the rainfall time series data into discrete data. As an addition, Simple K-Means algorithm was used to cluster the data produced by SAX. The findings show that the predictive accuracy of the classification prediction model is more than 80%.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

DTIC Science & Technology

2012-09-01

94035, USA abhinav.saxena@nasa.gov ABSTRACT Prognostics deals with the prediction of the end of life ( EOL ) of a system. EOL is a random variable, due...future evolution of the system, accumulating additional uncertainty into the predicted EOL . Prediction algorithms that do not account for these sources of...uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in

Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

NASA Astrophysics Data System (ADS)

Jin, Huijin; Wu, Sujun; Peng, Yuncheng

2013-12-01

In this study, an artificial neural network (ANN) was employed to predict the contact fatigue life of alloy cast steel rolls (ACSRs) as a function of alloy composition, heat treatment parameters, and contact stress by utilizing the back-propagation algorithm. The ANN was trained and tested using experimental data and a very good performance of the neural network was achieved. The well-trained neural network was then adopted to predict the contact fatigue life of chromium alloyed cast steel rolls with different alloy compositions and heat treatment processes. The prediction results showed that the maximum value of contact fatigue life was obtained with quenching at 960 °C, tempering at 520 °C, and under the contact stress of 2355 MPa. The optimal alloy composition was C-0.54, Si-0.66, Mn-0.67, Cr-4.74, Mo-0.46, V-0.13, Ni-0.34, and Fe-balance (wt.%). Some explanations of the predicted results from the metallurgical viewpoints are given. A convenient and powerful method of optimizing alloy composition and heat treatment parameters of ACSRs has been developed.

Associative memory or algorithmic search: a comparative study on learning strategies of bats and shrews.

PubMed

Page, Rachel A; von Merten, Sophie; Siemers, Björn M

2012-07-01

Two common strategies for successful foraging are learning to associate specific sensory cues with patches of prey ("associative learning") and using set decision-making rules to systematically scan for prey ("algorithmic search"). We investigated whether an animal's life history affects which of these two foraging strategies it is likely to use. Natterer's bats (Myotis nattereri) have slow life-history traits and we predicted they would be more likely to use associative learning. Common shrews (Sorex araneus) have fast life-history traits and we predicted that they would rely more heavily on routine-based search. Apart from their marked differences in life-history traits, these two mammals are similar in body size, brain weight, habitat, and diet. We assessed foraging strategy, associative learning ability, and retention time with a four-arm maze; one arm contained a food reward and was marked with four sensory stimuli. Bats and shrews differed significantly in their foraging strategies. Most bats learned to associate the sensory stimuli with the reward and remembered this association over time. Most shrews searched the maze using consistent decision-making rules, but did not learn or remember the association. We discuss these results in terms of life-history traits and other key differences between these species. Our results suggest a link between an animal's life-history strategy and its use of associative learning.

Predicting the Onset of Anxiety Syndromes at 12 Months in Primary Care Attendees. The PredictA-Spain Study

PubMed Central

Moreno-Peral, Patricia; Luna, Juan de Dios; Marston, Louise; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Muñoz-Bravo, Carlos; Bellón, Juan Ángel

2014-01-01

Background There are no risk algorithms for the onset of anxiety syndromes at 12 months in primary care. We aimed to develop and validate internally a risk algorithm to predict the onset of anxiety syndromes at 12 months. Methods A prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multilevel logistic regression and inverse probability weighting to build the risk algorithm. Our main outcome was generalized anxiety, panic and other non-specific anxiety syndromes as measured by the Primary Care Evaluation of Mental Disorders, Patient Health Questionnaire (PRIME-MD-PHQ). We recruited 3,564 adult primary care attendees without anxiety syndromes from 174 family physicians and 32 health centers in 6 Spanish provinces. Results The cumulative 12-month incidence of anxiety syndromes was 12.2%. The predictA-Spain risk algorithm included the following predictors of anxiety syndromes: province; sex (female); younger age; taking medicines for anxiety, depression or stress; worse physical and mental quality of life (SF-12); dissatisfaction with paid and unpaid work; perception of financial strain; and the interactions sex*age, sex*perception of financial strain, and age*dissatisfaction with paid work. The C-index was 0.80 (95% confidence interval = 0.78–0.83) and the Hedges' g = 1.17 (95% confidence interval = 1.04–1.29). The Copas shrinkage factor was 0.98 and calibration plots showed an accurate goodness of fit. Conclusions The predictA-Spain risk algorithm is valid to predict anxiety syndromes at 12 months. Although external validation is required, the predictA-Spain is available for use as a predictive tool in the prevention of anxiety syndromes in primary care. PMID:25184313

Structural optimization procedure of a composite wind turbine blade for reducing both material cost and blade weight

NASA Astrophysics Data System (ADS)

Hu, Weifei; Park, Dohyun; Choi, DongHoon

2013-12-01

A composite blade structure for a 2 MW horizontal axis wind turbine is optimally designed. Design requirements are simultaneously minimizing material cost and blade weight while satisfying the constraints on stress ratio, tip deflection, fatigue life and laminate layup requirements. The stress ratio and tip deflection under extreme gust loads and the fatigue life under a stochastic normal wind load are evaluated. A blade element wind load model is proposed to explain the wind pressure difference due to blade height change during rotor rotation. For fatigue life evaluation, the stress result of an implicit nonlinear dynamic analysis under a time-varying fluctuating wind is converted to the histograms of mean and amplitude of maximum stress ratio using the rainflow counting algorithm Miner's rule is employed to predict the fatigue life. After integrating and automating the whole analysis procedure an evolutionary algorithm is used to solve the discrete optimization problem.

A novel neural-inspired learning algorithm with application to clinical risk prediction.

PubMed

Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

2015-04-01

Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials. Copyright © 2015 Elsevier Inc. All rights reserved.

Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma.

PubMed

Arnold, David T; Rowen, Donna; Versteegh, Matthijs M; Morley, Anna; Hooper, Clare E; Maskell, Nicholas A

2015-01-23

In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published. Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed. The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset. This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

Intelligent automated control of life support systems using proportional representations.

PubMed

Wu, Annie S; Garibay, Ivan I

2004-06-01

Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions that multivector control strategies are an effective method for control of coupled dynamical systems.

Improving Allergen Prediction in Main Crops Using a Weighted Integrative Method.

PubMed

Li, Jing; Wang, Jing; Li, Jing

2017-12-01

As a public health problem, food allergy is frequently caused by food allergy proteins, which trigger a type-I hypersensitivity reaction in the immune system of atopic individuals. The food allergens in our daily lives are mainly from crops including rice, wheat, soybean and maize. However, allergens in these main crops are far from fully uncovered. Although some bioinformatics tools or methods predicting the potential allergenicity of proteins have been proposed, each method has their limitation. In this paper, we built a novel algorithm PREAL W , which integrated PREAL, FAO/WHO criteria and motif-based method by a weighted average score, to benefit the advantages of different methods. Our results illustrated PREAL W has better performance significantly in the crops' allergen prediction. This integrative allergen prediction algorithm could be useful for critical food safety matters. The PREAL W could be accessed at http://lilab.life.sjtu.edu.cn:8080/prealw .

Evaluating Algorithm Performance Metrics Tailored for Prognostics

NASA Technical Reports Server (NTRS)

Saxena, Abhinav; Celaya, Jose; Saha, Bhaskar; Saha, Sankalita; Goebel, Kai

2009-01-01

Prognostics has taken a center stage in Condition Based Maintenance (CBM) where it is desired to estimate Remaining Useful Life (RUL) of the system so that remedial measures may be taken in advance to avoid catastrophic events or unwanted downtimes. Validation of such predictions is an important but difficult proposition and a lack of appropriate evaluation methods renders prognostics meaningless. Evaluation methods currently used in the research community are not standardized and in many cases do not sufficiently assess key performance aspects expected out of a prognostics algorithm. In this paper we introduce several new evaluation metrics tailored for prognostics and show that they can effectively evaluate various algorithms as compared to other conventional metrics. Specifically four algorithms namely; Relevance Vector Machine (RVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Polynomial Regression (PR) are compared. These algorithms vary in complexity and their ability to manage uncertainty around predicted estimates. Results show that the new metrics rank these algorithms in different manner and depending on the requirements and constraints suitable metrics may be chosen. Beyond these results, these metrics offer ideas about how metrics suitable to prognostics may be designed so that the evaluation procedure can be standardized. 1

The circadian profile of epilepsy improves seizure forecasting.

PubMed

Karoly, Philippa J; Ung, Hoameng; Grayden, David B; Kuhlmann, Levin; Leyde, Kent; Cook, Mark J; Freestone, Dean R

2017-08-01

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency.

PubMed

Stinear, Cathy M; Byblow, Winston D; Ackerley, Suzanne J; Barber, P Alan; Smith, Marie-Claire

2017-04-01

Several clinical measures and biomarkers are associated with motor recovery after stroke, but none are used to guide rehabilitation for individual patients. The objective of this study was to evaluate the implementation of upper limb predictions in stroke rehabilitation, by combining clinical measures and biomarkers using the Predict Recovery Potential (PREP) algorithm. Predictions were provided for patients in the implementation group (n=110) and withheld from the comparison group (n=82). Predictions guided rehabilitation therapy focus for patients in the implementation group. The effects of predictive information on clinical practice (length of stay, therapist confidence, therapy content, and dose) were evaluated. Clinical outcomes (upper limb function, impairment and use, independence, and quality of life) were measured 3 and 6 months poststroke. The primary clinical practice outcome was inpatient length of stay. The primary clinical outcome was Action Research Arm Test score 3 months poststroke. Length of stay was 1 week shorter for the implementation group (11 days; 95% confidence interval, 9-13 days) than the comparison group (17 days; 95% confidence interval, 14-21 days; P =0.001), controlling for upper limb impairment, age, sex, and comorbidities. Therapists were more confident ( P =0.004) and modified therapy content according to predictions for the implementation group ( P <0.05). The algorithm correctly predicted the primary clinical outcome for 80% of patients in both groups. There were no adverse effects of algorithm implementation on patient outcomes at 3 or 6 months poststroke. PREP algorithm predictions modify therapy content and increase rehabilitation efficiency after stroke without compromising clinical outcome. URL: http://anzctr.org.au. Unique identifier: ACTRN12611000755932. © 2017 American Heart Association, Inc.

Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records.

PubMed

Hoffman, Sarah R; Vines, Anissa I; Halladay, Jacqueline R; Pfaff, Emily; Schiff, Lauren; Westreich, Daniel; Sundaresan, Aditi; Johnson, La-Shell; Nicholson, Wanda K

2018-06-01

Women with symptomatic uterine fibroids can report a myriad of symptoms, including pain, bleeding, infertility, and psychosocial sequelae. Optimizing fibroid research requires the ability to enroll populations of women with image-confirmed symptomatic uterine fibroids. Our objective was to develop an electronic health record-based algorithm to identify women with symptomatic uterine fibroids for a comparative effectiveness study of medical or surgical treatments on quality-of-life measures. Using an iterative process and text-mining techniques, an effective computable phenotype algorithm, composed of demographics, and clinical and laboratory characteristics, was developed with reasonable performance. Such algorithms provide a feasible, efficient way to identify populations of women with symptomatic uterine fibroids for the conduct of large traditional or pragmatic trials and observational comparative effectiveness studies. Symptomatic uterine fibroids, due to menorrhagia, pelvic pain, bulk symptoms, or infertility, are a source of substantial morbidity for reproductive-age women. Comparing Treatment Options for Uterine Fibroids is a multisite registry study to compare the effectiveness of hormonal or surgical fibroid treatments on women's perceptions of their quality of life. Electronic health record-based algorithms are able to identify large numbers of women with fibroids, but additional work is needed to develop electronic health record algorithms that can identify women with symptomatic fibroids to optimize fibroid research. We sought to develop an efficient electronic health record-based algorithm that can identify women with symptomatic uterine fibroids in a large health care system for recruitment into large-scale observational and interventional research in fibroid management. We developed and assessed the accuracy of 3 algorithms to identify patients with symptomatic fibroids using an iterative approach. The data source was the Carolina Data Warehouse for Health, a repository for the health system's electronic health record data. In addition to International Classification of Diseases, Ninth Revision diagnosis and procedure codes and clinical characteristics, text data-mining software was used to derive information from imaging reports to confirm the presence of uterine fibroids. Results of each algorithm were compared with expert manual review to calculate the positive predictive values for each algorithm. Algorithm 1 was composed of the following criteria: (1) age 18-54 years; (2) either ≥1 International Classification of Diseases, Ninth Revision diagnosis codes for uterine fibroids or mention of fibroids using text-mined key words in imaging records or documents; and (3) no International Classification of Diseases, Ninth Revision or Current Procedural Terminology codes for hysterectomy and no reported history of hysterectomy. The positive predictive value was 47% (95% confidence interval 39-56%). Algorithm 2 required ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids and positive text-mined key words and had a positive predictive value of 65% (95% confidence interval 50-79%). In algorithm 3, further refinements included ≥2 International Classification of Diseases, Ninth Revision diagnosis codes for fibroids on separate outpatient visit dates, the exclusion of women who had a positive pregnancy test within 3 months of their fibroid-related visit, and exclusion of incidentally detected fibroids during prenatal or emergency department visits. Algorithm 3 achieved a positive predictive value of 76% (95% confidence interval 71-81%). An electronic health record-based algorithm is capable of identifying cases of symptomatic uterine fibroids with moderate positive predictive value and may be an efficient approach for large-scale study recruitment. Copyright © 2018 Elsevier Inc. All rights reserved.

Deriving health utilities from the MacNew Heart Disease Quality of Life Questionnaire.

PubMed

Chen, Gang; McKie, John; Khan, Munir A; Richardson, Jeff R

2015-10-01

Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. © The European Society of Cardiology 2014.

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.

PubMed

Liu, Nehemiah T; Holcomb, John B; Wade, Charles E; Batchinsky, Andriy I; Cancio, Leopoldo C; Darrah, Mark I; Salinas, José

2014-02-01

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

Nontrivial Quantum Effects in Biology: A Skeptical Physicists' View

NASA Astrophysics Data System (ADS)

Wiseman, Howard; Eisert, Jens

The following sections are included: * Introduction * A Quantum Life Principle * A quantum chemistry principle? * The anthropic principle * Quantum Computing in the Brain * Nature did everything first? * Decoherence as the make or break issue * Quantum error correction * Uselessness of quantum algorithms for organisms * Quantum Computing in Genetics * Quantum search * Teleological aspects and the fast-track to life * Quantum Consciousness * Computability and free will * Time scales * Quantum Free Will * Predictability and free will * Determinism and free will * Acknowledgements * References

Different Applications of FORTRACC: From Convective Clouds to thunderstorms and radar fields

NASA Astrophysics Data System (ADS)

Morales, C.; Machado, L. A.

2009-09-01

The algorithm Forecasting and Tracking the Evolution of Cloud Clusters (ForTraCC), Vila et al. (2008), has been employed operationally in Brazil since 2005 to track and forecast the development of convective clouds. This technique depicts the main morphological features of the cloud systems and most importantly it reconstructs its entire life cycle. Based on this information, several relationships that use the area expansion and convective and stratiform fraction are employed to predict the life time duration and cloud area. Because of these features, the civil defense and power companies are using this information to mitigate the damages in the population. Further developments in FORTRACC included the integration of satellite rainfall retrievals, radar fields and thunderstorm initiation. These improvements try to address the following problems: a) most of the satellite rainfall retrievals do not take into account the life cycle stage that it is a key element on defining the rain area and rain intensity; b) by using the life cycle information it is possible to better predict the precipitation pattern observed in the radar fields; c) cloud signatures are associated to the development of systems that have lightning and no lightning activity. During the presentation, an overview of the different applications of FORTRACC will be presented including case studies and evaluation of the technique. Finally, the presentation will address how the users can have access to the algorithm to implement in their institute.

Autonomous detection of ISO fade point with color laser printers

NASA Astrophysics Data System (ADS)

Yan, Ni; Maggard, Eric; Fothergill, Roberta; Jessome, Renee J.; Allebach, Jan P.

2015-01-01

Image quality assessment is a very important field in image processing. Human observation is slow and subjective, it also requires strict environment setup for the psychological test 1. Thus developing algorithms to match desired human experiments is always in need. Many studies have focused on detecting the fading phenomenon after the materials are printed, that is to monitor the persistence of the color ink 2-4. However, fading is also a common artifact produced by printing systems when the cartridges run low. We want to develop an automatic system to monitor cartridge life and report fading defects when they appear. In this paper, we first describe a psychological experiment that studies the human perspective on printed fading pages. Then we propose an algorithm based on Color Space Projection and K-means clustering to predict the visibility of fading defects. At last, we integrate the psychological experiment result with our algorithm to give a machine learning tool that monitors cartridge life.

Development and Validation of a Machine Learning Algorithm and Hybrid System to Predict the Need for Life-Saving Interventions in Trauma Patients

DTIC Science & Technology

2014-01-01

were stored at a rate of 1 Hz. In addition, ECg waveform data from a single lead and pleth waveform data from a thumb-mounted pulse oximeter to the...blood oxygenation (SpO2). Combinations of these vital signs were also used to derive other measurements including shock index (SI = Hr/SBP) and pulse ...combining all vital signs, trends, and pulse characteristics recorded by the monitor, and apply- ing a multivariate sensor fusion algorithm that generates

Multispectral Image Processing for Plants

NASA Technical Reports Server (NTRS)

Miles, Gaines E.

1991-01-01

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

Rainflow Algorithm-Based Lifetime Estimation of Power Semiconductors in Utility Applications

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

GopiReddy, Lakshmi Reddy; Tolbert, Leon M.; Ozpineci, Burak

Rainflow algorithms are one of the popular counting methods used in fatigue and failure analysis in conjunction with semiconductor lifetime estimation models. However, the rain-flow algorithm used in power semiconductor reliability does not consider the time-dependent mean temperature calculation. The equivalent temperature calculation proposed by Nagode et al. is applied to semiconductor lifetime estimation in this paper. A month-long arc furnace load profile is used as a test profile to estimate temperatures in insulated-gate bipolar transistors (IGBTs) in a STATCOM for reactive compensation of load. In conclusion, the degradation in the life of the IGBT power device is predicted basedmore » on time-dependent temperature calculation.« less

Rainflow Algorithm-Based Lifetime Estimation of Power Semiconductors in Utility Applications

DOE PAGES

GopiReddy, Lakshmi Reddy; Tolbert, Leon M.; Ozpineci, Burak; ...

2015-07-15

Rainflow algorithms are one of the popular counting methods used in fatigue and failure analysis in conjunction with semiconductor lifetime estimation models. However, the rain-flow algorithm used in power semiconductor reliability does not consider the time-dependent mean temperature calculation. The equivalent temperature calculation proposed by Nagode et al. is applied to semiconductor lifetime estimation in this paper. A month-long arc furnace load profile is used as a test profile to estimate temperatures in insulated-gate bipolar transistors (IGBTs) in a STATCOM for reactive compensation of load. In conclusion, the degradation in the life of the IGBT power device is predicted basedmore » on time-dependent temperature calculation.« less

Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

PubMed Central

Mei, Wenjuan; Zeng, Xianping; Yang, Chenglin; Zhou, Xiuyun

2017-01-01

The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches. PMID:29099811

RandomForest4Life: a Random Forest for predicting ALS disease progression.

PubMed

Hothorn, Torsten; Jung, Hans H

2014-09-01

We describe a method for predicting disease progression in amyotrophic lateral sclerosis (ALS) patients. The method was developed as a submission to the DREAM Phil Bowen ALS Prediction Prize4Life Challenge of summer 2012. Based on repeated patient examinations over a three- month period, we used a random forest algorithm to predict future disease progression. The procedure was set up and internally evaluated using data from 1197 ALS patients. External validation by an expert jury was based on undisclosed information of an additional 625 patients; all patient data were obtained from the PRO-ACT database. In terms of prediction accuracy, the approach described here ranked third best. Our interpretation of the prediction model confirmed previous reports suggesting that past disease progression is a strong predictor of future disease progression measured on the ALS functional rating scale (ALSFRS). We also found that larger variability in initial ALSFRS scores is linked to faster future disease progression. The results reported here furthermore suggested that approaches taking the multidimensionality of the ALSFRS into account promise some potential for improved ALS disease prediction.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Evaluation and statistical inference for human connectomes.

PubMed

Pestilli, Franco; Yeatman, Jason D; Rokem, Ariel; Kay, Kendrick N; Wandell, Brian A

2014-10-01

Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.

Behavioral Modeling for Mental Health using Machine Learning Algorithms.

PubMed

Srividya, M; Mohanavalli, S; Bhalaji, N

2018-04-03

Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.

Prognostics Approach for Power MOSFET Under Thermal-Stress

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon Celaya; Saxena, Abhinav; Kulkarni, Chetan S.; Saha, Sankalita; Goebel, Kai

2012-01-01

The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real sensors (no simulated behavior), we are attempting to assess how such algorithm behaves under realistic conditions.

Mapping from disease-specific measures to health-state utility values in individuals with migraine.

PubMed

Gillard, Patrick J; Devine, Beth; Varon, Sepideh F; Liu, Lei; Sullivan, Sean D

2012-05-01

The objective of this study was to develop empirical algorithms that estimate health-state utility values from disease-specific quality-of-life scores in individuals with migraine. Data from a cross-sectional, multicountry study were used. Individuals with episodic and chronic migraine were randomly assigned to training or validation samples. Spearman's correlation coefficients between paired EuroQol five-dimensional (EQ-5D) questionnaire utility values and both Headache Impact Test (HIT-6) scores and Migraine-Specific Quality-of-Life Questionnaire version 2.1 (MSQ) domain scores (role restrictive, role preventive, and emotional function) were examined. Regression models were constructed to estimate EQ-5D questionnaire utility values from the HIT-6 score or the MSQ domain scores. Preferred algorithms were confirmed in the validation samples. In episodic migraine, the preferred HIT-6 and MSQ algorithms explained 22% and 25% of the variance (R(2)) in the training samples, respectively, and had similar prediction errors (root mean square errors of 0.30). In chronic migraine, the preferred HIT-6 and MSQ algorithms explained 36% and 45% of the variance in the training samples, respectively, and had similar prediction errors (root mean square errors 0.31 and 0.29). In episodic and chronic migraine, no statistically significant differences were observed between the mean observed and the mean estimated EQ-5D questionnaire utility values for the preferred HIT-6 and MSQ algorithms in the validation samples. The relationship between the EQ-5D questionnaire and the HIT-6 or the MSQ is adequate to use regression equations to estimate EQ-5D questionnaire utility values. The preferred HIT-6 and MSQ algorithms will be useful in estimating health-state utilities in migraine trials in which no preference-based measure is present. Copyright © 2012 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

An Evolutionary Algorithm for Feature Subset Selection in Hard Disk Drive Failure Prediction

ERIC Educational Resources Information Center

Bhasin, Harpreet

2011-01-01

Hard disk drives are used in everyday life to store critical data. Although they are reliable, failure of a hard disk drive can be catastrophic, especially in applications like medicine, banking, air traffic control systems, missile guidance systems, computer numerical controlled machines, and more. The use of Self-Monitoring, Analysis and…

Estimating Western U.S. Reservoir Sedimentation

NASA Astrophysics Data System (ADS)

Bensching, L.; Livneh, B.; Greimann, B. P.

2017-12-01

Reservoir sedimentation is a long-term problem for water management across the Western U.S. Observations of sedimentation are limited to reservoir surveys that are costly and infrequent, with many reservoirs having only two or fewer surveys. This work aims to apply a recently developed ensemble of sediment algorithms to estimate reservoir sedimentation over several western U.S. reservoirs. The sediment algorithms include empirical, conceptual, stochastic, and processes based approaches and are coupled with a hydrologic modeling framework. Preliminary results showed that the more complex and processed based algorithms performed better in predicting high sediment flux values and in a basin transferability experiment. However, more testing and validation is required to confirm sediment model skill. This work is carried out in partnership with the Bureau of Reclamation with the goal of evaluating the viability of reservoir sediment yield prediction across the western U.S. using a multi-algorithm approach. Simulations of streamflow and sediment fluxes are validated against observed discharges, as well as a Reservoir Sedimentation Information database that is being developed by the US Army Corps of Engineers. Specific goals of this research include (i) quantifying whether inter-algorithm differences consistently capture observational variability; (ii) identifying whether certain categories of models consistently produce the best results, (iii) assessing the expected sedimentation life-span of several western U.S. reservoirs through long-term simulations.

How Are Mate Preferences Linked with Actual Mate Selection? Tests of Mate Preference Integration Algorithms Using Computer Simulations and Actual Mating Couples

PubMed Central

Conroy-Beam, Daniel; Buss, David M.

2016-01-01

Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294) we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection. PMID:27276030

How Are Mate Preferences Linked with Actual Mate Selection? Tests of Mate Preference Integration Algorithms Using Computer Simulations and Actual Mating Couples.

PubMed

Conroy-Beam, Daniel; Buss, David M

2016-01-01

Prior mate preference research has focused on the content of mate preferences. Yet in real life, people must select mates among potentials who vary along myriad dimensions. How do people incorporate information on many different mate preferences in order to choose which partner to pursue? Here, in Study 1, we compare seven candidate algorithms for integrating multiple mate preferences in a competitive agent-based model of human mate choice evolution. This model shows that a Euclidean algorithm is the most evolvable solution to the problem of selecting fitness-beneficial mates. Next, across three studies of actual couples (Study 2: n = 214; Study 3: n = 259; Study 4: n = 294) we apply the Euclidean algorithm toward predicting mate preference fulfillment overall and preference fulfillment as a function of mate value. Consistent with the hypothesis that mate preferences are integrated according to a Euclidean algorithm, we find that actual mates lie close in multidimensional preference space to the preferences of their partners. Moreover, this Euclidean preference fulfillment is greater for people who are higher in mate value, highlighting theoretically-predictable individual differences in who gets what they want. These new Euclidean tools have important implications for understanding real-world dynamics of mate selection.

A novel approach for medical research on lymphomas

PubMed Central

Conte, Cécile; Palmaro, Aurore; Grosclaude, Pascale; Daubisse-Marliac, Laetitia; Despas, Fabien; Lapeyre-Mestre, Maryse

2018-01-01

Abstract The use of claims database to study lymphomas in real-life conditions is a crucial issue in the future. In this way, it is essential to develop validated algorithms for the identification of lymphomas in these databases. The aim of this study was to assess the validity of diagnosis codes in the French health insurance database to identify incident cases of lymphomas according to results of a regional cancer registry, as the gold standard. Between 2010 and 2013, incident lymphomas were identified in hospital data through 2 algorithms of selection. The results of the identification process and characteristics of incident lymphomas cases were compared with data from the Tarn Cancer Registry. Each algorithm's performance was assessed by estimating sensitivity, predictive positive value, specificity (SPE), and negative predictive value. During the period, the registry recorded 476 incident cases of lymphomas, of which 52 were Hodgkin lymphomas and 424 non-Hodgkin lymphomas. For corresponding area and period, algorithm 1 provides a number of incident cases close to the Registry, whereas algorithm 2 overestimated the number of incident cases by approximately 30%. Both algorithms were highly specific (SPE = 99.9%) but moderately sensitive. The comparative analysis illustrates that similar distribution and characteristics are observed in both sources. Given these findings, the use of claims database can be consider as a pertinent and powerful tool to conduct medico-economic or pharmacoepidemiological studies in lymphomas. PMID:29480830

Decomposition Technique for Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)

2014-01-01

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

Optimal reentry prediction of space objects from LEO using RSM and GA

NASA Astrophysics Data System (ADS)

Mutyalarao, M.; Raj, M. Xavier James

2012-07-01

The accurate estimation of the orbital life time (OLT) of decaying near-Earth objects is of considerable importance for the prediction of risk object re-entry time and hazard assessment as well as for mitigation strategies. Recently, due to the reentries of large number of risk objects, which poses threat to the human life and property, a great concern is developed in the space scientific community all over the World. The evolution of objects in Low Earth Orbit (LEO) is determined by a complex interplay of the perturbing forces, mainly due to atmospheric drag and Earth gravity. These orbits are mostly in low eccentric (eccentricity < 0.2) and have variations in perigee and apogee altitudes due to perturbations during a revolution. The changes in the perigee and apogee altitudes of these orbits are mainly due to the gravitational perturbations of the Earth and the atmospheric density. It has become necessary to use extremely complex force models to match with the present operational requirements and observational techniques. Further the re-entry time of the objects in such orbits is sensitive to the initial conditions. In this paper the problem of predicting re-entry time is attempted as an optimal estimation problem. It is known that the errors are more in eccentricity for the observations based on two line elements (TLEs). Thus two parameters, initial eccentricity and ballistic coefficient, are chosen for optimal estimation. These two parameters are computed with response surface method (RSM) using a genetic algorithm (GA) for the selected time zones, based on rough linear variation of response parameter, the mean semi-major axis during orbit evolution. Error minimization between the observed and predicted mean Semi-major axis is achieved by the application of an optimization algorithm such as Genetic Algorithm (GA). The basic feature of the present approach is that the model and measurement errors are accountable in terms of adjusting the ballistic coefficient and eccentricity. The methodology is tested with the recently reentered objects ROSAT and PHOBOS GRUNT satellites. The study reveals a good agreement with the actual reentry time of these objects. It is also observed that the absolute percentage error in re-entry prediction time for all the two objects is found to be very less. Keywords: low eccentric, Response surface method, Genetic algorithm, apogee altitude, Ballistic coefficient

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review.

PubMed

Sanchez-Morillo, Daniel; Fernandez-Granero, Miguel A; Leon-Jimenez, Antonio

2016-08-01

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients. © The Author(s) 2016.

A model for the progressive failure of laminated composite structural components

NASA Technical Reports Server (NTRS)

Allen, D. H.; Lo, D. C.

1991-01-01

Laminated continuous fiber polymeric composites are capable of sustaining substantial load induced microstructural damage prior to component failure. Because this damage eventually leads to catastrophic failure, it is essential to capture the mechanics of progressive damage in any cogent life prediction model. For the past several years the authors have been developing one solution approach to this problem. In this approach the mechanics of matrix cracking and delamination are accounted for via locally averaged internal variables which account for the kinematics of microcracking. Damage progression is predicted by using phenomenologically based damage evolution laws which depend on the load history. The result is a nonlinear and path dependent constitutive model which has previously been implemented to a finite element computer code for analysis of structural components. Using an appropriate failure model, this algorithm can be used to predict component life. In this paper the model will be utilized to demonstrate the ability to predict the load path dependence of the damage and stresses in plates subjected to fatigue loading.

Early-Life Intelligence Predicts Midlife Biological Age

PubMed Central

Caspi, Avshalom; Belsky, Daniel W.; Harrington, Honalee; Houts, Renate; Israel, Salomon; Levine, Morgan E.; Sugden, Karen; Williams, Benjamin; Poulton, Richie; Moffitt, Terrie E.

2016-01-01

Objectives: Early-life intelligence has been shown to predict multiple causes of death in populations around the world. This finding suggests that intelligence might influence mortality through its effects on a general process of physiological deterioration (i.e., individual variation in “biological age”). We examined whether intelligence could predict measures of aging at midlife before the onset of most age-related disease. Methods: We tested whether intelligence assessed in early childhood, middle childhood, and midlife predicted midlife biological age in members of the Dunedin Study, a population-representative birth cohort. Results: Lower intelligence predicted more advanced biological age at midlife as captured by perceived facial age, a 10-biomarker algorithm based on data from the National Health and Nutrition Examination Survey (NHANES), and Framingham heart age (r = 0.1–0.2). Correlations between intelligence and telomere length were less consistent. The associations between intelligence and biological age were not explained by differences in childhood health or parental socioeconomic status, and intelligence remained a significant predictor of biological age even when intelligence was assessed before Study members began their formal schooling. Discussion: These results suggest that accelerated aging may serve as one of the factors linking low early-life intelligence to increased rates of morbidity and mortality. PMID:26014827

Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

NASA Astrophysics Data System (ADS)

Dong, Hancheng; Jin, Xiaoning; Lou, Yangbing; Wang, Changhong

2014-12-01

Lithium-ion batteries are used as the main power source in many electronic and electrical devices. In particular, with the growth in battery-powered electric vehicle development, the lithium-ion battery plays a critical role in the reliability of vehicle systems. In order to provide timely maintenance and replacement of battery systems, it is necessary to develop a reliable and accurate battery health diagnostic that takes a prognostic approach. Therefore, this paper focuses on two main methods to determine a battery's health: (1) Battery State-of-Health (SOH) monitoring and (2) Remaining Useful Life (RUL) prediction. Both of these are calculated by using a filter algorithm known as the Support Vector Regression-Particle Filter (SVR-PF). Models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. Moreover, the RUL prediction model is proposed, which is able to provide the RUL value and update the RUL probability distribution to the End-of-Life cycle. Results for both methods are presented, showing that the proposed SOH monitoring and RUL prediction methods have good performance and that the SVR-PF has better monitoring and prediction capability than the standard particle filter (PF).

A comparative study of kinetic and connectionist modeling for shelf-life prediction of Basundi mix.

PubMed

Ruhil, A P; Singh, R R B; Jain, D K; Patel, A A; Patil, G R

2011-04-01

A ready-to-reconstitute formulation of Basundi, a popular Indian dairy dessert was subjected to storage at various temperatures (10, 25 and 40 °C) and deteriorative changes in the Basundi mix were monitored using quality indices like pH, hydroxyl methyl furfural (HMF), bulk density (BD) and insolubility index (II). The multiple regression equations and the Arrhenius functions that describe the parameters' dependence on temperature for the four physico-chemical parameters were integrated to develop mathematical models for predicting sensory quality of Basundi mix. Connectionist model using multilayer feed forward neural network with back propagation algorithm was also developed for predicting the storage life of the product employing artificial neural network (ANN) tool box of MATLAB software. The quality indices served as the input parameters whereas the output parameters were the sensorily evaluated flavour and total sensory score. A total of 140 observations were used and the prediction performance was judged on the basis of per cent root mean square error. The results obtained from the two approaches were compared. Relatively lower magnitudes of percent root mean square error for both the sensory parameters indicated that the connectionist models were better fitted than kinetic models for predicting storage life.

Risk adjustment model of credit life insurance using a genetic algorithm

NASA Astrophysics Data System (ADS)

Saputra, A.; Sukono; Rusyaman, E.

2018-03-01

In managing the risk of credit life insurance, insurance company should acknowledge the character of the risks to predict future losses. Risk characteristics can be learned in a claim distribution model. There are two standard approaches in designing the distribution model of claims over the insurance period i.e, collective risk model and individual risk model. In the collective risk model, the claim arises when risk occurs is called individual claim, accumulation of individual claim during a period of insurance is called an aggregate claim. The aggregate claim model may be formed by large model and a number of individual claims. How the measurement of insurance risk with the premium model approach and whether this approach is appropriate for estimating the potential losses occur in the future. In order to solve the problem Genetic Algorithm with Roulette Wheel Selection is used.

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Pattern Recognition and Size Prediction of Microcalcification Based on Physical Characteristics by Using Digital Mammogram Images.

PubMed

Jothilakshmi, G R; Raaza, Arun; Rajendran, V; Sreenivasa Varma, Y; Guru Nirmal Raj, R

2018-06-05

Breast cancer is one of the life-threatening cancers occurring in women. In recent years, from the surveys provided by various medical organizations, it has become clear that the mortality rate of females is increasing owing to the late detection of breast cancer. Therefore, an automated algorithm is needed to identify the early occurrence of microcalcification, which would assist radiologists and physicians in reducing the false predictions via image processing techniques. In this work, we propose a new algorithm to detect the pattern of a microcalcification by calculating its physical characteristics. The considered physical characteristics are the reflection coefficient and mass density of the binned digital mammogram image. The calculation of physical characteristics doubly confirms the presence of malignant microcalcification. Subsequently, by interpolating the physical characteristics via thresholding and mapping techniques, a three-dimensional (3D) projection of the region of interest (RoI) is obtained in terms of the distance in millimeter. The size of a microcalcification is determined using this 3D-projected view. This algorithm is verified with 100 abnormal mammogram images showing microcalcification and 10 normal mammogram images. In addition to the size calculation, the proposed algorithm acts as a good classifier that is used to classify the considered input image as normal or abnormal with the help of only two physical characteristics. This proposed algorithm exhibits a classification accuracy of 99%.

Firefly as a novel swarm intelligence variable selection method in spectroscopy.

PubMed

Goodarzi, Mohammad; dos Santos Coelho, Leandro

2014-12-10

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. Copyright © 2014. Published by Elsevier B.V.

Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15.

PubMed

Mpundu-Kaambwa, Christine; Chen, Gang; Russo, Remo; Stevens, Katherine; Petersen, Karin Dam; Ratcliffe, Julie

2017-04-01

The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based. This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis. The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15-17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models. The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE. Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15-17 years. Applicability of the algorithm in younger populations should be assessed in further research.

Health-aware Model Predictive Control of Pasteurization Plant

NASA Astrophysics Data System (ADS)

Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos

2017-01-01

In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.

Prognostics and Health Monitoring: Application to Electric Vehicles

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

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

Smith, Kandler; Shi, Ying; Santhanagopalan, Shriram

Predictive models of Li-ion battery lifetime must consider a multiplicity of electrochemical, thermal, and mechanical degradation modes experienced by batteries in application environments. To complicate matters, Li-ion batteries can experience different degradation trajectories that depend on storage and cycling history of the application environment. Rates of degradation are controlled by factors such as temperature history, electrochemical operating window, and charge/discharge rate. We present a generalized battery life prognostic model framework for battery systems design and control. The model framework consists of trial functions that are statistically regressed to Li-ion cell life datasets wherein the cells have been aged under differentmore » levels of stress. Degradation mechanisms and rate laws dependent on temperature, storage, and cycling condition are regressed to the data, with multiple model hypotheses evaluated and the best model down-selected based on statistics. The resulting life prognostic model, implemented in state variable form, is extensible to arbitrary real-world scenarios. The model is applicable in real-time control algorithms to maximize battery life and performance. We discuss efforts to reduce lifetime prediction error and accommodate its inevitable impact in controller design.« less

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Hazard Forecasting by MRI: A Prediction Algorithm of the First Kind

NASA Astrophysics Data System (ADS)

Lomnitz, C.

2003-12-01

Seismic gaps do not tell us when and where the next earthquake is due. We present new results on limited earthquake hazard prediction at plate boundaries. Our algorithm quantifies earthquake hazard in seismic gaps. The prediction window found for M7 is on the order of 50 km by 20 years (Lomnitz, 1996a). The earth is unstable with respect to small perturbations of the initial conditions. A prediction of the first kind is an estimate of the time evolution of a complex system with fixed boundary conditions in response to changes in the initial state, for example, weather prediction (Edward Lorenz, 1975; Hasselmann, 2002). We use the catalog of large world earthquakes as a proxy for the initial conditions. The MRI algorithm simulates the response of the system to updating the catalog. After a local stress transient dP the entropy decays as (grad dP)2 due to transient flows directed toward the epicenter. Healing is the thermodynamic process which resets the state of stress. It proceeds as a power law from the rupture boundary inwards, as in a wound. The half-life of a rupture is defined as the healing time which shrinks the size of a scar by half. Healed segments of plate boundary can rupture again. From observations in Chile, Mexico and Japan we find that the half-life of a seismic rupture is about 20 years, in agreement with seismic gap observations. The moment ratio MR is defined as the contrast between the cumulative regional moment release and the local moment deficiency at time t along the plate boundary. The procedure is called MRI. The findings: (1) MRI works; (2) major earthquakes match prominent peaks in the MRI graph; (3) important events (Central Chile 1985; Mexico 1985; Kobe 1995) match MRI peaks which began to emerge 10 to 20 years before the earthquake; (4) The emergence of peaks in MRI depends on earlier ruptures that occurred, not adjacent to but at 10 to 20 fault lengths from the epicentral region, in agreement with triggering effects. The hazard enhancement in space is shaped like a Mexican hat function. The central part is the aftershock region, separated by a ring of quiescence from an outer region of increased rupture probability(Lomnitz, 1996b). In conclusion, we may speak of seismic weather prediction using MRI. Hasselmann, K. (2002). Is climate predictable? In The Science of Disasters, A. Bunde, J. Kropp and H.J. Schellnhuber, eds. (Springer, Berlin, 140-169). Lomnitz, C. (1996a). Predicting earthquakes with the MRI algorithm, Seismol. Res. Letters, 67, 40-46. Lomnitz, C. (1996b). Search of a worldwide catalog for earthquakes triggered at intermediate distances, Bull. Seismol. Soc. Am., 86, 293-298. Lorenz, E. (1975). Climate predictability: The physical basis of climate and climate modeling. World Meteorol. Org., Geneva, Report 16, 132.

Early-Life Intelligence Predicts Midlife Biological Age.

PubMed

Schaefer, Jonathan D; Caspi, Avshalom; Belsky, Daniel W; Harrington, Honalee; Houts, Renate; Israel, Salomon; Levine, Morgan E; Sugden, Karen; Williams, Benjamin; Poulton, Richie; Moffitt, Terrie E

2016-11-01

Early-life intelligence has been shown to predict multiple causes of death in populations around the world. This finding suggests that intelligence might influence mortality through its effects on a general process of physiological deterioration (i.e., individual variation in "biological age"). We examined whether intelligence could predict measures of aging at midlife before the onset of most age-related disease. We tested whether intelligence assessed in early childhood, middle childhood, and midlife predicted midlife biological age in members of the Dunedin Study, a population-representative birth cohort. Lower intelligence predicted more advanced biological age at midlife as captured by perceived facial age, a 10-biomarker algorithm based on data from the National Health and Nutrition Examination Survey (NHANES), and Framingham heart age (r = 0.1-0.2). Correlations between intelligence and telomere length were less consistent. The associations between intelligence and biological age were not explained by differences in childhood health or parental socioeconomic status, and intelligence remained a significant predictor of biological age even when intelligence was assessed before Study members began their formal schooling. These results suggest that accelerated aging may serve as one of the factors linking low early-life intelligence to increased rates of morbidity and mortality. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP.

PubMed

Peak, Jasmine; Goranitis, Ilias; Day, Ed; Copello, Alex; Freemantle, Nick; Frew, Emma

2018-05-30

Economic evaluation normally requires information to be collected on outcome improvement using utility values. This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult. One potential solution is the use of mapping to generate utility values from clinical measures. This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D (EQ-5D-5 L) and the ICEpop CAPability measure for Adults (ICECAP-A) from the three commonly used clinical measures; the CORE-OM, the LDQ and the TOP measures. Models were estimated using pilot trial data of heroin users in opiate substitution treatment. In the trial the EQ-5D-5 L, ICECAP-A, CORE-OM, LDQ and TOP were administered at baseline, three and twelve month time intervals. Mapping was conducted using estimation and validation datasets. The normal estimation dataset, which comprised of baseline sample data, used ordinary least squares (OLS) and tobit regression methods. Data from the baseline and three month time periods were combined to create a pooled estimation dataset. Cluster and mixed regression methods were used to map from this dataset. Predictive accuracy of the models was assessed using the root mean square error (RMSE) and the mean absolute error (MAE). Algorithms were validated using sample data from the follow-up time periods. Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence. Although both measures can be predicted, the ICECAP-A was better predicted by the clinical measures. There were no advantages of pooling the data. There were 6 chosen mapping algorithms, which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178. It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping. In the context of opiate dependence, these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options. ISRCTN22608399 . Date of registration: 27/04/2012. Date of first randomisation: 14/08/2012.

Corrosion Prediction with Parallel Finite Element Modeling for Coupled Hygro-Chemo Transport into Concrete under Chloride-Rich Environment

PubMed Central

Na, Okpin; Cai, Xiao-Chuan; Xi, Yunping

2017-01-01

The prediction of the chloride-induced corrosion is very important because of the durable life of concrete structure. To simulate more realistic durability performance of concrete structures, complex scientific methods and more accurate material models are needed. In order to predict the robust results of corrosion initiation time and to describe the thin layer from concrete surface to reinforcement, a large number of fine meshes are also used. The purpose of this study is to suggest more realistic physical model regarding coupled hygro-chemo transport and to implement the model with parallel finite element algorithm. Furthermore, microclimate model with environmental humidity and seasonal temperature is adopted. As a result, the prediction model of chloride diffusion under unsaturated condition was developed with parallel algorithms and was applied to the existing bridge to validate the model with multi-boundary condition. As the number of processors increased, the computational time decreased until the number of processors became optimized. Then, the computational time increased because the communication time between the processors increased. The framework of present model can be extended to simulate the multi-species de-icing salts ingress into non-saturated concrete structures in future work. PMID:28772714

Prognostics for Ground Support Systems: Case Study on Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai

2011-01-01

Prognostics technologies determine the health (or damage) state of a component or sub-system, and make end of life (EOL) and remaining useful life (RUL) predictions. Such information enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics methodology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the flow of propellant, so failures may have a significant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle filtering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.

Breast Cancer Patients' Depression Prediction by Machine Learning Approach.

PubMed

Cvetković, Jovana

2017-09-14

One of the most common cancer in females is breasts cancer. This cancer can has high impact on the women including health and social dimensions. One of the most common social dimension is depression caused by breast cancer. Depression can impairs life quality. Depression is one of the symptom among the breast cancer patients. One of the solution is to eliminate the depression in breast cancer patients is by treatments but these treatments can has different unpredictable impacts on the patients. Therefore it is suitable to develop algorithm in order to predict the depression range.

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi. PMID:22759391

Cognitive Inference Device for Activity Supervision in the Elderly

PubMed Central

2014-01-01

Human activity, life span, and quality of life are enhanced by innovations in science and technology. Aging individual needs to take advantage of these developments to lead a self-regulated life. However, maintaining a self-regulated life at old age involves a high degree of risk, and the elderly often fail at this goal. Thus, the objective of our study is to investigate the feasibility of implementing a cognitive inference device (CI-device) for effective activity supervision in the elderly. To frame the CI-device, we propose a device design framework along with an inference algorithm and implement the designs through an artificial neural model with different configurations, mapping the CI-device's functions to minimise the device's prediction error. An analysis and discussion are then provided to validate the feasibility of CI-device implementation for activity supervision in the elderly. PMID:25405211

Predicting the survival of diabetes using neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Data mining techniques at the present time are used in predicting diseases of health care industries. Neural Network is one among the prevailing method in data mining techniques of an intelligent field for predicting diseases in health care industries. This paper presents a study on the prediction of the survival of diabetes diseases using different learning algorithms from the supervised learning algorithms of neural network. Three learning algorithms are considered in this study: (i) The levenberg-marquardt learning algorithm (ii) The Bayesian regulation learning algorithm and (iii) The scaled conjugate gradient learning algorithm. The network is trained using the Pima Indian Diabetes Dataset with the help of MATLAB R2014(a) software. The performance of each algorithm is further discussed through regression analysis. The prediction accuracy of the best algorithm is further computed to validate the accurate prediction

A Novel Method for Satellite Maneuver Prediction

NASA Astrophysics Data System (ADS)

Shabarekh, C.; Kent-Bryant, J.; Keselman, G.; Mitidis, A.

2016-09-01

A space operations tradecraft consisting of detect-track-characterize-catalog is insufficient for maintaining Space Situational Awareness (SSA) as space becomes increasingly congested and contested. In this paper, we apply analytical methodology from the Geospatial-Intelligence (GEOINT) community to a key challenge in SSA: predicting where and when a satellite may maneuver in the future. We developed a machine learning approach to probabilistically characterize Patterns of Life (PoL) for geosynchronous (GEO) satellites. PoL are repeatable, predictable behaviors that an object exhibits within a context and is driven by spatio-temporal, relational, environmental and physical constraints. An example of PoL are station-keeping maneuvers in GEO which become generally predictable as the satellite re-positions itself to account for orbital perturbations. In an earlier publication, we demonstrated the ability to probabilistically predict maneuvers of the Galaxy 15 (NORAD ID: 28884) satellite with high confidence eight days in advance of the actual maneuver. Additionally, we were able to detect deviations from expected PoL within hours of the predicted maneuver [6]. This was done with a custom unsupervised machine learning algorithm, the Interval Similarity Model (ISM), which learns repeating intervals of maneuver patterns from unlabeled historical observations and then predicts future maneuvers. In this paper, we introduce a supervised machine learning algorithm that works in conjunction with the ISM to produce a probabilistic distribution of when future maneuvers will occur. The supervised approach uses a Support Vector Machine (SVM) to process the orbit state whereas the ISM processes the temporal intervals between maneuvers and the physics-based characteristics of the maneuvers. This multiple model approach capitalizes on the mathematical strengths of each respective algorithm while incorporating multiple features and inputs. Initial findings indicate that the combined approach can predict 70% of maneuver times within 3 days of a true maneuver time and 22% of maneuver times within 24 hours of a maneuver. We have also been able to detect deviations from expected maneuver patterns up to a week in advance.

A review on prognostics and health monitoring of Li-ion battery

NASA Astrophysics Data System (ADS)

Zhang, Jingliang; Lee, Jay

2011-08-01

The functionality and reliability of Li-ion batteries as major energy storage devices have received more and more attention from a wide spectrum of stakeholders, including federal/state policymakers, business leaders, technical researchers, environmental groups and the general public. Failures of Li-ion battery not only result in serious inconvenience and enormous replacement/repair costs, but also risk catastrophic consequences such as explosion due to overheating and short circuiting. In order to prevent severe failures from occurring, and to optimize Li-ion battery maintenance schedules, breakthroughs in prognostics and health monitoring of Li-ion batteries, with an emphasis on fault detection, correction and remaining-useful-life prediction, must be achieved. This paper reviews various aspects of recent research and developments in Li-ion battery prognostics and health monitoring, and summarizes the techniques, algorithms and models used for state-of-charge (SOC) estimation, current/voltage estimation, capacity estimation and remaining-useful-life (RUL) prediction.

Testing an earthquake prediction algorithm

USGS Publications Warehouse

Kossobokov, V.G.; Healy, J.H.; Dewey, J.W.

1997-01-01

A test to evaluate earthquake prediction algorithms is being applied to a Russian algorithm known as M8. The M8 algorithm makes intermediate term predictions for earthquakes to occur in a large circle, based on integral counts of transient seismicity in the circle. In a retroactive prediction for the period January 1, 1985 to July 1, 1991 the algorithm as configured for the forward test would have predicted eight of ten strong earthquakes in the test area. A null hypothesis, based on random assignment of predictions, predicts eight earthquakes in 2.87% of the trials. The forward test began July 1, 1991 and will run through December 31, 1997. As of July 1, 1995, the algorithm had forward predicted five out of nine earthquakes in the test area, which success ratio would have been achieved in 53% of random trials with the null hypothesis.

Flaw characterization through nonlinear ultrasonics and wavelet cross-correlation algorithms

NASA Astrophysics Data System (ADS)

Bunget, Gheorghe; Yee, Andrew; Stewart, Dylan; Rogers, James; Henley, Stanley; Bugg, Chris; Cline, John; Webster, Matthew; Farinholt, Kevin; Friedersdorf, Fritz

2018-04-01

Ultrasonic measurements have become increasingly important non-destructive techniques to characterize flaws found within various in-service industrial components. The prediction of remaining useful life based on fracture analysis depends on the accurate estimation of flaw size and orientation. However, amplitude-based ultrasonic measurements are not able to estimate the plastic zones that exist ahead of crack tips. Estimating the size of the plastic zone is an advantage since some flaws may propagate faster than others. This paper presents a wavelet cross-correlation (WCC) algorithm that was applied to nonlinear analysis of ultrasonically guided waves (GW). By using this algorithm, harmonics present in the waveforms were extracted and nonlinearity parameters were used to indicate both the tip of the cracks and size of the plastic zone. B-scans performed with the quadratic nonlinearities were sensitive to micro-damage specific to plastic zones.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Improving the Sensitivity and Positive Predictive Value in a Cystic Fibrosis Newborn Screening Program Using a Repeat Immunoreactive Trypsinogen and Genetic Analysis.

PubMed

Sontag, Marci K; Lee, Rachel; Wright, Daniel; Freedenberg, Debra; Sagel, Scott D

2016-08-01

To evaluate the performance of a new cystic fibrosis (CF) newborn screening algorithm, comprised of immunoreactive trypsinogen (IRT) in first (24-48 hours of life) and second (7-14 days of life) dried blood spot plus DNA on second dried blood spot, over existing algorithms. A retrospective review of the IRT/IRT/DNA algorithm implemented in Colorado, Wyoming, and Texas. A total of 1 520 079 newborns were screened, 32 557 (2.1%) had abnormal first IRT; 8794 (0.54%) on second. Furthermore, 14 653 mutation analyses were performed; 1391 newborns were referred for diagnostic testing; 274 newborns were diagnosed; and 201/274 (73%) of newborns had 2 mutations on the newborn screening CFTR panel. Sensitivity was 96.2%, compared with sensitivity of 76.1% observed with IRT/IRT (105 ng/mL cut-offs, P < .0001). The ratio of newborns with CF to heterozygote carriers was 1:2.5, and newborns with CF to newborns with CFTR-related metabolic syndrome was 10.8:1. The overall positive predictive value was 20%. The median age of diagnosis was 28, 30, and 39.5 days in the 3 states. IRT/IRT/DNA is more sensitive than IRT/IRT because of lower cut-offs (∼97 percentile or 60 ng/mL); higher cut-offs in IRT/IRT programs (>99 percentile, 105 ng/mL) would not achieve sufficient sensitivity. Carrier identification and identification of newborns with CFTR-related metabolic syndrome is less common in IRT/IRT/DNA compared with IRT/DNA. The time to diagnosis is nominally longer, but diagnosis can be achieved in the neonatal period and opportunities to further improve timeliness have been enacted. IRT/IRT/DNA algorithm should be considered by programs with 2 routine screens. Copyright © 2016 Elsevier Inc. All rights reserved.

Enhancing fatigue life of cylinder-crown integrated structure by optimizing dimension

NASA Astrophysics Data System (ADS)

Zhang, Weiwei; Wang, Xiaosong; Wang, Zhongren; Yuan, Shijian

2015-03-01

Cylinder-crown integrated hydraulic press (CCIHP) is a new press structure. The hemispherical hydraulic cylinder also functions as a main portion of crown, which has lower weight and higher section modulus compared with the conventional hydraulic cylinder and press crown. As a result, the material strength capacity is better utilized. During the engineering design of cylinder-crown integrated structure, in order to increase the fatigue life, structural optimization on the basis of the adaptive macro genetic algorithms (AMGA) is first conducted to both reduce weight and decrease peak stress. It is shown that the magnitude of the maximum principal stress is decreased by 28.6%, and simultaneously the total weight is reduced by 4.4%. Subsequently, strain-controlled fatigue test is carried out, and the stress-strain hysteresis loops and cyclic hardening curve are obtained. Based on linear fit, the fatigue properties are calculated and used for the fatigue life prediction. It is shown that the predicted fatigue life is significantly increased from 157000 to 1070000 cycles after structural optimization. Finally, according to the optimization design, a 6300 kN CCIHP has been manufactured, and priority application has been also suggested.

Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

PubMed

Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

2015-12-01

The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p < 0.001). Arruda was significantly better than d'Avila in predicting adjacent sites (p = 0.035) and the percent of the contralateral site prediction was higher with d'Avila than Arruda (p = 0.013). All algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

Development of a Practical Methodology for Elastic-Plastic and Fully Plastic Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

McClung, R. C.; Chell, G. G.; Lee, Y. -D.; Russell, D. A.; Orient, G. E.

1999-01-01

A practical engineering methodology has been developed to analyze and predict fatigue crack growth rates under elastic-plastic and fully plastic conditions. The methodology employs the closure-corrected effective range of the J-integral, delta J(sub eff) as the governing parameter. The methodology contains original and literature J and delta J solutions for specific geometries, along with general methods for estimating J for other geometries and other loading conditions, including combined mechanical loading and combined primary and secondary loading. The methodology also contains specific practical algorithms that translate a J solution into a prediction of fatigue crack growth rate or life, including methods for determining crack opening levels, crack instability conditions, and material properties. A critical core subset of the J solutions and the practical algorithms has been implemented into independent elastic-plastic NASGRO modules. All components of the entire methodology, including the NASGRO modules, have been verified through analysis and experiment, and limits of applicability have been identified.

Development of a Practical Methodology for Elastic-Plastic and Fully Plastic Fatigue Crack Growth

NASA Technical Reports Server (NTRS)

McClung, R. C.; Chell, G. G.; Lee, Y.-D.; Russell, D. A.; Orient, G. E.

1999-01-01

A practical engineering methodology has been developed to analyze and predict fatigue crack growth rates under elastic-plastic and fully plastic conditions. The methodology employs the closure-corrected effective range of the J-integral, (Delta)J(sub eff), as the governing parameter. The methodology contains original and literature J and (Delta)J solutions for specific geometries, along with general methods for estimating J for other geometries and other loading conditions, including combined mechanical loading and combined primary and secondary loading. The methodology also contains specific practical algorithms that translate a J solution into a prediction of fatigue crack growth rate or life, including methods for determining crack opening levels, crack instability conditions, and material properties. A critical core subset of the J solutions and the practical algorithms has been implemented into independent elastic-plastic NASGRO modules. All components of the entire methodology, including the NASGRO modules, have been verified through analysis and experiment, and limits of applicability have been identified.

Planner-Based Control of Advanced Life Support Systems

NASA Technical Reports Server (NTRS)

Muscettola, Nicola; Kortenkamp, David; Fry, Chuck; Bell, Scott

2005-01-01

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster: simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods. We describe reference implementation of an advanced control system using the IDEA control architecture developed at NASA Ames Research Center. IDEA uses planning/scheduling as the sole reasoning method for predictive and reactive closed loop control. We describe preliminary experiments in planner-based control of ALS carried out on an integrated ALS simulation developed at NASA Johnson Space Center.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.

PubMed

McGovern, Amy; Gagne, David J; Williams, John K; Brown, Rodger A; Basara, Jeffrey B

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

Screening for cystic fibrosis in New York State: considerations for algorithm improvements.

PubMed

Kay, Denise M; Maloney, Breanne; Hamel, Rhonda; Pearce, Melissa; DeMartino, Lenore; McMahon, Rebecca; McGrath, Emily; Krein, Lea; Vogel, Beth; Saavedra-Matiz, Carlos A; Caggana, Michele; Tavakoli, Norma P

2016-02-01

Newborn screening for cystic fibrosis (CF), a chronic progressive disease affecting mucus viscosity, has been beneficial in both improving life expectancy and the quality of life for individuals with CF. In New York State from 2007 to 2012 screening for CF involved measuring immunoreactive trypsinogen (IRT) levels in dried blood spots from newborns using the IMMUCHEM(™) Blood Spot Trypsin-MW ELISA kit. Any specimen in the top 5% IRT level underwent DNA analysis using the InPlex(®) CF Molecular Test. Of the 1.48 million newborns screened during the 6-year time period, 7631 babies were referred for follow-up. CF was confirmed in 251 cases, and 94 cases were diagnosed with CF transmembrane conductance regulated-related metabolic syndrome or possible CF. Nine reports of false negatives were made to the program. Variation in daily average IRT was observed depending on the season (4-6 ng/ml) and kit lot (<3 ng/ml), supporting the use of a floating cutoff. The screening method had a sensitivity of 96.5%, specificity of 99.6%, positive predictive value of 4.5%, and negative predictive value of 99.5%. Considerations for CF screening algorithms should include IRT variations resulting from age at specimen collection, sex, race/ethnicity, season, and manufacturer kit lots. Measuring IRT level in dried blood spots is the first-tier screen for CF. Current algorithms for CF screening lead to substantial false-positive referral rates. IRT values were affected by age of infant when specimen is collected, race/ethnicity and sex of infant, and changes in seasons and manufacturer kit lots The prevalence of CF in NYS is 1 in 4200 with the highest prevalence in White infants (1 in 2600) and the lowest in Black infants (1 in 15,400).

A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines.

PubMed

Sánchez Lasheras, Fernando; García Nieto, Paulino José; de Cos Juez, Francisco Javier; Mayo Bayón, Ricardo; González Suárez, Victor Manuel

2015-03-23

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

PubMed Central

Lasheras, Fernando Sánchez; Nieto, Paulino José García; de Cos Juez, Francisco Javier; Bayón, Ricardo Mayo; Suárez, Victor Manuel González

2015-01-01

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines. PMID:25806876

Process and Structural Health Monitoring of Composite Structures with Embedded Fiber Optic Sensors and Piezoelectric Transducers

NASA Astrophysics Data System (ADS)

Keulen, Casey James

Advanced composite materials are becoming increasingly more valuable in a plethora of engineering applications due to properties such as tailorability, low specific strength and stiffness and resistance to fatigue and corrosion. Compared to more traditional metallic and ceramic materials, advanced composites such as carbon, aramid or glass reinforced plastic are relatively new and still require research to optimize their capabilities. Three areas that composites stand to benefit from improvement are processing, damage detection and life prediction. Fiber optic sensors and piezoelectric transducers show great potential for advances in these areas. This dissertation presents the research performed on improving the efficiency of advanced composite materials through the use of embedded fiber optic sensors and surface mounted piezoelectric transducers. Embedded fiber optic sensors are used to detect the presence of resin during the injection stage of resin transfer molding, monitor the degree of cure and predict the remaining useful life while in service. A sophisticated resin transfer molding apparatus was developed with the ability of embedding fiber optics into the composite and a glass viewing window so that resin flow sensors could be verified visually. A novel technique for embedding optical fiber into both 2- and 3-D structures was developed. A theoretical model to predict the remaining useful life was developed and a systematic test program was conducted to verify this model. A network of piezoelectric transducers was bonded to a composite panel in order to develop a structural health monitoring algorithm capable of detecting and locating damage in a composite structure. A network configuration was introduced that allows for a modular expansion of the system to accommodate larger structures and an algorithm based on damage progression history was developed to implement the network. The details and results of this research are contained in four manuscripts that are included in Appendices A-D while the body of the dissertation provides background information and a summary of the results.

Predicting Out-of-Office Blood Pressure in the Clinic for the Diagnosis of Hypertension in Primary Care: An Economic Evaluation.

PubMed

Monahan, Mark; Jowett, Sue; Lovibond, Kate; Gill, Paramjit; Godwin, Marshall; Greenfield, Sheila; Hanley, Janet; Hobbs, F D Richard; Martin, Una; Mant, Jonathan; McKinstry, Brian; Williams, Bryan; Sheppard, James P; McManus, Richard J

2018-02-01

Clinical guidelines in the United States and United Kingdom recommend that individuals with suspected hypertension should have ambulatory blood pressure (BP) monitoring to confirm the diagnosis. This approach reduces misdiagnosis because of white coat hypertension but will not identify people with masked hypertension who may benefit from treatment. The Predicting Out-of-Office Blood Pressure (PROOF-BP) algorithm predicts masked and white coat hypertension based on patient characteristics and clinic BP, improving the accuracy of diagnosis while limiting subsequent ambulatory BP monitoring. This study assessed the cost-effectiveness of using this tool in diagnosing hypertension in primary care. A Markov cost-utility cohort model was developed to compare diagnostic strategies: the PROOF-BP approach, including those with clinic BP ≥130/80 mm Hg who receive ambulatory BP monitoring as guided by the algorithm, compared with current standard diagnostic strategies including those with clinic BP ≥140/90 mm Hg combined with further monitoring (ambulatory BP monitoring as reference, clinic, and home monitoring also assessed). The model adopted a lifetime horizon with a 3-month time cycle, taking a UK Health Service/Personal Social Services perspective. The PROOF-BP algorithm was cost-effective in screening all patients with clinic BP ≥130/80 mm Hg compared with current strategies that only screen those with clinic BP ≥140/90 mm Hg, provided healthcare providers were willing to pay up to £20 000 ($26 000)/quality-adjusted life year gained. Deterministic and probabilistic sensitivity analyses supported the base-case findings. The PROOF-BP algorithm seems to be cost-effective compared with the conventional BP diagnostic options in primary care. Its use in clinical practice is likely to lead to reduced cardiovascular disease, death, and disability. © 2017 American Heart Association, Inc.

A pointing facilitation system for motor-impaired users combining polynomial smoothing and time-weighted gradient target prediction models.

PubMed

Blow, Nikolaus; Biswas, Pradipta

2017-01-01

As computers become more and more essential for everyday life, people who cannot use them are missing out on an important tool. The predominant method of interaction with a screen is a mouse, and difficulty in using a mouse can be a huge obstacle for people who would otherwise gain great value from using a computer. If mouse pointing were to be made easier, then a large number of users may be able to begin using a computer efficiently where they may previously have been unable to. The present article aimed to improve pointing speeds for people with arm or hand impairments. The authors investigated different smoothing and prediction models on a stored data set involving 25 people, and the best of these algorithms were chosen. A web-based prototype was developed combining a polynomial smoothing algorithm with a time-weighted gradient target prediction model. The adapted interface gave an average improvement of 13.5% in target selection times in a 10-person study of representative users of the system. A demonstration video of the system is available at https://youtu.be/sAzbrKHivEY.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

[Radiation conditions and radiation risks for cosmonauts flying to Mars using electrical jet microthrusters].

PubMed

Shafirkin, A V; Kolomenskiĭ, A V

2008-01-01

According to recent workups, the Mars mission spacecraft will be designed with an electrical jet microthrusters rather than a power reactor facility. The article contains analysis of the main sources of radiation hazard during the exploration mission using this cost-efficient, ecological, easy-to-operate propulsion powered by solar arrays. In addition, the authors make predictions of the generalized doses of ionizing radiation for mission durations of 730 and 900 days behind various shielding thicknesses, and on the Martian surface. Calculation algorithms are described and radiation risks are estimated for the crew life span and possible life time reduction in consequence of participation in the mission.

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm.

PubMed

Chiroma, Haruna; Abdul-kareem, Sameem; Khan, Abdullah; Nawi, Nazri Mohd; Gital, Abdulsalam Ya'u; Shuib, Liyana; Abubakar, Adamu I; Rahman, Muhammad Zubair; Herawan, Tutut

2015-01-01

Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks--hence, reducing global warming. The policy implications are discussed in the paper.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health

PubMed Central

Zafra-Cabeza, Ascensión; Rivera, Daniel E.; Collins, Linda M.; Ridao, Miguel A.; Camacho, Eduardo F.

2010-01-01

This paper examines how control engineering and risk management techniques can be applied in the field of behavioral health through their use in the design and implementation of adaptive behavioral interventions. Adaptive interventions are gaining increasing acceptance as a means to improve prevention and treatment of chronic, relapsing disorders, such as abuse of alcohol, tobacco, and other drugs, mental illness, and obesity. A risk-based Model Predictive Control (MPC) algorithm is developed for a hypothetical intervention inspired by Fast Track, a real-life program whose long-term goal is the prevention of conduct disorders in at-risk children. The MPC-based algorithm decides on the appropriate frequency of counselor home visits, mentoring sessions, and the availability of after-school recreation activities by relying on a model that includes identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. MPC is particularly suited for the problem because of its constraint-handling capabilities, and its ability to scale to interventions involving multiple tailoring variables. By systematically accounting for risks and adapting treatment components over time, an MPC approach as described in this paper can increase intervention effectiveness and adherence while reducing waste, resulting in advantages over conventional fixed treatment. A series of simulations are conducted under varying conditions to demonstrate the effectiveness of the algorithm. PMID:21643450

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Algorithm for predicting death among older adults in the home care setting: study protocol for the Risk Evaluation for Support: Predictions for Elder-life in the Community Tool (RESPECT)

PubMed Central

Manuel, Douglas G; Taljaard, Monica; Chalifoux, Mathieu; Bennett, Carol; Costa, Andrew P; Bronskill, Susan; Kobewka, Daniel; Tanuseputro, Peter

2016-01-01

Introduction Older adults living in the community often have multiple, chronic conditions and functional impairments. A challenge for healthcare providers working in the community is the lack of a predictive tool that can be applied to the broad spectrum of mortality risks observed and may be used to inform care planning. Objective To predict survival time for older adults in the home care setting. The final mortality risk algorithm will be implemented as a web-based calculator that can be used by older adults needing care and by their caregivers. Design Open cohort study using the Resident Assessment Instrument for Home Care (RAI-HC) data in Ontario, Canada, from 1 January 2007 to 31 December 2013. Participants The derivation cohort will consist of ∼437 000 older adults who had an RAI-HC assessment between 1 January 2007 and 31 December 2012. A split sample validation cohort will include ∼122 000 older adults with an RAI-HC assessment between 1 January and 31 December 2013. Main outcome measures Predicted survival from the time of an RAI-HC assessment. All deaths (n≈245 000) will be ascertained through linkage to a population-based registry that is maintained by the Ministry of Health in Ontario. Statistical analysis Proportional hazards regression will be estimated after assessment of assumptions. Predictors will include sociodemographic factors, social support, health conditions, functional status, cognition, symptoms of decline and prior healthcare use. Model performance will be evaluated for 6-month and 12-month predicted risks, including measures of calibration (eg, calibration plots) and discrimination (eg, c-statistics). The final algorithm will use combined development and validation data. Ethics and dissemination Research ethics approval has been granted by the Sunnybrook Health Sciences Centre Review Board. Findings will be disseminated through presentations at conferences and in peer-reviewed journals. Trial registration number NCT02779309, Pre-results. PMID:27909039

Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™).

PubMed

Lloyd, Andrew; Kerr, Cicely; Breheny, Katie; Brazier, John; Ortiz, Aurora; Borg, Emma

2014-03-01

Condition-specific preference-based measures can offer utility data where they would not otherwise be available or where generic measures may lack sensitivity, although they lack comparability across conditions. This study aimed to develop an algorithm for estimating utilities from the short bowel syndrome health-related quality of life scale (SBS-QoL™). SBS-QoL™ items were selected based on factor and item performance analysis of a European SBS-QoL™ dataset and consultation with 3 SBS clinical experts. Six-dimension health states were developed using 8 SBS-QoL™ items (2 dimensions combined 2 SBS-QoL™ items). SBS health states were valued by a UK general population sample (N = 250) using the lead-time time trade-off method. Preference weights or 'utility decrements' for each severity level of each dimension were estimated by regression models and used to develop the scoring algorithm. Mean utilities for the SBS health states ranged from -0.46 (worst health state, very much affected on all dimensions) to 0.92 (best health state, not at all affected on all dimensions). The random effects model with maximum likelihood estimation regression had the best predictive ability and lowest root mean squared error and mean absolute error, and was used to develop the scoring algorithm. The preference-weighted scoring algorithm for the SBS-QoL™ developed is able to estimate a wide range of utility values from patient-level SBS-QoL™ data. This allows estimation of SBS HRQL impact for the purpose of economic evaluation of SBS treatment benefits.

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm or conceivably lead to a radically different approach to earthquake prediction.

Effectiveness and cost-effectiveness of a cardiovascular risk prediction algorithm for people with severe mental illness (PRIMROSE).

PubMed

Zomer, Ella; Osborn, David; Nazareth, Irwin; Blackburn, Ruth; Burton, Alexandra; Hardoon, Sarah; Holt, Richard Ian Gregory; King, Michael; Marston, Louise; Morris, Stephen; Omar, Rumana; Petersen, Irene; Walters, Kate; Hunter, Rachael Maree

2017-09-05

To determine the cost-effectiveness of two bespoke severe mental illness (SMI)-specific risk algorithms compared with standard risk algorithms for primary cardiovascular disease (CVD) prevention in those with SMI. Primary care setting in the UK. The analysis was from the National Health Service perspective. 1000 individuals with SMI from The Health Improvement Network Database, aged 30-74 years and without existing CVD, populated the model. Four cardiovascular risk algorithms were assessed: (1) general population lipid, (2) general population body mass index (BMI), (3) SMI-specific lipid and (4) SMI-specific BMI, compared against no algorithm. At baseline, each cardiovascular risk algorithm was applied and those considered high risk ( > 10%) were assumed to be prescribed statin therapy while others received usual care. Quality-adjusted life years (QALYs) and costs were accrued for each algorithm including no algorithm, and cost-effectiveness was calculated using the net monetary benefit (NMB) approach. Deterministic and probabilistic sensitivity analyses were performed to test assumptions made and uncertainty around parameter estimates. The SMI-specific BMI algorithm had the highest NMB resulting in 15 additional QALYs and a cost saving of approximately £53 000 per 1000 patients with SMI over 10 years, followed by the general population lipid algorithm (13 additional QALYs and a cost saving of £46 000). The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of an SMI-specific BMI algorithm (blood tests not required) makes it an attractive algorithm to implement in clinical settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Physics-of-Failure Approach to Prognostics

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.

2017-01-01

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

A traveling salesman approach for predicting protein functions.

PubMed

Johnson, Olin; Liu, Jing

2006-10-12

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

A traveling salesman approach for predicting protein functions

PubMed Central

Johnson, Olin; Liu, Jing

2006-01-01

Background Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. Results Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm [1] on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. Conclusion Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems. PMID:17147783

Mathematical simulation and optimization of cutting mode in turning of workpieces made of nickel-based heat-resistant alloy

NASA Astrophysics Data System (ADS)

Bogoljubova, M. N.; Afonasov, A. I.; Kozlov, B. N.; Shavdurov, D. E.

2018-05-01

A predictive simulation technique of optimal cutting modes in the turning of workpieces made of nickel-based heat-resistant alloys, different from the well-known ones, is proposed. The impact of various factors on the cutting process with the purpose of determining optimal parameters of machining in concordance with certain effectiveness criteria is analyzed in the paper. A mathematical model of optimization, algorithms and computer programmes, visual graphical forms reflecting dependences of the effectiveness criteria – productivity, net cost, and tool life on parameters of the technological process - have been worked out. A nonlinear model for multidimensional functions, “solution of the equation with multiple unknowns”, “a coordinate descent method” and heuristic algorithms are accepted to solve the problem of optimization of cutting mode parameters. Research shows that in machining of workpieces made from heat-resistant alloy AISI N07263, the highest possible productivity will be achieved with the following parameters: cutting speed v = 22.1 m/min., feed rate s=0.26 mm/rev; tool life T = 18 min.; net cost – 2.45 per hour.

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge.

PubMed

Najeeb, Najeeb W; Detweiler, Carrick

2017-07-17

When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes' power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values.

Extending Wireless Rechargeable Sensor Network Life without Full Knowledge

PubMed Central

Najeeb, Najeeb W.; Detweiler, Carrick

2017-01-01

When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes’ power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values. PMID:28714936

External validation of the international risk prediction algorithm for major depressive episode in the US general population: the PredictD-US study.

PubMed

Nigatu, Yeshambel T; Liu, Yan; Wang, JianLi

2016-07-22

Multivariable risk prediction algorithms are useful for making clinical decisions and for health planning. While prediction algorithms for new onset of major depression in the primary care attendees in Europe and elsewhere have been developed, the performance of these algorithms in different populations is not known. The objective of this study was to validate the PredictD algorithm for new onset of major depressive episode (MDE) in the US general population. Longitudinal study design was conducted with approximate 3-year follow-up data from a nationally representative sample of the US general population. A total of 29,621 individuals who participated in Wave 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and who did not have an MDE in the past year at Wave 1 were included. The PredictD algorithm was directly applied to the selected participants. MDE was assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule, based on the DSM-IV criteria. Among the participants, 8 % developed an MDE over three years. The PredictD algorithm had acceptable discriminative power (C-statistics = 0.708, 95 % CI: 0.696, 0.720), but poor calibration (p < 0.001) with the NESARC data. In the European primary care attendees, the algorithm had a C-statistics of 0.790 (95 % CI: 0.767, 0.813) with a perfect calibration. The PredictD algorithm has acceptable discrimination, but the calibration capacity was poor in the US general population despite of re-calibration. Therefore, based on the results, at current stage, the use of PredictD in the US general population for predicting individual risk of MDE is not encouraged. More independent validation research is needed.

Zsyntax: a formal language for molecular biology with projected applications in text mining and biological prediction.

PubMed

Boniolo, Giovanni; D'Agostino, Marcello; Di Fiore, Pier Paolo

2010-03-03

We propose a formal language that allows for transposing biological information precisely and rigorously into machine-readable information. This language, which we call Zsyntax (where Z stands for the Greek word zetaomegaeta, life), is grounded on a particular type of non-classical logic, and it can be used to write algorithms and computer programs. We present it as a first step towards a comprehensive formal language for molecular biology in which any biological process can be written and analyzed as a sort of logical "deduction". Moreover, we illustrate the potential value of this language, both in the field of text mining and in that of biological prediction.

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

PubMed

Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

2005-09-01

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

Fast Demand Forecast of Electric Vehicle Charging Stations for Cell Phone Application

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

Majidpour, Mostafa; Qiu, Charlie; Chung, Ching-Yen

This paper describes the core cellphone application algorithm which has been implemented for the prediction of energy consumption at Electric Vehicle (EV) Charging Stations at UCLA. For this interactive user application, the total time of accessing database, processing the data and making the prediction, needs to be within a few seconds. We analyze four relatively fast Machine Learning based time series prediction algorithms for our prediction engine: Historical Average, kNearest Neighbor, Weighted k-Nearest Neighbor, and Lazy Learning. The Nearest Neighbor algorithm (k Nearest Neighbor with k=1) shows better performance and is selected to be the prediction algorithm implemented for themore » cellphone application. Two applications have been designed on top of the prediction algorithm: one predicts the expected available energy at the station and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is about one second for both applications.« less

Recent developments of the NESSUS probabilistic structural analysis computer program

NASA Technical Reports Server (NTRS)

Millwater, H.; Wu, Y.-T.; Torng, T.; Thacker, B.; Riha, D.; Leung, C. P.

1992-01-01

The NESSUS probabilistic structural analysis computer program combines state-of-the-art probabilistic algorithms with general purpose structural analysis methods to compute the probabilistic response and the reliability of engineering structures. Uncertainty in loading, material properties, geometry, boundary conditions and initial conditions can be simulated. The structural analysis methods include nonlinear finite element and boundary element methods. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. The scope of the code has recently been expanded to include probabilistic life and fatigue prediction of structures in terms of component and system reliability and risk analysis of structures considering cost of failure. The code is currently being extended to structural reliability considering progressive crack propagation. Several examples are presented to demonstrate the new capabilities.

Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Jadaan, Osama J.; Gyekenyesi, John P.

2005-01-01

An analytical methodology is developed to predict the probability of survival (reliability) of ceramic components subjected to harsh thermomechanical loads that can vary with time (transient reliability analysis). This capability enables more accurate prediction of ceramic component integrity against fracture in situations such as turbine startup and shutdown, operational vibrations, atmospheric reentry, or other rapid heating or cooling situations (thermal shock). The transient reliability analysis methodology developed herein incorporates the following features: fast-fracture transient analysis (reliability analysis without slow crack growth, SCG); transient analysis with SCG (reliability analysis with time-dependent damage due to SCG); a computationally efficient algorithm to compute the reliability for components subjected to repeated transient loading (block loading); cyclic fatigue modeling using a combined SCG and Walker fatigue law; proof testing for transient loads; and Weibull and fatigue parameters that are allowed to vary with temperature or time. Component-to-component variation in strength (stochastic strength response) is accounted for with the Weibull distribution, and either the principle of independent action or the Batdorf theory is used to predict the effect of multiaxial stresses on reliability. The reliability analysis can be performed either as a function of the component surface (for surface-distributed flaws) or component volume (for volume-distributed flaws). The transient reliability analysis capability has been added to the NASA CARES/ Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. CARES/Life was also updated to interface with commercially available finite element analysis software, such as ANSYS, when used to model the effects of transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

Modeling of thermo-mechanical and irradiation behavior of mixed oxide fuel for sodium fast reactors

NASA Astrophysics Data System (ADS)

Karahan, Aydın; Buongiorno, Jacopo

2010-01-01

An engineering code to model the irradiation behavior of UO2-PuO2 mixed oxide fuel pins in sodium-cooled fast reactors was developed. The code was named fuel engineering and structural analysis tool (FEAST-OXIDE). FEAST-OXIDE has several modules working in coupled form with an explicit numerical algorithm. These modules describe: (1) fission gas release and swelling, (2) fuel chemistry and restructuring, (3) temperature distribution, (4) fuel-clad chemical interaction and (5) fuel-clad mechanical analysis. Given the fuel pin geometry, composition and irradiation history, FEAST-OXIDE can analyze fuel and cladding thermo-mechanical behavior at both steady-state and design-basis transient scenarios. The code was written in FORTRAN-90 program language. The mechanical analysis module implements the LIFE algorithm. Fission gas release and swelling behavior is described by the OGRES and NEFIG models. However, the original OGRES model has been extended to include the effects of joint oxide gain (JOG) formation on fission gas release and swelling. A detailed fuel chemistry model has been included to describe the cesium radial migration and JOG formation, oxygen and plutonium radial distribution and the axial migration of cesium. The fuel restructuring model includes the effects of as-fabricated porosity migration, irradiation-induced fuel densification, grain growth, hot pressing and fuel cracking and relocation. Finally, a kinetics model is included to predict the clad wastage formation. FEAST-OXIDE predictions have been compared to the available FFTF, EBR-II and JOYO databases, as well as the LIFE-4 code predictions. The agreement was found to be satisfactory for steady-state and slow-ramp over-power accidents.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.

PubMed

Kroenke, Candyce H; Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J

2016-03-01

The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A review of predictive coding algorithms.

PubMed

Spratling, M W

2017-03-01

Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term "predictive coding". This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology. Copyright © 2016 Elsevier Inc. All rights reserved.

A Robustly Stabilizing Model Predictive Control Algorithm

NASA Technical Reports Server (NTRS)

Ackmece, A. Behcet; Carson, John M., III

2007-01-01

A model predictive control (MPC) algorithm that differs from prior MPC algorithms has been developed for controlling an uncertain nonlinear system. This algorithm guarantees the resolvability of an associated finite-horizon optimal-control problem in a receding-horizon implementation.

Basecalling with LifeTrace

PubMed Central

Walther, Dirk; Bartha, Gábor; Morris, Macdonald

2001-01-01

A pivotal step in electrophoresis sequencing is the conversion of the raw, continuous chromatogram data into the actual sequence of discrete nucleotides, a process referred to as basecalling. We describe a novel algorithm for basecalling implemented in the program LifeTrace. Like Phred, currently the most widely used basecalling software program, LifeTrace takes processed trace data as input. It was designed to be tolerant to variable peak spacing by means of an improved peak-detection algorithm that emphasizes local chromatogram information over global properties. LifeTrace is shown to generate high-quality basecalls and reliable quality scores. It proved particularly effective when applied to MegaBACE capillary sequencing machines. In a benchmark test of 8372 dye-primer MegaBACE chromatograms, LifeTrace generated 17% fewer substitution errors, 16% fewer insertion/deletion errors, and 2.4% more aligned bases to the finished sequence than did Phred. For two sets totaling 6624 dye-terminator chromatograms, the performance improvement was 15% fewer substitution errors, 10% fewer insertion/deletion errors, and 2.1% more aligned bases. The processing time required by LifeTrace is comparable to that of Phred. The predicted quality scores were in line with observed quality scores, permitting direct use for quality clipping and in silico single nucleotide polymorphism (SNP) detection. Furthermore, we introduce a new type of quality score associated with every basecall: the gap-quality. It estimates the probability of a deletion error between the current and the following basecall. This additional quality score improves detection of single basepair deletions when used for locating potential basecalling errors during the alignment. We also describe a new protocol for benchmarking that we believe better discerns basecaller performance differences than methods previously published. PMID:11337481

Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

2016-07-01

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

Is procalcitonin-guided antimicrobial use cost-effective in adult patients with suspected bacterial infection and sepsis?

PubMed

Harrison, Michelle; Collins, Curtis D

2015-03-01

Procalcitonin has emerged as a promising biomarker of bacterial infection. Published literature demonstrates that use of procalcitonin testing and an associated treatment pathway reduces duration of antibiotic therapy without impacting mortality. The objective of this study was to determine the financial impact of utilizing a procalcitonin-guided treatment algorithm in hospitalized patients with sepsis. Cost-minimization and cost-utility analysis. Hypothetical cohort of adult ICU patients with suspected bacterial infection and sepsis. Utilizing published clinical and economic data, a decision analytic model was developed from the U.S. hospital perspective. Effectiveness and utility measures were defined using cost-per-clinical episode and cost per quality-adjusted life years (QALYs). Upper and lower sensitivity ranges were determined for all inputs. Univariate and probabilistic sensitivity analyses assessed the robustness of our model and variables. Incremental cost-effectiveness ratios (ICERs) were calculated and compared to predetermined willingness-to-pay thresholds. Base-case results predicted the use of a procalcitonin-guided treatment algorithm dominated standard care with improved quality (0.0002 QALYs) and decreased overall treatment costs ($65). The model was sensitive to a number of key variables that had the potential to impact results, including algorithm adherence (<42.3%), number and cost of procalcitonin tests ordered (≥9 and >$46), days of antimicrobial reduction (<1.6 d), incidence of nephrotoxicity and rate of nephrotoxicity reduction. The combination of procalcitonin testing with an evidence-based treatment algorithm may improve patients' quality of life while decreasing costs in ICU patients with suspected bacterial infection and sepsis; however, results were highly dependent on a number of variables and assumptions.

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39).

PubMed

Borchani, Hanen; Bielza, Concha; Martı Nez-Martı N, Pablo; Larrañaga, Pedro

2012-12-01

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson's patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson's disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables. Copyright © 2012 Elsevier Inc. All rights reserved.

Can human experts predict solubility better than computers?

PubMed

Boobier, Samuel; Osbourn, Anne; Mitchell, John B O

2017-12-13

In this study, we design and carry out a survey, asking human experts to predict the aqueous solubility of druglike organic compounds. We investigate whether these experts, drawn largely from the pharmaceutical industry and academia, can match or exceed the predictive power of algorithms. Alongside this, we implement 10 typical machine learning algorithms on the same dataset. The best algorithm, a variety of neural network known as a multi-layer perceptron, gave an RMSE of 0.985 log S units and an R 2 of 0.706. We would not have predicted the relative success of this particular algorithm in advance. We found that the best individual human predictor generated an almost identical prediction quality with an RMSE of 0.942 log S units and an R 2 of 0.723. The collection of algorithms contained a higher proportion of reasonably good predictors, nine out of ten compared with around half of the humans. We found that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median generated excellent predictivity. While our consensus human predictor achieved very slightly better headline figures on various statistical measures, the difference between it and the consensus machine learning predictor was both small and statistically insignificant. We conclude that human experts can predict the aqueous solubility of druglike molecules essentially equally well as machine learning algorithms. We find that, for either humans or algorithms, combining individual predictions into a consensus predictor by taking their median is a powerful way of benefitting from the wisdom of crowds.

Discrete sequence prediction and its applications

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We apply sequence prediction using a simple and practical sequence-prediction algorithm, called TDAG. The TDAG algorithm is first tested by comparing its performance with some common data compression algorithms. Then it is adapted to the detailed requirements of dynamic program optimization, with excellent results.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data

PubMed Central

Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J.

2016-01-01

Abstract Background: The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. Methods: We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women’s Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms—one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV—using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this “triangulation.” Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. Results: The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Conclusions: Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. PMID:26582243

Maximum life spur gear design

NASA Technical Reports Server (NTRS)

Savage, M.; Mackulin, M. J.; Coe, H. H.; Coy, J. J.

1991-01-01

Optimization procedures allow one to design a spur gear reduction for maximum life and other end use criteria. A modified feasible directions search algorithm permits a wide variety of inequality constraints and exact design requirements to be met with low sensitivity to initial guess values. The optimization algorithm is described, and the models for gear life and performance are presented. The algorithm is compact and has been programmed for execution on a desk top computer. Two examples are presented to illustrate the method and its application.

Application of simple mathematical expressions to relate the half-lives of xenobiotics in rats to values in humans.

PubMed

Ward, Keith W; Erhardt, Paul; Bachmann, Kenneth

2005-01-01

Previous publications from GlaxoSmithKline and University of Toledo laboratories convey our independent attempts to predict the half-lives of xenobiotics in humans using data obtained from rats. The present investigation was conducted to compare the performance of our published models against a common dataset obtained by merging the two sets of rat versus human half-life (hHL) data previously used by each laboratory. After combining data, mathematical analyses were undertaken by deploying both of our previous models, namely the use of an empirical algorithm based on a best-fit model and the use of rat-to-human liver blood flow ratios as a half-life correction factor. Both qualitative and quantitative analyses were performed, as well as evaluation of the impact of molecular properties on predictability. The merged dataset was remarkably diverse with respect to physiochemical and pharmacokinetic (PK) properties. Application of both models revealed similar predictability, depending upon the measure of stipulated accuracy. Certain molecular features, particularly rotatable bond count and pK(a), appeared to influence the accuracy of prediction. This collaborative effort has resulted in an improved understanding and appreciation of the value of rats to serve as a surrogate for the prediction of xenobiotic half-lives in humans when clinical pharmacokinetic studies are not possible or practicable.

The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

PubMed

Cui, Zaixu; Gong, Gaolang

2018-06-02

Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of behavior/cognition and offer important guidance for choosing the ML regression algorithm or sample size in relevant investigations. Copyright © 2018 Elsevier Inc. All rights reserved.

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

PubMed Central

Chiroma, Haruna; Abdul-kareem, Sameem; Khan, Abdullah; Nawi, Nazri Mohd.; Gital, Abdulsalam Ya’u; Shuib, Liyana; Abubakar, Adamu I.; Rahman, Muhammad Zubair; Herawan, Tutut

2015-01-01

Background Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. Methods/Findings The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. Conclusion An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper. PMID:26305483

A prediction algorithm for first onset of major depression in the general population: development and validation.

PubMed

Wang, JianLi; Sareen, Jitender; Patten, Scott; Bolton, James; Schmitz, Norbert; Birney, Arden

2014-05-01

Prediction algorithms are useful for making clinical decisions and for population health planning. However, such prediction algorithms for first onset of major depression do not exist. The objective of this study was to develop and validate a prediction algorithm for first onset of major depression in the general population. Longitudinal study design with approximate 3-year follow-up. The study was based on data from a nationally representative sample of the US general population. A total of 28 059 individuals who participated in Waves 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions and who had not had major depression at Wave 1 were included. The prediction algorithm was developed using logistic regression modelling in 21 813 participants from three census regions. The algorithm was validated in participants from the 4th census region (n=6246). Major depression occurred since Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions, assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-diagnostic and statistical manual for mental disorders IV. A prediction algorithm containing 17 unique risk factors was developed. The algorithm had good discriminative power (C statistics=0.7538, 95% CI 0.7378 to 0.7699) and excellent calibration (F-adjusted test=1.00, p=0.448) with the weighted data. In the validation sample, the algorithm had a C statistic of 0.7259 and excellent calibration (Hosmer-Lemeshow χ(2)=3.41, p=0.906). The developed prediction algorithm has good discrimination and calibration capacity. It can be used by clinicians, mental health policy-makers and service planners and the general public to predict future risk of having major depression. The application of the algorithm may lead to increased personalisation of treatment, better clinical decisions and more optimal mental health service planning.

A Battery Health Monitoring Framework for Planetary Rovers

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Kulkarni, Chetan Shrikant

2014-01-01

Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computaitonally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire

PubMed Central

Abascal-Bolado, Beatriz; Novotny, Paul J; Sloan, Jeff A; Karpman, Craig; Dulohery, Megan M; Benzo, Roberto P

2015-01-01

Purpose Forecasting hospitalization in patients with COPD has gained significant interest in the field of COPD care. There is a need to find simple tools that can help clinicians to stratify the risk of hospitalization in these patients at the time of care. The perception of quality of life has been reported to be independently associated with hospitalizations, but questionnaires are impractical for daily clinical use. Individual questions from valid questionnaires can have robust predictive abilities, as has been suggested in previous reports, as a way to use patient-reported outcomes to forecast important events like hospitalizations in COPD. Our primary aim was to assess the predictive value of individual questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS) on the risk of hospitalization and to develop a clinically relevant and simple algorithm that clinicians can use in routine practice to identify patients with an increased risk of hospitalization. Patients and methods A total of 493 patients with COPD prospectively recruited from an outpatient pulmonary clinic completed the CRQ-SAS, demographic information, pulmonary function testing, and clinical outcomes. The cohort had a mean age of 70 years, was 54% male, with forced expiratory volume in 1 second percentage predicted 42.8±16.7, and modified Medical Research Council dyspnea scale score of 2±1.13. Results Our analysis validated the original CRQ-SAS domains. Importantly, recursive partitioning analysis identified three CRQ-SAS items regarding fear or panic of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms that were highly predictive of hospitalization. We propose a robust (area under the curve =0.70) but short and easy algorithm for daily clinical care to forecast hospitalizations in patients with COPD. Conclusion We identified three themes – fear of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms – as important patient-reported outcomes to predict hospitalizations, and propose a short and easy algorithm to forecast hospitalizations in patients with COPD. PMID:26543362

Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire.

PubMed

Abascal-Bolado, Beatriz; Novotny, Paul J; Sloan, Jeff A; Karpman, Craig; Dulohery, Megan M; Benzo, Roberto P

2015-01-01

Forecasting hospitalization in patients with COPD has gained significant interest in the field of COPD care. There is a need to find simple tools that can help clinicians to stratify the risk of hospitalization in these patients at the time of care. The perception of quality of life has been reported to be independently associated with hospitalizations, but questionnaires are impractical for daily clinical use. Individual questions from valid questionnaires can have robust predictive abilities, as has been suggested in previous reports, as a way to use patient-reported outcomes to forecast important events like hospitalizations in COPD. Our primary aim was to assess the predictive value of individual questions from the Chronic Respiratory Questionnaire Self-Assessment Survey (CRQ-SAS) on the risk of hospitalization and to develop a clinically relevant and simple algorithm that clinicians can use in routine practice to identify patients with an increased risk of hospitalization. A total of 493 patients with COPD prospectively recruited from an outpatient pulmonary clinic completed the CRQ-SAS, demographic information, pulmonary function testing, and clinical outcomes. The cohort had a mean age of 70 years, was 54% male, with forced expiratory volume in 1 second percentage predicted 42.8±16.7, and modified Medical Research Council dyspnea scale score of 2±1.13. Our analysis validated the original CRQ-SAS domains. Importantly, recursive partitioning analysis identified three CRQ-SAS items regarding fear or panic of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms that were highly predictive of hospitalization. We propose a robust (area under the curve =0.70) but short and easy algorithm for daily clinical care to forecast hospitalizations in patients with COPD. We identified three themes - fear of breathlessness, dyspnea with basic activities of daily living, and depressive symptoms - as important patient-reported outcomes to predict hospitalizations, and propose a short and easy algorithm to forecast hospitalizations in patients with COPD.

Advanced Ground Systems Maintenance Prognostics Project

NASA Technical Reports Server (NTRS)

Perotti, Jose M.

2015-01-01

The project implements prognostics capabilities to predict when a component system or subsystem will no longer meet desired functional or performance criteria, called the end of life. The capability also provides an assessment of the remaining useful life of a hardware component. The project enables the delivery of system health advisories to ground system operators. This project will use modeling techniques and algorithms to assess components' health andpredict remaining life for such components. The prognostics capability being developed will beused:during the design phase and during pre/post operations to conduct planning and analysis ofsystem design, maintenance & logistics plans, and system/mission operations plansduring real-time operations to monitor changes to components' health and assess their impacton operations.This capability will be interfaced to Ground Operations' command and control system as a part ofthe AGSM project to help assure system availability and mission success. The initial modelingeffort for this capability will be developed for Liquid Oxygen ground loading applications.

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

Algorithms, complexity, and the sciences

PubMed Central

Papadimitriou, Christos

2014-01-01

Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution. PMID:25349382

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Zhang, Li-jie

2017-10-01

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

Can we derive an 'exchange rate' between descriptive and preference-based outcome measures for stroke? Results from the transfer to utility (TTU) technique

PubMed Central

Mortimer, Duncan; Segal, Leonie; Sturm, Jonathan

2009-01-01

Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. Methods We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Results Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. Conclusion While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. PMID:19371444

Predicting functional decline and survival in amyotrophic lateral sclerosis.

PubMed

Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

2017-01-01

Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

Mechanobiological simulations of peri-acetabular bone ingrowth: a comparative analysis of cell-phenotype specific and phenomenological algorithms.

PubMed

Mukherjee, Kaushik; Gupta, Sanjay

2017-03-01

Several mechanobiology algorithms have been employed to simulate bone ingrowth around porous coated implants. However, there is a scarcity of quantitative comparison between the efficacies of commonly used mechanoregulatory algorithms. The objectives of this study are: (1) to predict peri-acetabular bone ingrowth using cell-phenotype specific algorithm and to compare these predictions with those obtained using phenomenological algorithm and (2) to investigate the influences of cellular parameters on bone ingrowth. The variation in host bone material property and interfacial micromotion of the implanted pelvis were mapped onto the microscale model of implant-bone interface. An overall variation of 17-88 % in peri-acetabular bone ingrowth was observed. Despite differences in predicted tissue differentiation patterns during the initial period, both the algorithms predicted similar spatial distribution of neo-tissue layer, after attainment of equilibrium. Results indicated that phenomenological algorithm, being computationally faster than the cell-phenotype specific algorithm, might be used to predict peri-prosthetic bone ingrowth. The cell-phenotype specific algorithm, however, was found to be useful in numerically investigating the influence of alterations in cellular activities on bone ingrowth, owing to biologically related factors. Amongst the host of cellular activities, matrix production rate of bone tissue was found to have predominant influence on peri-acetabular bone ingrowth.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

PubMed Central

Roetker, Nicholas S.; Yonker, James A.; Chang, Vicky; Roan, Carol L.; Herd, Pamela; Hauser, Taissa S.; Hauser, Robert M.

2013-01-01

Objectives. We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. Methods. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors—13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors—18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. Results. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. Conclusions. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic–environmental–sociobehavioral interactions in depressive symptoms. PMID:23927508

pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring.

PubMed

Jiménez-García, Brian; Pons, Carles; Fernández-Recio, Juan

2013-07-01

pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock. Supplementary data are available at Bioinformatics online.

Improving personalized link prediction by hybrid diffusion

NASA Astrophysics Data System (ADS)

Liu, Jin-Hu; Zhu, Yu-Xiao; Zhou, Tao

2016-04-01

Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which each individual will get equal number of diversiform predictions. While the performances of many classical algorithms are not satisfactory under this framework, thus new algorithms are in urgent need. Motivated by previous researches in other fields, we generalize heat conduction process to the framework of personalized link prediction and find that this method outperforms many classical similarity-based algorithms, especially in the performance of diversity. In addition, we demonstrate that adding one ground node that is supposed to connect all the nodes in the system will greatly benefit the performance of heat conduction. Finally, better hybrid algorithms composed of local random walk and heat conduction have been proposed. Numerical results show that the hybrid algorithms can outperform other algorithms simultaneously in all four adopted metrics: AUC, precision, recall and hamming distance. In a word, this work may shed some light on the in-depth understanding of the effect of physical processes in personalized link prediction.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Predicting Vasovagal Syncope from Heart Rate and Blood Pressure: A Prospective Study in 140 Subjects.

PubMed

Virag, Nathalie; Erickson, Mark; Taraborrelli, Patricia; Vetter, Rolf; Lim, Phang Boon; Sutton, Richard

2018-04-28

We developed a vasovagal syncope (VVS) prediction algorithm for use during head-up tilt with simultaneous analysis of heart rate (HR) and systolic blood pressure (SBP). We previously tested this algorithm retrospectively in 1155 subjects, showing sensitivity 95%, specificity 93% and median prediction time of 59s. This study was prospective, single center, on 140 subjects to evaluate this VVS prediction algorithm and assess if retrospective results were reproduced and clinically relevant. Primary endpoint was VVS prediction: sensitivity and specificity >80%. In subjects, referred for 60° head-up tilt (Italian protocol), non-invasive HR and SBP were supplied to the VVS prediction algorithm: simultaneous analysis of RR intervals, SBP trends and their variability represented by low-frequency power generated cumulative risk which was compared with a predetermined VVS risk threshold. When cumulative risk exceeded threshold, an alert was generated. Prediction time was duration between first alert and syncope. Of 140 subjects enrolled, data was usable for 134. Of 83 tilt+ve (61.9%), 81 VVS events were correctly predicted and of 51 tilt-ve subjects (38.1%), 45 were correctly identified as negative by the algorithm. Resulting algorithm performance was sensitivity 97.6%, specificity 88.2%, meeting primary endpoint. Mean VVS prediction time was 2min 26s±3min16s with median 1min 25s. Using only HR and HR variability (without SBP) the mean prediction time reduced to 1min34s±1min45s with median 1min13s. The VVS prediction algorithm, is clinically-relevant tool and could offer applications including providing a patient alarm, shortening tilt-test time, or triggering pacing intervention in implantable devices. Copyright © 2018. Published by Elsevier Inc.

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

PubMed

Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

2016-04-01

Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008-2012.

PubMed

Spreco, A; Eriksson, O; Dahlström, Ö; Timpka, T

2017-07-01

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for 'nowcasting' (integrated detection and prediction) of influenza activity are warranted.

NWRA AVOSS Wake Vortex Prediction Algorithm. 3.1.1

NASA Technical Reports Server (NTRS)

Robins, R. E.; Delisi, D. P.; Hinton, David (Technical Monitor)

2002-01-01

This report provides a detailed description of the wake vortex prediction algorithm used in the Demonstration Version of NASA's Aircraft Vortex Spacing System (AVOSS). The report includes all equations used in the algorithm, an explanation of how to run the algorithm, and a discussion of how the source code for the algorithm is organized. Several appendices contain important supplementary information, including suggestions for enhancing the algorithm and results from test cases.

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

NASA Technical Reports Server (NTRS)

Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

PubMed

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Use of four next-generation sequencing platforms to determine HIV-1 coreceptor tropism.

PubMed

Archer, John; Weber, Jan; Henry, Kenneth; Winner, Dane; Gibson, Richard; Lee, Lawrence; Paxinos, Ellen; Arts, Eric J; Robertson, David L; Mimms, Larry; Quiñones-Mateu, Miguel E

2012-01-01

HIV-1 coreceptor tropism assays are required to rule out the presence of CXCR4-tropic (non-R5) viruses prior treatment with CCR5 antagonists. Phenotypic (e.g., Trofile™, Monogram Biosciences) and genotypic (e.g., population sequencing linked to bioinformatic algorithms) assays are the most widely used. Although several next-generation sequencing (NGS) platforms are available, to date all published deep sequencing HIV-1 tropism studies have used the 454™ Life Sciences/Roche platform. In this study, HIV-1 co-receptor usage was predicted for twelve patients scheduled to start a maraviroc-based antiretroviral regimen. The V3 region of the HIV-1 env gene was sequenced using four NGS platforms: 454™, PacBio® RS (Pacific Biosciences), Illumina®, and Ion Torrent™ (Life Technologies). Cross-platform variation was evaluated, including number of reads, read length and error rates. HIV-1 tropism was inferred using Geno2Pheno, Web PSSM, and the 11/24/25 rule and compared with Trofile™ and virologic response to antiretroviral therapy. Error rates related to insertions/deletions (indels) and nucleotide substitutions introduced by the four NGS platforms were low compared to the actual HIV-1 sequence variation. Each platform detected all major virus variants within the HIV-1 population with similar frequencies. Identification of non-R5 viruses was comparable among the four platforms, with minor differences attributable to the algorithms used to infer HIV-1 tropism. All NGS platforms showed similar concordance with virologic response to the maraviroc-based regimen (75% to 80% range depending on the algorithm used), compared to Trofile (80%) and population sequencing (70%). In conclusion, all four NGS platforms were able to detect minority non-R5 variants at comparable levels suggesting that any NGS-based method can be used to predict HIV-1 coreceptor usage.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

PubMed

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

An improved reversible data hiding algorithm based on modification of prediction errors

NASA Astrophysics Data System (ADS)

Jafar, Iyad F.; Hiary, Sawsan A.; Darabkh, Khalid A.

2014-04-01

Reversible data hiding algorithms are concerned with the ability of hiding data and recovering the original digital image upon extraction. This issue is of interest in medical and military imaging applications. One particular class of such algorithms relies on the idea of histogram shifting of prediction errors. In this paper, we propose an improvement over one popular algorithm in this class. The improvement is achieved by employing a different predictor, the use of more bins in the prediction error histogram in addition to multilevel embedding. The proposed extension shows significant improvement over the original algorithm and its variations.

An accelerated non-Gaussianity based multichannel predictive deconvolution method with the limited supporting region of filters

NASA Astrophysics Data System (ADS)

Li, Zhong-xiao; Li, Zhen-chun

2016-09-01

The multichannel predictive deconvolution can be conducted in overlapping temporal and spatial data windows to solve the 2D predictive filter for multiple removal. Generally, the 2D predictive filter can better remove multiples at the cost of more computation time compared with the 1D predictive filter. In this paper we first use the cross-correlation strategy to determine the limited supporting region of filters where the coefficients play a major role for multiple removal in the filter coefficient space. To solve the 2D predictive filter the traditional multichannel predictive deconvolution uses the least squares (LS) algorithm, which requires primaries and multiples are orthogonal. To relax the orthogonality assumption the iterative reweighted least squares (IRLS) algorithm and the fast iterative shrinkage thresholding (FIST) algorithm have been used to solve the 2D predictive filter in the multichannel predictive deconvolution with the non-Gaussian maximization (L1 norm minimization) constraint of primaries. The FIST algorithm has been demonstrated as a faster alternative to the IRLS algorithm. In this paper we introduce the FIST algorithm to solve the filter coefficients in the limited supporting region of filters. Compared with the FIST based multichannel predictive deconvolution without the limited supporting region of filters the proposed method can reduce the computation burden effectively while achieving a similar accuracy. Additionally, the proposed method can better balance multiple removal and primary preservation than the traditional LS based multichannel predictive deconvolution and FIST based single channel predictive deconvolution. Synthetic and field data sets demonstrate the effectiveness of the proposed method.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

A Comparison of Filter-based Approaches for Model-based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Saha, Bhaskar; Goebel, Kai

2012-01-01

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

PubMed

Ergen, Tolga; Kozat, Suleyman Serdar

2017-09-13

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation

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

Hines, J. Wesley; Upadhyaya, Belle; Sharp, Michael

On-line monitoring and tracking of nuclear plant system and component degradation is being investigated as a method for improving the safety, reliability, and maintainability of aging nuclear power plants. Accurate prediction of the current degradation state of system components and structures is important for accurate estimates of their remaining useful life (RUL). The correct quantification and propagation of both the measurement uncertainty and model uncertainty is necessary for quantifying the uncertainty of the RUL prediction. This research project developed and validated methods to perform RUL estimation throughout the lifecycle of plant components. Prognostic methods should seamlessly operate from beginning ofmore » component life (BOL) to end of component life (EOL). We term this "Lifecycle Prognostics." When a component is put into use, the only information available may be past failure times of similar components used in similar conditions, and the predicted failure distribution can be estimated with reliability methods such as Weibull Analysis (Type I Prognostics). As the component operates, it begins to degrade and consume its available life. This life consumption may be a function of system stresses, and the failure distribution should be updated to account for the system operational stress levels (Type II Prognostics). When degradation becomes apparent, this information can be used to again improve the RUL estimate (Type III Prognostics). This research focused on developing prognostics algorithms for the three types of prognostics, developing uncertainty quantification methods for each of the algorithms, and, most importantly, developing a framework using Bayesian methods to transition between prognostic model types and update failure distribution estimates as new information becomes available. The developed methods were then validated on a range of accelerated degradation test beds. The ultimate goal of prognostics is to provide an accurate assessment for RUL predictions, with as little uncertainty as possible. From a reliability and maintenance standpoint, there would be improved safety by avoiding all failures. Calculated risk would decrease, saving money by avoiding unnecessary maintenance. One major bottleneck for data-driven prognostics is the availability of run-to-failure degradation data. Without enough degradation data leading to failure, prognostic models can yield RUL distributions with large uncertainty or mathematically unsound predictions. To address these issues a "Lifecycle Prognostics" method was developed to create RUL distributions from Beginning of Life (BOL) to End of Life (EOL). This employs established Type I, II, and III prognostic methods, and Bayesian transitioning between each Type. Bayesian methods, as opposed to classical frequency statistics, show how an expected value, a priori, changes with new data to form a posterior distribution. For example, when you purchase a component you have a prior belief, or estimation, of how long it will operate before failing. As you operate it, you may collect information related to its condition that will allow you to update your estimated failure time. Bayesian methods are best used when limited data are available. The use of a prior also means that information is conserved when new data are available. The weightings of the prior belief and information contained in the sampled data are dependent on the variance (uncertainty) of the prior, the variance (uncertainty) of the data, and the amount of measured data (number of samples). If the variance of the prior is small compared to the uncertainty of the data, the prior will be weighed more heavily. However, as more data are collected, the data will be weighted more heavily and will eventually swamp out the prior in calculating the posterior distribution of model parameters. Fundamentally Bayesian analysis updates a prior belief with new data to get a posterior belief. The general approach to applying the Bayesian method to lifecycle prognostics consisted of identifying the prior, which is the RUL estimate and uncertainty from the previous prognostics type, and combining it with observational data related to the newer prognostics type. The resulting lifecycle prognostics algorithm uses all available information throughout the component lifecycle.« less

Electrochemistry-based Battery Modeling for Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Kulkarni, Chetan Shrikant

2013-01-01

Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.

PubMed

Sasaki, Satoshi; Comber, Alexis J; Suzuki, Hiroshi; Brunsdon, Chris

2010-01-28

Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations. Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared. The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes.

PubMed

Hung, Andrew J; Chen, Jian; Che, Zhengping; Nilanon, Tanachat; Jarc, Anthony; Titus, Micha; Oh, Paul J; Gill, Inderbir S; Liu, Yan

2018-05-01

Surgical performance is critical for clinical outcomes. We present a novel machine learning (ML) method of processing automated performance metrics (APMs) to evaluate surgical performance and predict clinical outcomes after robot-assisted radical prostatectomy (RARP). We trained three ML algorithms utilizing APMs directly from robot system data (training material) and hospital length of stay (LOS; training label) (≤2 days and >2 days) from 78 RARP cases, and selected the algorithm with the best performance. The selected algorithm categorized the cases as "Predicted as expected LOS (pExp-LOS)" and "Predicted as extended LOS (pExt-LOS)." We compared postoperative outcomes of the two groups (Kruskal-Wallis/Fisher's exact tests). The algorithm then predicted individual clinical outcomes, which we compared with actual outcomes (Spearman's correlation/Fisher's exact tests). Finally, we identified five most relevant APMs adopted by the algorithm during predicting. The "Random Forest-50" (RF-50) algorithm had the best performance, reaching 87.2% accuracy in predicting LOS (73 cases as "pExp-LOS" and 5 cases as "pExt-LOS"). The "pExp-LOS" cases outperformed the "pExt-LOS" cases in surgery time (3.7 hours vs 4.6 hours, p = 0.007), LOS (2 days vs 4 days, p = 0.02), and Foley duration (9 days vs 14 days, p = 0.02). Patient outcomes predicted by the algorithm had significant association with the "ground truth" in surgery time (p < 0.001, r = 0.73), LOS (p = 0.05, r = 0.52), and Foley duration (p < 0.001, r = 0.45). The five most relevant APMs, adopted by the RF-50 algorithm in predicting, were largely related to camera manipulation. To our knowledge, ours is the first study to show that APMs and ML algorithms may help assess surgical RARP performance and predict clinical outcomes. With further accrual of clinical data (oncologic and functional data), this process will become increasingly relevant and valuable in surgical assessment and training.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

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

Peyret, Thomas; Poulin, Patrick; Krishnan, Kannan, E-mail: kannan.krishnan@umontreal.ca

The algorithms in the literature focusing to predict tissue:blood PC (P{sub tb}) for environmental chemicals and tissue:plasma PC based on total (K{sub p}) or unbound concentration (K{sub pu}) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P{sub tb}, K{sub p} and K{sub pu} for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such amore » way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P{sub tb}, K{sub p} or K{sub pu} of muscle (n = 174), liver (n = 139) and adipose tissue (n = 141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.« less

Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data.

PubMed

Horwitz, Leora I; Grady, Jacqueline N; Cohen, Dorothy B; Lin, Zhenqiu; Volpe, Mark; Ngo, Chi K; Masica, Andrew L; Long, Theodore; Wang, Jessica; Keenan, Megan; Montague, Julia; Suter, Lisa G; Ross, Joseph S; Drye, Elizabeth E; Krumholz, Harlan M; Bernheim, Susannah M

2015-10-01

It is desirable not to include planned readmissions in readmission measures because they represent deliberate, scheduled care. To develop an algorithm to identify planned readmissions, describe its performance characteristics, and identify improvements. Consensus-driven algorithm development and chart review validation study at 7 acute-care hospitals in 2 health systems. For development, all discharges qualifying for the publicly reported hospital-wide readmission measure. For validation, all qualifying same-hospital readmissions that were characterized by the algorithm as planned, and a random sampling of same-hospital readmissions that were characterized as unplanned. We calculated weighted sensitivity and specificity, and positive and negative predictive values of the algorithm (version 2.1), compared to gold standard chart review. In consultation with 27 experts, we developed an algorithm that characterizes 7.8% of readmissions as planned. For validation we reviewed 634 readmissions. The weighted sensitivity of the algorithm was 45.1% overall, 50.9% in large teaching centers and 40.2% in smaller community hospitals. The weighted specificity was 95.9%, positive predictive value was 51.6%, and negative predictive value was 94.7%. We identified 4 minor changes to improve algorithm performance. The revised algorithm had a weighted sensitivity 49.8% (57.1% at large hospitals), weighted specificity 96.5%, positive predictive value 58.7%, and negative predictive value 94.5%. Positive predictive value was poor for the 2 most common potentially planned procedures: diagnostic cardiac catheterization (25%) and procedures involving cardiac devices (33%). An administrative claims-based algorithm to identify planned readmissions is feasible and can facilitate public reporting of primarily unplanned readmissions. © 2015 Society of Hospital Medicine.

Parametric Bayesian priors and better choice of negative examples improve protein function prediction.

PubMed

Youngs, Noah; Penfold-Brown, Duncan; Drew, Kevin; Shasha, Dennis; Bonneau, Richard

2013-05-01

Computational biologists have demonstrated the utility of using machine learning methods to predict protein function from an integration of multiple genome-wide data types. Yet, even the best performing function prediction algorithms rely on heuristics for important components of the algorithm, such as choosing negative examples (proteins without a given function) or determining key parameters. The improper choice of negative examples, in particular, can hamper the accuracy of protein function prediction. We present a novel approach for choosing negative examples, using a parameterizable Bayesian prior computed from all observed annotation data, which also generates priors used during function prediction. We incorporate this new method into the GeneMANIA function prediction algorithm and demonstrate improved accuracy of our algorithm over current top-performing function prediction methods on the yeast and mouse proteomes across all metrics tested. Code and Data are available at: http://bonneaulab.bio.nyu.edu/funcprop.html

Compressed sensing based missing nodes prediction in temporal communication network

NASA Astrophysics Data System (ADS)

Cheng, Guangquan; Ma, Yang; Liu, Zhong; Xie, Fuli

2018-02-01

The reconstruction of complex network topology is of great theoretical and practical significance. Most research so far focuses on the prediction of missing links. There are many mature algorithms for link prediction which have achieved good results, but research on the prediction of missing nodes has just begun. In this paper, we propose an algorithm for missing node prediction in complex networks. We detect the position of missing nodes based on their neighbor nodes under the theory of compressed sensing, and extend the algorithm to the case of multiple missing nodes using spectral clustering. Experiments on real public network datasets and simulated datasets show that our algorithm can detect the locations of hidden nodes effectively with high precision.

A New Approach to Predict user Mobility Using Semantic Analysis and Machine Learning.

PubMed

Fernandes, Roshan; D'Souza G L, Rio

2017-10-19

Mobility prediction is a technique in which the future location of a user is identified in a given network. Mobility prediction provides solutions to many day-to-day life problems. It helps in seamless handovers in wireless networks to provide better location based services and to recalculate paths in Mobile Ad hoc Networks (MANET). In the present study, a framework is presented which predicts user mobility in presence and absence of mobility history. Naïve Bayesian classification algorithm and Markov Model are used to predict user future location when user mobility history is available. An attempt is made to predict user future location by using Short Message Service (SMS) and instantaneous Geological coordinates in the absence of mobility patterns. The proposed technique compares the performance metrics with commonly used Markov Chain model. From the experimental results it is evident that the techniques used in this work gives better results when considering both spatial and temporal information. The proposed method predicts user's future location in the absence of mobility history quite fairly. The proposed work is applied to predict the mobility of medical rescue vehicles and social security systems.

Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva

2018-04-01

Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.

Appendix F. Developmental enforcement algorithm definition document : predictive braking enforcement algorithm definition document.

DOT National Transportation Integrated Search

2012-05-01

The purpose of this document is to fully define and describe the logic flow and mathematical equations for a predictive braking enforcement algorithm intended for implementation in a Positive Train Control (PTC) system.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A low computation cost method for seizure prediction.

PubMed

Zhang, Yanli; Zhou, Weidong; Yuan, Qi; Wu, Qi

2014-10-01

The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction. Copyright © 2014 Elsevier B.V. All rights reserved.

Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

PubMed

Kautzky, Alexander; Dold, Markus; Bartova, Lucie; Spies, Marie; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Fabbri, Chiara; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD. © Copyright 2017 Physicians Postgraduate Press, Inc.

Model predictive control design for polytopic uncertain systems by synthesising multi-step prediction scenarios

NASA Astrophysics Data System (ADS)

Lu, Jianbo; Xi, Yugeng; Li, Dewei; Xu, Yuli; Gan, Zhongxue

2018-01-01

A common objective of model predictive control (MPC) design is the large initial feasible region, low online computational burden as well as satisfactory control performance of the resulting algorithm. It is well known that interpolation-based MPC can achieve a favourable trade-off among these different aspects. However, the existing results are usually based on fixed prediction scenarios, which inevitably limits the performance of the obtained algorithms. So by replacing the fixed prediction scenarios with the time-varying multi-step prediction scenarios, this paper provides a new insight into improvement of the existing MPC designs. The adopted control law is a combination of predetermined multi-step feedback control laws, based on which two MPC algorithms with guaranteed recursive feasibility and asymptotic stability are presented. The efficacy of the proposed algorithms is illustrated by a numerical example.

Applying data mining techniques to improve diagnosis in neonatal jaundice.

PubMed

Ferreira, Duarte; Oliveira, Abílio; Freitas, Alberto

2012-12-07

Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa--EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

PubMed Central

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. PMID:25180585

A digital prediction algorithm for a single-phase boost PFC

NASA Astrophysics Data System (ADS)

Qing, Wang; Ning, Chen; Weifeng, Sun; Shengli, Lu; Longxing, Shi

2012-12-01

A novel digital control algorithm for digital control power factor correction is presented, which is called the prediction algorithm and has a feature of a higher PF (power factor) with lower total harmonic distortion, and a faster dynamic response with the change of the input voltage or load current. For a certain system, based on the current system state parameters, the prediction algorithm can estimate the track of the output voltage and the inductor current at the next switching cycle and get a set of optimized control sequences to perfectly track the trajectory of input voltage. The proposed prediction algorithm is verified at different conditions, and computer simulation and experimental results under multi-situations confirm the effectiveness of the prediction algorithm. Under the circumstances that the input voltage is in the range of 90-265 V and the load current in the range of 20%-100%, the PF value is larger than 0.998. The startup and the recovery times respectively are about 0.1 s and 0.02 s without overshoot. The experimental results also verify the validity of the proposed method.

Protein docking prediction using predicted protein-protein interface.

PubMed

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Pitch-Learning Algorithm For Speech Encoders

NASA Technical Reports Server (NTRS)

Bhaskar, B. R. Udaya

1988-01-01

Adaptive algorithm detects and corrects errors in sequence of estimates of pitch period of speech. Algorithm operates in conjunction with techniques used to estimate pitch period. Used in such parametric and hybrid speech coders as linear predictive coders and adaptive predictive coders.

Multiple Damage Progression Paths in Model-Based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai Frank

2011-01-01

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Reliability considerations for the total strain range version of strainrange partitioning

NASA Technical Reports Server (NTRS)

Wirsching, P. H.; Wu, Y. T.

1984-01-01

A proposed total strainrange version of strainrange partitioning (SRP) to enhance the manner in which SRP is applied to life prediction is considered with emphasis on how advanced reliability technology can be applied to perform risk analysis and to derive safety check expressions. Uncertainties existing in the design factors associated with life prediction of a component which experiences the combined effects of creep and fatigue can be identified. Examples illustrate how reliability analyses of such a component can be performed when all design factors in the SRP model are random variables reflecting these uncertainties. The Rackwitz-Fiessler and Wu algorithms are used and estimates of the safety index and the probablity of failure are demonstrated for a SRP problem. Methods of analysis of creep-fatigue data with emphasis on procedures for producing synoptic statistics are presented. An attempt to demonstrate the importance of the contribution of the uncertainties associated with small sample sizes (fatique data) to risk estimates is discussed. The procedure for deriving a safety check expression for possible use in a design criteria document is presented.

Adaptive Trajectory Prediction Algorithm for Climbing Flights

NASA Technical Reports Server (NTRS)

Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz

2012-01-01

Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.

An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data.

PubMed

Nidheesh, N; Abdul Nazeer, K A; Ameer, P M

2017-12-01

Clustering algorithms with steps involving randomness usually give different results on different executions for the same dataset. This non-deterministic nature of algorithms such as the K-Means clustering algorithm limits their applicability in areas such as cancer subtype prediction using gene expression data. It is hard to sensibly compare the results of such algorithms with those of other algorithms. The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids. We propose an improved, density based version of K-Means, which involves a novel and systematic method for selecting initial centroids. The key idea of the algorithm is to select data points which belong to dense regions and which are adequately separated in feature space as the initial centroids. We compared the proposed algorithm to a set of eleven widely used single clustering algorithms and a prominent ensemble clustering algorithm which is being used for cancer data classification, based on the performances on a set of datasets comprising ten cancer gene expression datasets. The proposed algorithm has shown better overall performance than the others. There is a pressing need in the Biomedical domain for simple, easy-to-use and more accurate Machine Learning tools for cancer subtype prediction. The proposed algorithm is simple, easy-to-use and gives stable results. Moreover, it provides comparatively better predictions of cancer subtypes from gene expression data. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

PubMed

Nandola, Naresh N; Rivera, Daniel E

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats.

PubMed

De Feo, Vito; Boi, Fabio; Safaai, Houman; Onken, Arno; Panzeri, Stefano; Vato, Alessandro

2017-01-01

Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state. Here we tested this idea by using a bidirectional BMI to investigate the gain in performance arising from using a state-dependent decoding algorithm. This BMI, implemented in anesthetized rats, controlled the movement of a dynamical system using neural activity decoded from motor cortex and fed back to the brain the dynamical system's position by electrically microstimulating somatosensory cortex. We found that using state-dependent algorithms that tracked the dynamics of ongoing activity led to an increase in the amount of information extracted form neural activity by 22%, with a consequently increase in all of the indices measuring the BMI's performance in controlling the dynamical system. This suggests that state-dependent decoding algorithms may be used to enhance BMIs at moderate computational cost.

Assessing the external validity of algorithms to estimate EQ-5D-3L from the WOMAC.

PubMed

Kiadaliri, Aliasghar A; Englund, Martin

2016-10-04

The use of mapping algorithms have been suggested as a solution to predict health utilities when no preference-based measure is included in the study. However, validity and predictive performance of these algorithms are highly variable and hence assessing the accuracy and validity of algorithms before use them in a new setting is of importance. The aim of the current study was to assess the predictive accuracy of three mapping algorithms to estimate the EQ-5D-3L from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) among Swedish people with knee disorders. Two of these algorithms developed using ordinary least squares (OLS) models and one developed using mixture model. The data from 1078 subjects mean (SD) age 69.4 (7.2) years with frequent knee pain and/or knee osteoarthritis from the Malmö Osteoarthritis study in Sweden were used. The algorithms' performance was assessed using mean error, mean absolute error, and root mean squared error. Two types of prediction were estimated for mixture model: weighted average (WA), and conditional on estimated component (CEC). The overall mean was overpredicted by an OLS model and underpredicted by two other algorithms (P < 0.001). All predictions but the CEC predictions of mixture model had a narrower range than the observed scores (22 to 90 %). All algorithms suffered from overprediction for severe health states and underprediction for mild health states with lesser extent for mixture model. While the mixture model outperformed OLS models at the extremes of the EQ-5D-3D distribution, it underperformed around the center of the distribution. While algorithm based on mixture model reflected the distribution of EQ-5D-3L data more accurately compared with OLS models, all algorithms suffered from systematic bias. This calls for caution in applying these mapping algorithms in a new setting particularly in samples with milder knee problems than original sample. Assessing the impact of the choice of these algorithms on cost-effectiveness studies through sensitivity analysis is recommended.

Cost-effectiveness of pharmacogenetics-guided warfarin therapy vs. alternative anticoagulation in atrial fibrillation.

PubMed

Pink, J; Pirmohamed, M; Lane, S; Hughes, D A

2014-02-01

Pharmacogenetics-guided warfarin dosing is an alternative to standard clinical algorithms and new oral anticoagulants for patients with nonvalvular atrial fibrillation. However, clinical evidence for pharmacogenetics-guided warfarin dosing is limited to intermediary outcomes, and consequently, there is a lack of information on the cost-effectiveness of anticoagulation treatment options. A clinical trial simulation of S-warfarin was used to predict times within therapeutic range for different dosing algorithms. Relative risks of clinical events, obtained from a meta-analysis of trials linking times within therapeutic range with outcomes, served as inputs to an economic analysis. Neither dabigatran nor rivaroxaban were cost-effective options. Along the cost-effectiveness frontier, in relation to clinically dosed warfarin, pharmacogenetics-guided warfarin and apixaban had incremental cost-effectiveness ratios of £13,226 and £20,671 per quality-adjusted life year gained, respectively. On the basis of our simulations, apixaban appears to be the most cost-effective treatment.

Identification of coupling direction: Application to cardiorespiratory interaction

NASA Astrophysics Data System (ADS)

Rosenblum, Michael G.; Cimponeriu, Laura; Bezerianos, Anastasios; Patzak, Andreas; Mrowka, Ralf

2002-04-01

We consider the problem of experimental detection of directionality of weak coupling between two self-sustained oscillators from bivariate data. We further develop the method introduced by Rosenblum and Pikovsky [Phys. Rev. E 64, 045202 (2001)], suggesting an alternative approach. Next, we consider another framework for identification of directionality, based on the idea of mutual predictability. Our algorithms provide directionality index that shows whether the coupling between the oscillators is unidirectional or bidirectional, and quantifies the asymmetry of bidirectional coupling. We demonstrate the efficiency of three different algorithms in determination of directionality index from short and noisy data. These techniques are then applied to analysis of cardiorespiratory interaction in healthy infants. The results reveal that the direction of coupling between cardiovascular and respiratory systems varies with the age within the first 6 months of life. We find a tendency to change from nearly symmetric bidirectional interaction to nearly unidirectional one (from respiration to the cardiovascular system).

A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Using Chou's pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location.

PubMed

Jiang, Xiaoying; Wei, Rong; Zhao, Yanjun; Zhang, Tongliang

2008-05-01

The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three AdaBoost classifiers. The base classifier algorithms in three AdaBoost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different AdaBoost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.

New algorithm for toric intraocular lens power calculation considering the posterior corneal astigmatism.

PubMed

Canovas, Carmen; Alarcon, Aixa; Rosén, Robert; Kasthurirangan, Sanjeev; Ma, Joseph J K; Koch, Douglas D; Piers, Patricia

2018-02-01

To assess the accuracy of toric intraocular lens (IOL) power calculations of a new algorithm that incorporates the effect of posterior corneal astigmatism (PCA). Abbott Medical Optics, Inc., Groningen, the Netherlands. Retrospective case report. In eyes implanted with toric IOLs, the exact vergence formula of the Tecnis toric calculator was used to predict refractive astigmatism from preoperative biometry, surgeon-estimated surgically induced astigmatism (SIA), and implanted IOL power, with and without including the new PCA algorithm. For each calculation method, the error in predicted refractive astigmatism was calculated as the vector difference between the prediction and the actual refraction. Calculations were also made using postoperative keratometry (K) values to eliminate the potential effect of incorrect SIA estimates. The study comprised 274 eyes. The PCA algorithm significantly reduced the centroid error in predicted refractive astigmatism (P < .001). With the PCA algorithm, the centroid error reduced from 0.50 @ 1 to 0.19 @ 3 when using preoperative K values and from 0.30 @ 0 to 0.02 @ 84 when using postoperative K values. Patients who had anterior corneal against-the-rule, with-the-rule, and oblique astigmatism had improvement with the PCA algorithm. In addition, the PCA algorithm reduced the median absolute error in all groups (P < .001). The use of the new PCA algorithm decreased the error in the prediction of residual refractive astigmatism in eyes implanted with toric IOLs. Therefore, the new PCA algorithm, in combination with an exact vergence IOL power calculation formula, led to an increased predictability of toric IOL power. Copyright © 2018 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Predicting missing links in complex networks based on common neighbors and distance

PubMed Central

Yang, Jinxuan; Zhang, Xiao-Dong

2016-01-01

The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity. PMID:27905526

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

NASA Astrophysics Data System (ADS)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

The Behavioral and Neural Mechanisms Underlying the Tracking of Expertise

PubMed Central

Boorman, Erie D.; O’Doherty, John P.; Adolphs, Ralph; Rangel, Antonio

2013-01-01

Summary Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms’ expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent’s prediction was concordant rather than discordant with the subject’s own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network—medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus—represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. PMID:24360551

Neural Generalized Predictive Control: A Newton-Raphson Implementation

NASA Technical Reports Server (NTRS)

Soloway, Donald; Haley, Pamela J.

1997-01-01

An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

Algorithm for Training a Recurrent Multilayer Perceptron

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Rais, Omar T.; Menon, Sunil K.; Atiya, Amir F.

2004-01-01

An improved algorithm has been devised for training a recurrent multilayer perceptron (RMLP) for optimal performance in predicting the behavior of a complex, dynamic, and noisy system multiple time steps into the future. [An RMLP is a computational neural network with self-feedback and cross-talk (both delayed by one time step) among neurons in hidden layers]. Like other neural-network-training algorithms, this algorithm adjusts network biases and synaptic-connection weights according to a gradient-descent rule. The distinguishing feature of this algorithm is a combination of global feedback (the use of predictions as well as the current output value in computing the gradient at each time step) and recursiveness. The recursive aspect of the algorithm lies in the inclusion of the gradient of predictions at each time step with respect to the predictions at the preceding time step; this recursion enables the RMLP to learn the dynamics. It has been conjectured that carrying the recursion to even earlier time steps would enable the RMLP to represent a noisier, more complex system.

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO).

PubMed

Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2017-08-01

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

PubMed

El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

2015-04-01

This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

A comprehensive performance evaluation on the prediction results of existing cooperative transcription factors identification algorithms.

PubMed

Lai, Fu-Jou; Chang, Hong-Tsun; Huang, Yueh-Min; Wu, Wei-Sheng

2014-01-01

Eukaryotic transcriptional regulation is known to be highly connected through the networks of cooperative transcription factors (TFs). Measuring the cooperativity of TFs is helpful for understanding the biological relevance of these TFs in regulating genes. The recent advances in computational techniques led to various predictions of cooperative TF pairs in yeast. As each algorithm integrated different data resources and was developed based on different rationales, it possessed its own merit and claimed outperforming others. However, the claim was prone to subjectivity because each algorithm compared with only a few other algorithms and only used a small set of performance indices for comparison. This motivated us to propose a series of indices to objectively evaluate the prediction performance of existing algorithms. And based on the proposed performance indices, we conducted a comprehensive performance evaluation. We collected 14 sets of predicted cooperative TF pairs (PCTFPs) in yeast from 14 existing algorithms in the literature. Using the eight performance indices we adopted/proposed, the cooperativity of each PCTFP was measured and a ranking score according to the mean cooperativity of the set was given to each set of PCTFPs under evaluation for each performance index. It was seen that the ranking scores of a set of PCTFPs vary with different performance indices, implying that an algorithm used in predicting cooperative TF pairs is of strength somewhere but may be of weakness elsewhere. We finally made a comprehensive ranking for these 14 sets. The results showed that Wang J's study obtained the best performance evaluation on the prediction of cooperative TF pairs in yeast. In this study, we adopted/proposed eight performance indices to make a comprehensive performance evaluation on the prediction results of 14 existing cooperative TFs identification algorithms. Most importantly, these proposed indices can be easily applied to measure the performance of new algorithms developed in the future, thus expedite progress in this research field.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

PubMed

Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-02-01

The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius). The prediction of the backscattered form function is investigated by a combined approach between fuzzy clustering algorithms and bio-inspired algorithms. Four famous fuzzy clustering algorithms: the fuzzy c-means (FCM), the Gustafson-Kessel algorithm (GK), the fuzzy c-regression model (FCRM) and the Gath-Geva algorithm (GG) are combined with particle swarm optimization and genetic algorithm. The symmetric and antisymmetric circumferential waves A, S 0 , A 1 , S 1 and S 2 are investigated in a reduced frequency (k 1 a) range extends over 0.1

Packing Boxes into Multiple Containers Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Menghani, Deepak; Guha, Anirban

2016-07-01

Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.

Prediction algorithms for urban traffic control

DOT National Transportation Integrated Search

1979-02-01

The objectives of this study are to 1) review and assess the state-of-the-art of prediction algorithms for urban traffic control in terms of their accuracy and application, and 2) determine the prediction accuracy obtainable by examining the performa...

Phase 2 development of Great Lakes algorithms for Nimbus-7 coastal zone color scanner

NASA Technical Reports Server (NTRS)

Tanis, Fred J.

1984-01-01

A series of experiments have been conducted in the Great Lakes designed to evaluate the application of the NIMBUS-7 Coastal Zone Color Scanner (CZCS). Atmospheric and water optical models were used to relate surface and subsurface measurements to satellite measured radiances. Absorption and scattering measurements were reduced to obtain a preliminary optical model for the Great Lakes. Algorithms were developed for geometric correction, correction for Rayleigh and aerosol path radiance, and prediction of chlorophyll-a pigment and suspended mineral concentrations. The atmospheric algorithm developed compared favorably with existing algorithms and was the only algorithm found to adequately predict the radiance variations in the 670 nm band. The atmospheric correction algorithm developed was designed to extract needed algorithm parameters from the CZCS radiance values. The Gordon/NOAA ocean algorithms could not be demonstrated to work for Great Lakes waters. Predicted values of chlorophyll-a concentration compared favorably with expected and measured data for several areas of the Great Lakes.

Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression.

PubMed

Gijsberts, Arjan; Metta, Giorgio

2013-05-01

Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated incrementally in real-time and require minimal human intervention. Incremental Sparse Spectrum Gaussian Process Regression is an algorithm that is targeted specifically for use in this context. Rather than developing a novel algorithm from the ground up, the method is based on the thoroughly studied Gaussian Process Regression algorithm, therefore ensuring a solid theoretical foundation. Non-linearity and a bounded update complexity are achieved simultaneously by means of a finite dimensional random feature mapping that approximates a kernel function. As a result, the computational cost for each update remains constant over time. Finally, algorithmic simplicity and support for automated hyperparameter optimization ensures convenience when employed in practice. Empirical validation on a number of synthetic and real-life learning problems confirms that the performance of Incremental Sparse Spectrum Gaussian Process Regression is superior with respect to the popular Locally Weighted Projection Regression, while computational requirements are found to be significantly lower. The method is therefore particularly suited for learning with real-time constraints or when computational resources are limited. Copyright © 2012 Elsevier Ltd. All rights reserved.

Prognostics of Power Electronics, Methods and Validation Experiments

NASA Technical Reports Server (NTRS)

Kulkarni, Chetan S.; Celaya, Jose R.; Biswas, Gautam; Goebel, Kai

2012-01-01

Abstract Failure of electronic devices is a concern for future electric aircrafts that will see an increase of electronics to drive and control safety-critical equipment throughout the aircraft. As a result, investigation of precursors to failure in electronics and prediction of remaining life of electronic components is of key importance. DC-DC power converters are power electronics systems employed typically as sourcing elements for avionics equipment. Current research efforts in prognostics for these power systems focuses on the identification of failure mechanisms and the development of accelerated aging methodologies and systems to accelerate the aging process of test devices, while continuously measuring key electrical and thermal parameters. Preliminary model-based prognostics algorithms have been developed making use of empirical degradation models and physics-inspired degradation model with focus on key components like electrolytic capacitors and power MOSFETs (metal-oxide-semiconductor-field-effect-transistor). This paper presents current results on the development of validation methods for prognostics algorithms of power electrolytic capacitors. Particularly, in the use of accelerated aging systems for algorithm validation. Validation of prognostics algorithms present difficulties in practice due to the lack of run-to-failure experiments in deployed systems. By using accelerated experiments, we circumvent this problem in order to define initial validation activities.

Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications

USDA-ARS?s Scientific Manuscript database

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3.

PubMed

Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K

2016-10-01

Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm. By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power. Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation. Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin. This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.'s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.'s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology.

Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3

PubMed Central

Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K.

2016-01-01

STUDY QUESTION Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, METHODS The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the curve (AUC) to establish the predictive strength of the algorithm. MAIN RESULTS AND THE ROLE OF CHANCE By applying the here developed algorithm (KIDScore), which was based on six annotations (the number of pronuclei equals 2 at the 1-cell stage, time from insemination to pronuclei fading at the 1-cell stage, time from insemination to the 2-cell stage, time from insemination to the 3-cell stage, time from insemination to the 5-cell stage and time from insemination to the 8-cell stage) and ranking the embryos in five groups, the implantation potential of the embryos was predicted with an AUC of 0.650. On Day 3 the KIDScore algorithm was capable of predicting blastocyst development with an AUC of 0.745 and blastocyst quality with an AUC of 0.679. In a comparison of blastocyst prediction including six other published algorithms and KIDScore, only KIDScore and one more algorithm surpassed an algorithm constructed on conventional Alpha/ESHRE consensus timings in terms of predictive power. LIMITATIONS, REASONS FOR CAUTION Some morphological assessments were not available and consequently three of the algorithms in the comparison were not used in full and may therefore have been put at a disadvantage. Algorithms based on implantation data from Day 3 embryo transfers require adjustments to be capable of predicting the implantation potential of Day 5 embryo transfers. The current study is restricted by its retrospective nature and absence of live birth information. Prospective Randomized Controlled Trials should be used in future studies to establish the value of time-lapse technology and morphokinetic evaluation. WIDER IMPLICATIONS OF THE FINDINGS Algorithms applicable to different culture conditions can be developed if based on large data sets of heterogeneous origin. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by Vitrolife A/S, Denmark and Vitrolife AB, Sweden. B.M.P.’s company BMP Analytics is performing consultancy for Vitrolife A/S. M.B. is employed at Vitrolife A/S. M.M.’s company ilabcomm GmbH received honorarium for consultancy from Vitrolife AB. D.K.G. received research support from Vitrolife AB. PMID:27609980

High Speed Research Noise Prediction Code (HSRNOISE) User's and Theoretical Manual

NASA Technical Reports Server (NTRS)

Golub, Robert (Technical Monitor); Rawls, John W., Jr.; Yeager, Jessie C.

2004-01-01

This report describes a computer program, HSRNOISE, that predicts noise levels for a supersonic aircraft powered by mixed flow turbofan engines with rectangular mixer-ejector nozzles. It fully documents the noise prediction algorithms, provides instructions for executing the HSRNOISE code, and provides predicted noise levels for the High Speed Research (HSR) program Technology Concept (TC) aircraft. The component source noise prediction algorithms were developed jointly by Boeing, General Electric Aircraft Engines (GEAE), NASA and Pratt & Whitney during the course of the NASA HSR program. Modern Technologies Corporation developed an alternative mixer ejector jet noise prediction method under contract to GEAE that has also been incorporated into the HSRNOISE prediction code. Algorithms for determining propagation effects and calculating noise metrics were taken from the NASA Aircraft Noise Prediction Program.

Real-time prediction and gating of respiratory motion using an extended Kalman filter and Gaussian process regression

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2015-01-01

Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR+, implements a gating function without pre-specifying a particular region of the patient’s breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR+ algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR+ implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR+ in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR+. The experimental results show that the EKF-GPR+ algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR+ reduces the patient-wise RMS error to 37%, 39% and 42% in percent ratios relative to no prediction for a duty cycle of 80% at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The experiments also confirm that EKF-GPR+ controls the duty cycle with reasonable accuracy.

Application of Model-based Prognostics to a Pneumatic Valves Testbed

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Kulkarni, Chetan S.; Gorospe, George

2014-01-01

Pneumatic-actuated valves play an important role in many applications, including cryogenic propellant loading for space operations. Model-based prognostics emphasizes the importance of a model that describes the nominal and faulty behavior of a system, and how faulty behavior progresses in time, causing the end of useful life of the system. We describe the construction of a testbed consisting of a pneumatic valve that allows the injection of faulty behavior and controllable fault progression. The valve opens discretely, and is controlled through a solenoid valve. Controllable leaks of pneumatic gas in the testbed are introduced through proportional valves, allowing the testing and validation of prognostics algorithms for pneumatic valves. A new valve prognostics approach is developed that estimates fault progression and predicts remaining life based only on valve timing measurements. Simulation experiments demonstrate and validate the approach.

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Prediction based active ramp metering control strategy with mobility and safety assessment

NASA Astrophysics Data System (ADS)

Fang, Jie; Tu, Lili

2018-04-01

Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.

False-nearest-neighbors algorithm and noise-corrupted time series

NASA Astrophysics Data System (ADS)

Rhodes, Carl; Morari, Manfred

1997-05-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented.

Protein Structure Prediction with Evolutionary Algorithms

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

Hart, W.E.; Krasnogor, N.; Pelta, D.A.

1999-02-08

Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.

A Universal Tare Load Prediction Algorithm for Strain-Gage Balance Calibration Data Analysis

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2011-01-01

An algorithm is discussed that may be used to estimate tare loads of wind tunnel strain-gage balance calibration data. The algorithm was originally developed by R. Galway of IAR/NRC Canada and has been described in the literature for the iterative analysis technique. Basic ideas of Galway's algorithm, however, are universally applicable and work for both the iterative and the non-iterative analysis technique. A recent modification of Galway's algorithm is presented that improves the convergence behavior of the tare load prediction process if it is used in combination with the non-iterative analysis technique. The modified algorithm allows an analyst to use an alternate method for the calculation of intermediate non-linear tare load estimates whenever Galway's original approach does not lead to a convergence of the tare load iterations. It is also shown in detail how Galway's algorithm may be applied to the non-iterative analysis technique. Hand load data from the calibration of a six-component force balance is used to illustrate the application of the original and modified tare load prediction method. During the analysis of the data both the iterative and the non-iterative analysis technique were applied. Overall, predicted tare loads for combinations of the two tare load prediction methods and the two balance data analysis techniques showed excellent agreement as long as the tare load iterations converged. The modified algorithm, however, appears to have an advantage over the original algorithm when absolute voltage measurements of gage outputs are processed using the non-iterative analysis technique. In these situations only the modified algorithm converged because it uses an exact solution of the intermediate non-linear tare load estimate for the tare load iteration.

Prediction Of The Expected Safety Performance Of Rural Two-Lane Highways

DOT National Transportation Integrated Search

2000-12-01

This report presents an algorithm for predicting the safety performance of a rural two-lane highway. The accident prediction algorithm consists of base models and accident modification factors for both roadway segments and at-grade intersections on r...

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

Strategies for concurrent processing of complex algorithms in data driven architectures

NASA Technical Reports Server (NTRS)

Stoughton, John W.; Mielke, Roland R.; Som, Sukhamony

1990-01-01

The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Strategies for concurrent processing of complex algorithms in data driven architectures

NASA Technical Reports Server (NTRS)

Som, Sukhamoy; Stoughton, John W.; Mielke, Roland R.

1990-01-01

Performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures are discussed. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called algorithm to architecture mapping model (ATAMM). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

Accuracy of algorithms to predict accessory pathway location in children with Wolff-Parkinson-White syndrome.

PubMed

Wren, Christopher; Vogel, Melanie; Lord, Stephen; Abrams, Dominic; Bourke, John; Rees, Philip; Rosenthal, Eric

2012-02-01

The aim of this study was to examine the accuracy in predicting pathway location in children with Wolff-Parkinson-White syndrome for each of seven published algorithms. ECGs from 100 consecutive children with Wolff-Parkinson-White syndrome undergoing electrophysiological study were analysed by six investigators using seven published algorithms, six of which had been developed in adult patients. Accuracy and concordance of predictions were adjusted for the number of pathway locations. Accessory pathways were left-sided in 49, septal in 20 and right-sided in 31 children. Overall accuracy of prediction was 30-49% for the exact location and 61-68% including adjacent locations. Concordance between investigators varied between 41% and 86%. No algorithm was better at predicting septal pathways (accuracy 5-35%, improving to 40-78% including adjacent locations), but one was significantly worse. Predictive accuracy was 24-53% for the exact location of right-sided pathways (50-71% including adjacent locations) and 32-55% for the exact location of left-sided pathways (58-73% including adjacent locations). All algorithms were less accurate in our hands than in other authors' own assessment. None performed well in identifying midseptal or right anteroseptal accessory pathway locations.

Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

USGS Publications Warehouse

Watling, James I.; Brandt, Laura A.; Bucklin, David N.; Fujisaki, Ikuko; Mazzotti, Frank J.; Romañach, Stephanie; Speroterra, Carolina

2015-01-01

Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.

Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm.

PubMed

Cuthbertson, Carmen C; Kucharska-Newton, Anna; Faurot, Keturah R; Stürmer, Til; Jonsson Funk, Michele; Palta, Priya; Windham, B Gwen; Thai, Sydney; Lund, Jennifer L

2018-07-01

Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data. Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011-2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality. The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds. The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

Detection of dominant flow and abnormal events in surveillance video

NASA Astrophysics Data System (ADS)

Kwak, Sooyeong; Byun, Hyeran

2011-02-01

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

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

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine

2009-03-05

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Methods for predicting properties and tailoring salt solutions for industrial processes

NASA Technical Reports Server (NTRS)

Ally, Moonis R.

1993-01-01

An algorithm developed at Oak Ridge National Laboratory accurately and quickly predicts thermodynamic properties of concentrated aqueous salt solutions. This algorithm is much simpler and much faster than other modeling schemes and is unique because it can predict solution behavior at very high concentrations and under varying conditions. Typical industrial applications of this algorithm would be in manufacture of inorganic chemicals by crystallization, thermal storage, refrigeration and cooling, extraction of metals, emissions controls, etc.

Application of Avco data analysis and prediction techniques (ADAPT) to prediction of sunspot activity

NASA Technical Reports Server (NTRS)

Hunter, H. E.; Amato, R. A.

1972-01-01

The results are presented of the application of Avco Data Analysis and Prediction Techniques (ADAPT) to derivation of new algorithms for the prediction of future sunspot activity. The ADAPT derived algorithms show a factor of 2 to 3 reduction in the expected 2-sigma errors in the estimates of the 81-day running average of the Zurich sunspot numbers. The report presents: (1) the best estimates for sunspot cycles 20 and 21, (2) a comparison of the ADAPT performance with conventional techniques, and (3) specific approaches to further reduction in the errors of estimated sunspot activity and to recovery of earlier sunspot historical data. The ADAPT programs are used both to derive regression algorithm for prediction of the entire 11-year sunspot cycle from the preceding two cycles and to derive extrapolation algorithms for extrapolating a given sunspot cycle based on any available portion of the cycle.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

A novel toolpath force prediction algorithm using CAM volumetric data for optimizing robotic arthroplasty.

PubMed

Kianmajd, Babak; Carter, David; Soshi, Masakazu

2016-10-01

Robotic total hip arthroplasty is a procedure in which milling operations are performed on the femur to remove material for the insertion of a prosthetic implant. The robot performs the milling operation by following a sequential list of tool motions, also known as a toolpath, generated by a computer-aided manufacturing (CAM) software. The purpose of this paper is to explain a new toolpath force prediction algorithm that predicts cutting forces, which results in improving the quality and safety of surgical systems. With a custom macro developed in the CAM system's native application programming interface, cutting contact patch volume was extracted from CAM simulations. A time domain cutting force model was then developed through the use of a cutting force prediction algorithm. The second portion validated the algorithm by machining a hip canal in simulated bone using a CNC machine. Average cutting forces were measured during machining using a dynamometer and compared to the values predicted from CAM simulation data using the proposed method. The results showed the predicted forces matched the measured forces in both magnitude and overall pattern shape. However, due to inconsistent motion control, the time duration of the forces was slightly distorted. Nevertheless, the algorithm effectively predicted the forces throughout an entire hip canal procedure. This method provides a fast and easy technique for predicting cutting forces during orthopedic milling by utilizing data within a CAM software.

Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation.

PubMed

Baumgärtel, Regina M; Krawczyk-Becker, Martin; Marquardt, Daniel; Völker, Christoph; Hu, Hongmei; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Ernst, Stephan M A; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-12-30

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise. Three instrumental measures were employed to assess predicted speech intelligibility and predicted sound quality: the intelligibility-weighted signal-to-noise ratio, the short-time objective intelligibility measure, and the perceptual evaluation of speech quality. The results show substantial improvements in predicted speech intelligibility as well as sound quality for the proposed algorithms. The evaluated coherence-based noise reduction algorithm was able to provide improvements in predicted audio signal quality. For the tested single-channel noise reduction algorithm, improvements in intelligibility-weighted signal-to-noise ratio were observed in all but the nonstationary cafeteria ambient noise scenario. Binaural minimum variance distortionless response beamforming algorithms performed particularly well in all noise scenarios. © The Author(s) 2015.

Comparing Binaural Pre-processing Strategies I

PubMed Central

Krawczyk-Becker, Martin; Marquardt, Daniel; Völker, Christoph; Hu, Hongmei; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Ernst, Stephan M. A.; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-01-01

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise. Three instrumental measures were employed to assess predicted speech intelligibility and predicted sound quality: the intelligibility-weighted signal-to-noise ratio, the short-time objective intelligibility measure, and the perceptual evaluation of speech quality. The results show substantial improvements in predicted speech intelligibility as well as sound quality for the proposed algorithms. The evaluated coherence-based noise reduction algorithm was able to provide improvements in predicted audio signal quality. For the tested single-channel noise reduction algorithm, improvements in intelligibility-weighted signal-to-noise ratio were observed in all but the nonstationary cafeteria ambient noise scenario. Binaural minimum variance distortionless response beamforming algorithms performed particularly well in all noise scenarios. PMID:26721920

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

Code-based Diagnostic Algorithms for Idiopathic Pulmonary Fibrosis. Case Validation and Improvement.

PubMed

Ley, Brett; Urbania, Thomas; Husson, Gail; Vittinghoff, Eric; Brush, David R; Eisner, Mark D; Iribarren, Carlos; Collard, Harold R

2017-06-01

Population-based studies of idiopathic pulmonary fibrosis (IPF) in the United States have been limited by reliance on diagnostic code-based algorithms that lack clinical validation. To validate a well-accepted International Classification of Diseases, Ninth Revision, code-based algorithm for IPF using patient-level information and to develop a modified algorithm for IPF with enhanced predictive value. The traditional IPF algorithm was used to identify potential cases of IPF in the Kaiser Permanente Northern California adult population from 2000 to 2014. Incidence and prevalence were determined overall and by age, sex, and race/ethnicity. A validation subset of cases (n = 150) underwent expert medical record and chest computed tomography review. A modified IPF algorithm was then derived and validated to optimize positive predictive value. From 2000 to 2014, the traditional IPF algorithm identified 2,608 cases among 5,389,627 at-risk adults in the Kaiser Permanente Northern California population. Annual incidence was 6.8/100,000 person-years (95% confidence interval [CI], 6.1-7.7) and was higher in patients with older age, male sex, and white race. The positive predictive value of the IPF algorithm was only 42.2% (95% CI, 30.6 to 54.6%); sensitivity was 55.6% (95% CI, 21.2 to 86.3%). The corrected incidence was estimated at 5.6/100,000 person-years (95% CI, 2.6-10.3). A modified IPF algorithm had improved positive predictive value but reduced sensitivity compared with the traditional algorithm. A well-accepted International Classification of Diseases, Ninth Revision, code-based IPF algorithm performs poorly, falsely classifying many non-IPF cases as IPF and missing a substantial proportion of IPF cases. A modification of the IPF algorithm may be useful for future population-based studies of IPF.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

PubMed

Tamibmaniam, Jayashamani; Hussin, Narwani; Cheah, Wee Kooi; Ng, Kee Sing; Muninathan, Prema

2016-01-01

WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics

PubMed Central

Reeder, Jens; Giegerich, Robert

2004-01-01

Background The general problem of RNA secondary structure prediction under the widely used thermodynamic model is known to be NP-complete when the structures considered include arbitrary pseudoknots. For restricted classes of pseudoknots, several polynomial time algorithms have been designed, where the O(n6)time and O(n4) space algorithm by Rivas and Eddy is currently the best available program. Results We introduce the class of canonical simple recursive pseudoknots and present an algorithm that requires O(n4) time and O(n2) space to predict the energetically optimal structure of an RNA sequence, possible containing such pseudoknots. Evaluation against a large collection of known pseudoknotted structures shows the adequacy of the canonization approach and our algorithm. Conclusions RNA pseudoknots of medium size can now be predicted reliably as well as efficiently by the new algorithm. PMID:15294028

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

PubMed Central

Wang, Jie-Sheng; Han, Shuang

2015-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

Model Predictive Control Based Motion Drive Algorithm for a Driving Simulator

NASA Astrophysics Data System (ADS)

Rehmatullah, Faizan

In this research, we develop a model predictive control based motion drive algorithm for the driving simulator at Toronto Rehabilitation Institute. Motion drive algorithms exploit the limitations of the human vestibular system to formulate a perception of motion within the constrained workspace of a simulator. In the absence of visual cues, the human perception system is unable to distinguish between acceleration and the force of gravity. The motion drive algorithm determines control inputs to displace the simulator platform, and by using the resulting inertial forces and angular rates, creates the perception of motion. By using model predictive control, we can optimize the use of simulator workspace for every maneuver while simulating the vehicle perception. With the ability to handle nonlinear constraints, the model predictive control allows us to incorporate workspace limitations.

Understanding the Effects of Sampling on Healthcare Risk Modeling for the Prediction of Future High-Cost Patients

NASA Astrophysics Data System (ADS)

Moturu, Sai T.; Liu, Huan; Johnson, William G.

Rapidly rising healthcare costs represent one of the major issues plaguing the healthcare system. Data from the Arizona Health Care Cost Containment System, Arizona's Medicaid program provide a unique opportunity to exploit state-of-the-art machine learning and data mining algorithms to analyze data and provide actionable findings that can aid cost containment. Our work addresses specific challenges in this real-life healthcare application with respect to data imbalance in the process of building predictive risk models for forecasting high-cost patients. We survey the literature and propose novel data mining approaches customized for this compelling application with specific focus on non-random sampling. Our empirical study indicates that the proposed approach is highly effective and can benefit further research on cost containment in the healthcare industry.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

The utility and limitations of current web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection #

PubMed Central

Chaves, Francisco A.; Lee, Alvin H.; Nayak, Jennifer; Richards, Katherine A.; Sant, Andrea J.

2012-01-01

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding their success in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple web-available algorithms by comparing their predictions and our results using purely empirical methods for epitope discovery in influenza that utilized overlapping peptides and cytokine Elispots, for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false positive and false negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC50 values and true dissociation rates of peptide:MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to “train” the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules. PMID:22467652

Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: an online database and application of the MAPS statement.

PubMed

Dakin, Helen; Abel, Lucy; Burns, Richéal; Yang, Yaling

2018-02-12

The Health Economics Research Centre (HERC) Database of Mapping Studies was established in 2013, based on a systematic review of studies developing mapping algorithms predicting EQ-5D. The Mapping onto Preference-based measures reporting Standards (MAPS) statement was published in 2015 to improve reporting of mapping studies. We aimed to update the systematic review and assess the extent to which recently-published studies mapping condition-specific quality of life or clinical measures to the EQ-5D follow the guidelines published in the MAPS Reporting Statement. A published systematic review was updated using the original inclusion criteria to include studies published by December 2016. We included studies reporting novel algorithms mapping from any clinical measure or patient-reported quality of life measure to either the EQ-5D-3L or EQ-5D-5L. Titles and abstracts of all identified studies and the full text of papers published in 2016 were assessed against the MAPS checklist. The systematic review identified 144 mapping studies reporting 190 algorithms mapping from 110 different source instruments to EQ-5D. Of the 17 studies published in 2016, nine (53%) had titles that followed the MAPS statement guidance, although only two (12%) had abstracts that fully addressed all MAPS items. When the full text of these papers was assessed against the complete MAPS checklist, only two studies (12%) were found to fulfil or partly fulfil all criteria. Of the 141 papers (across all years) that included abstracts, the items on the MAPS statement checklist that were fulfilled by the largest number of studies comprised having a structured abstract (95%) and describing target instruments (91%) and source instruments (88%). The number of published mapping studies continues to increase. Our updated database provides a convenient way to identify mapping studies for use in cost-utility analysis. Most recent studies do not fully address all items on the MAPS checklist.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm.

PubMed

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-04-01

Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The 'predictAL-10' risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the 'predictAL-9'), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. © British Journal of General Practice 2017.

Predicting missing links and identifying spurious links via likelihood analysis

NASA Astrophysics Data System (ADS)

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-03-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

Predicting missing links and identifying spurious links via likelihood analysis

PubMed Central

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-01-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

Traffic Noise Ground Attenuation Algorithm Evaluation

NASA Astrophysics Data System (ADS)

Herman, Lloyd Allen

The Federal Highway Administration traffic noise prediction program, STAMINA 2.0, was evaluated for its accuracy. In addition, the ground attenuation algorithm used in the Ontario ORNAMENT method was evaluated to determine its potential to improve these predictions. Field measurements of sound levels were made at 41 sites on I-440 in Nashville, Tennessee in order to both study noise barrier effectiveness and to evaluate STAMINA 2.0 and the performance of the ORNAMENT ground attenuation algorithm. The measurement sites, which contain large variations in terrain, included several cross sections. Further, all sites contain some type of barrier, natural or constructed, which could more fully expose the strength and weaknesses of the ground attenuation algorithms. The noise barrier evaluation was accomplished in accordance with American National Standard Methods for Determination of Insertion Loss of Outdoor Noise Barriers which resulted in an evaluation of this standard. The entire 7.2 mile length of I-440 was modeled using STAMINA 2.0. A multiple run procedure was developed to emulate the results that would be obtained if the ORNAMENT algorithm was incorporated into STAMINA 2.0. Finally, the predicted noise levels based on STAMINA 2.0 and STAMINA with the ORNAMENT ground attenuation algorithm were compared with each other and with the field measurements. It was found that STAMINA 2.0 overpredicted noise levels by an average of over 2 dB for the receivers on I-440, whereas, the STAMINA with ORNAMENT ground attenuation algorithm overpredicted noise levels by an average of less than 0.5 dB. The mean errors for the two predictions were found to be statistically different from each other, and the mean error for the prediction with the ORNAMENT ground attenuation algorithm was not found to be statistically different from zero. The STAMINA 2.0 program predicts little, if any, ground attenuation for receivers at typical first-row distances from highways where noise barriers are used. The ORNAMENT ground attenuation algorithm, which recognizes and better compensates for the presence of obstacles in the propagation path of a sound wave, predicted significant amounts of ground attenuation for most sites.

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

PubMed

González-Recio, O; Jiménez-Montero, J A; Alenda, R

2013-01-01

In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy and bias. This modification may be used to speed the calculus of genome-assisted evaluation in large data sets such us those obtained from consortiums. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A numerical solution of Duffing's equations including the prediction of jump phenomena

NASA Technical Reports Server (NTRS)

Moyer, E. T., Jr.; Ghasghai-Abdi, E.

1987-01-01

Numerical methodology for the solution of Duffing's differential equation is presented. Algorithms for the prediction of multiple equilibrium solutions and jump phenomena are developed. In addition, a filtering algorithm for producing steady state solutions is presented. The problem of a rigidly clamped circular plate subjected to cosinusoidal pressure loading is solved using the developed algorithms (the plate is assumed to be in the geometrically nonlinear range). The results accurately predict regions of solution multiplicity and jump phenomena.

Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records

PubMed Central

Weiss, Jeremy C; Page, David; Peissig, Peggy L; Natarajan, Sriraam; McCarty, Catherine

2013-01-01

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices. PMID:25360347

Bridging the gap between real-life data and simulated data by providing a highly realistic fall dataset for evaluating camera-based fall detection algorithms.

PubMed

Baldewijns, Greet; Debard, Glen; Mertes, Gert; Vanrumste, Bart; Croonenborghs, Tom

2016-03-01

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.

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

Zawisza, I; Yan, H; Yin, F

Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogatemore » signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.« less

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

NASA Astrophysics Data System (ADS)

Baraldi, P.; Bonfanti, G.; Zio, E.

2018-03-01

The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

PubMed

Cui, Yiqian; Shi, Junyou; Wang, Zili

2015-11-01

Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

The legal and ethical concerns that arise from using complex predictive analytics in health care.

PubMed

Cohen, I Glenn; Amarasingham, Ruben; Shah, Anand; Xie, Bin; Lo, Bernard

2014-07-01

Predictive analytics, or the use of electronic algorithms to forecast future events in real time, makes it possible to harness the power of big data to improve the health of patients and lower the cost of health care. However, this opportunity raises policy, ethical, and legal challenges. In this article we analyze the major challenges to implementing predictive analytics in health care settings and make broad recommendations for overcoming challenges raised in the four phases of the life cycle of a predictive analytics model: acquiring data to build the model, building and validating it, testing it in real-world settings, and disseminating and using it more broadly. For instance, we recommend that model developers implement governance structures that include patients and other stakeholders starting in the earliest phases of development. In addition, developers should be allowed to use already collected patient data without explicit consent, provided that they comply with federal regulations regarding research on human subjects and the privacy of health information. Project HOPE—The People-to-People Health Foundation, Inc.

Research prioritization through prediction of future impact on biomedical science: a position paper on inference-analytics.

PubMed

Ganapathiraju, Madhavi K; Orii, Naoki

2013-08-30

Advances in biotechnology have created "big-data" situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist's perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact. We propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI. This position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims.

PubMed

Chastek, Benjamin J; Oleen-Burkey, Merrikay; Lopez-Bresnahan, Maria V

2010-01-01

Relapse is a common measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objective of this study was to test the content validity of an operational algorithm for detecting relapse in claims data. A claims-based relapse detection algorithm was tested by comparing its detection rate over a 1-year period with relapses identified based on medical chart review. According to the algorithm, MS patients in a US healthcare claims database who had either (1) a primary claim for MS during hospitalization or (2) a corticosteroid claim following a MS-related outpatient visit were designated as having a relapse. Patient charts were examined for explicit indication of relapse or care suggestive of relapse. Positive and negative predictive values were calculated. Medical charts were reviewed for 300 MS patients, half of whom had a relapse according to the algorithm. The claims-based criteria correctly classified 67.3% of patients with relapses (positive predictive value) and 70.0% of patients without relapses (negative predictive value; kappa 0.373: p < 0.001). Alternative algorithms did not improve on the predictive value of the operational algorithm. Limitations of the algorithm include lack of differentiation between relapsing-remitting MS and other types, and that it does not incorporate measures of function and disability. The claims-based algorithm appeared to successfully detect moderate-to-severe MS relapse. This validated definition can be applied to future claims-based MS studies.

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

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

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

The Current Status of Unsteady CFD Approaches for Aerodynamic Flow Control

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Singer, Bart A.; Yamaleev, Nail; Vatsa, Veer N.; Viken, Sally A.; Atkins, Harold L.

2002-01-01

An overview of the current status of time dependent algorithms is presented. Special attention is given to algorithms used to predict fluid actuator flows, as well as other active and passive flow control devices. Capabilities for the next decade are predicted, and principal impediments to the progress of time-dependent algorithms are identified.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Predictive Caching Using the TDAG Algorithm

NASA Technical Reports Server (NTRS)

Laird, Philip; Saul, Ronald

1992-01-01

We describe how the TDAG algorithm for learning to predict symbol sequences can be used to design a predictive cache store. A model of a two-level mass storage system is developed and used to calculate the performance of the cache under various conditions. Experimental simulations provide good confirmation of the model.

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies

NASA Technical Reports Server (NTRS)

Celaya, Jose; Saxena, Abhinav; Saha, Sankalita; Goebel, Kai F.

2011-01-01

An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Dieattach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

PubMed Central

González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

2017-01-01

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage. PMID:28075364

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks.

PubMed

González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

2017-01-09

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

Community detection in complex networks using link prediction

NASA Astrophysics Data System (ADS)

Cheng, Hui-Min; Ning, Yi-Zi; Yin, Zhao; Yan, Chao; Liu, Xin; Zhang, Zhong-Yuan

2018-01-01

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

The sensitivity and negative predictive value of a pediatric cervical spine clearance algorithm that minimizes computerized tomography.

PubMed

Arbuthnot, Mary; Mooney, David P

2017-01-01

It is crucial to identify cervical spine injuries while minimizing ionizing radiation. This study analyzes the sensitivity and negative predictive value of a pediatric cervical spine clearance algorithm. We performed a retrospective review of all children <21years old who were admitted following blunt trauma and underwent cervical spine clearance utilizing our institution's cervical spine clearance algorithm over a 10-year period. Age, gender, International Classification of Diseases 9th Edition diagnosis codes, presence or absence of cervical collar on arrival, Injury Severity Score, and type of cervical spine imaging obtained were extracted from the trauma registry and electronic medical record. Descriptive statistics were used and the sensitivity and negative predictive value of the algorithm were calculated. Approximately 125,000 children were evaluated in the Emergency Department and 11,331 were admitted. Of the admitted children, 1023 patients arrived in a cervical collar without advanced cervical spine imaging and were evaluated using the cervical spine clearance algorithm. Algorithm sensitivity was 94.4% and the negative predictive value was 99.9%. There was one missed injury, a spinous process tip fracture in a teenager maintained in a collar. Our algorithm was associated with a low missed injury rate and low CT utilization rate, even in children <3years old. IV. Published by Elsevier Inc.

On the origin of synthetic life: attribution of output to a particular algorithm

NASA Astrophysics Data System (ADS)

Yampolskiy, Roman V.

2017-01-01

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin’s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples’ origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers.

Nondestructive evaluation using dipole model analysis with a scan type magnetic camera

NASA Astrophysics Data System (ADS)

Lee, Jinyi; Hwang, Jiseong

2005-12-01

Large structures such as nuclear power, thermal power, chemical and petroleum refining plants are drawing interest with regard to the economic aspect of extending component life in respect to the poor environment created by high pressure, high temperature, and fatigue, securing safety from corrosion and exceeding their designated life span. Therefore, technology that accurately calculates and predicts degradation and defects of aging materials is extremely important. Among different methods available, nondestructive testing using magnetic methods is effective in predicting and evaluating defects on the surface of or surrounding ferromagnetic structures. It is important to estimate the distribution of magnetic field intensity for applicable magnetic methods relating to industrial nondestructive evaluation. A magnetic camera provides distribution of a quantitative magnetic field with a homogeneous lift-off and spatial resolution. It is possible to interpret the distribution of magnetic field when the dipole model was introduced. This study proposed an algorithm for nondestructive evaluation using dipole model analysis with a scan type magnetic camera. The numerical and experimental considerations of the quantitative evaluation of several sizes and shapes of cracks using magnetic field images of the magnetic camera were examined.

A material political economy: Automated Trading Desk and price prediction in high-frequency trading.

PubMed

MacKenzie, Donald

2017-04-01

This article contains the first detailed historical study of one of the new high-frequency trading (HFT) firms that have transformed many of the world's financial markets. The study, of Automated Trading Desk (ATD), one of the earliest and most important such firms, focuses on how ATD's algorithms predicted share price changes. The article argues that political-economic struggles are integral to the existence of some of the 'pockets' of predictable structure in the otherwise random movements of prices, to the availability of the data that allow algorithms to identify these pockets, and to the capacity of algorithms to use these predictions to trade profitably. The article also examines the role of HFT algorithms such as ATD's in the epochal, fiercely contested shift in US share trading from 'fixed-role' markets towards 'all-to-all' markets.

Local-search based prediction of medical image registration error

NASA Astrophysics Data System (ADS)

Saygili, Görkem

2018-03-01

Medical image registration is a crucial task in many different medical imaging applications. Hence, considerable amount of work has been published recently that aim to predict the error in a registration without any human effort. If provided, these error predictions can be used as a feedback to the registration algorithm to further improve its performance. Recent methods generally start with extracting image-based and deformation-based features, then apply feature pooling and finally train a Random Forest (RF) regressor to predict the real registration error. Image-based features can be calculated after applying a single registration but provide limited accuracy whereas deformation-based features such as variation of deformation vector field may require up to 20 registrations which is a considerably high time-consuming task. This paper proposes to use extracted features from a local search algorithm as image-based features to estimate the error of a registration. The proposed method comprises a local search algorithm to find corresponding voxels between registered image pairs and based on the amount of shifts and stereo confidence measures, it predicts the amount of registration error in millimetres densely using a RF regressor. Compared to other algorithms in the literature, the proposed algorithm does not require multiple registrations, can be efficiently implemented on a Graphical Processing Unit (GPU) and can still provide highly accurate error predictions in existence of large registration error. Experimental results with real registrations on a public dataset indicate a substantially high accuracy achieved by using features from the local search algorithm.

Detecting complexes from edge-weighted PPI networks via genes expression analysis.

PubMed

Zhang, Zehua; Song, Jian; Tang, Jijun; Xu, Xinying; Guo, Fei

2018-04-24

Identifying complexes from PPI networks has become a key problem to elucidate protein functions and identify signal and biological processes in a cell. Proteins binding as complexes are important roles of life activity. Accurate determination of complexes in PPI networks is crucial for understanding principles of cellular organization. We propose a novel method to identify complexes on PPI networks, based on different co-expression information. First, we use Markov Cluster Algorithm with an edge-weighting scheme to calculate complexes on PPI networks. Then, we propose some significant features, such as graph information and gene expression analysis, to filter and modify complexes predicted by Markov Cluster Algorithm. To evaluate our method, we test on two experimental yeast PPI networks. On DIP network, our method has Precision and F-Measure values of 0.6004 and 0.5528. On MIPS network, our method has F-Measure and S n values of 0.3774 and 0.3453. Comparing to existing methods, our method improves Precision value by at least 0.1752, F-Measure value by at least 0.0448, S n value by at least 0.0771. Experiments show that our method achieves better results than some state-of-the-art methods for identifying complexes on PPI networks, with the prediction quality improved in terms of evaluation criteria.

Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Modeling pilot interaction with automated digital avionics systems: Guidance and control algorithms for contour and nap-of-the-Earth flight

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1990-01-01

A collection of technical papers are presented that cover modeling pilot interaction with automated digital avionics systems and guidance and control algorithms for contour and nap-of-the-earth flight. The titles of the papers presented are as follows: (1) Automation effects in a multiloop manual control system; (2) A qualitative model of human interaction with complex dynamic systems; (3) Generalized predictive control of dynamic systems; (4) An application of generalized predictive control to rotorcraft terrain-following flight; (5) Self-tuning generalized predictive control applied to terrain-following flight; and (6) Precise flight path control using a predictive algorithm.

Space shuttle propulsion parameter estimation using optimal estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

The first twelve system state variables are presented with the necessary mathematical developments for incorporating them into the filter/smoother algorithm. Other state variables, i.e., aerodynamic coefficients can be easily incorporated into the estimation algorithm, representing uncertain parameters, but for initial checkout purposes are treated as known quantities. An approach for incorporating the NASA propulsion predictive model results into the optimal estimation algorithm was identified. This approach utilizes numerical derivatives and nominal predictions within the algorithm with global iterations of the algorithm. The iterative process is terminated when the quality of the estimates provided no longer significantly improves.

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.

A parallel algorithm for the initial screening of space debris collisions prediction using the SGP4/SDP4 models and GPU acceleration

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-05-01

Currently, a tremendous amount of space debris in Earth's orbit imperils operational spacecraft. It is essential to undertake risk assessments of collisions and predict dangerous encounters in space. However, collision predictions for an enormous amount of space debris give rise to large-scale computations. In this paper, a parallel algorithm is established on the Compute Unified Device Architecture (CUDA) platform of NVIDIA Corporation for collision prediction. According to the parallel structure of NVIDIA graphics processors, a block decomposition strategy is adopted in the algorithm. Space debris is divided into batches, and the computation and data transfer operations of adjacent batches overlap. As a consequence, the latency to access shared memory during the entire computing process is significantly reduced, and a higher computing speed is reached. Theoretically, a simulation of collision prediction for space debris of any amount and for any time span can be executed. To verify this algorithm, a simulation example including 1382 pieces of debris, whose operational time scales vary from 1 min to 3 days, is conducted on Tesla C2075 of NVIDIA. The simulation results demonstrate that with the same computational accuracy as that of a CPU, the computing speed of the parallel algorithm on a GPU is 30 times that on a CPU. Based on this algorithm, collision prediction of over 150 Chinese spacecraft for a time span of 3 days can be completed in less than 3 h on a single computer, which meets the timeliness requirement of the initial screening task. Furthermore, the algorithm can be adapted for multiple tasks, including particle filtration, constellation design, and Monte-Carlo simulation of an orbital computation.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Predicting the amount of coke deposition on catalyst pellets through image analysis and soft computing

NASA Astrophysics Data System (ADS)

Zhang, Jingqiong; Zhang, Wenbiao; He, Yuting; Yan, Yong

2016-11-01

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.

A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation.

PubMed

Layton, D M; Bundschuh, R

2005-01-01

Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.

Testing earthquake prediction algorithms: Statistically significant advance prediction of the largest earthquakes in the Circum-Pacific, 1992-1997

USGS Publications Warehouse

Kossobokov, V.G.; Romashkova, L.L.; Keilis-Borok, V. I.; Healy, J.H.

1999-01-01

Algorithms M8 and MSc (i.e., the Mendocino Scenario) were used in a real-time intermediate-term research prediction of the strongest earthquakes in the Circum-Pacific seismic belt. Predictions are made by M8 first. Then, the areas of alarm are reduced by MSc at the cost that some earthquakes are missed in the second approximation of prediction. In 1992-1997, five earthquakes of magnitude 8 and above occurred in the test area: all of them were predicted by M8 and MSc identified correctly the locations of four of them. The space-time volume of the alarms is 36% and 18%, correspondingly, when estimated with a normalized product measure of empirical distribution of epicenters and uniform time. The statistical significance of the achieved results is beyond 99% both for M8 and MSc. For magnitude 7.5 + , 10 out of 19 earthquakes were predicted by M8 in 40% and five were predicted by M8-MSc in 13% of the total volume considered. This implies a significance level of 81% for M8 and 92% for M8-MSc. The lower significance levels might result from a global change in seismic regime in 1993-1996, when the rate of the largest events has doubled and all of them become exclusively normal or reversed faults. The predictions are fully reproducible; the algorithms M8 and MSc in complete formal definitions were published before we started our experiment [Keilis-Borok, V.I., Kossobokov, V.G., 1990. Premonitory activation of seismic flow: Algorithm M8, Phys. Earth and Planet. Inter. 61, 73-83; Kossobokov, V.G., Keilis-Borok, V.I., Smith, S.W., 1990. Localization of intermediate-term earthquake prediction, J. Geophys. Res., 95, 19763-19772; Healy, J.H., Kossobokov, V.G., Dewey, J.W., 1992. A test to evaluate the earthquake prediction algorithm, M8. U.S. Geol. Surv. OFR 92-401]. M8 is available from the IASPEI Software Library [Healy, J.H., Keilis-Borok, V.I., Lee, W.H.K. (Eds.), 1997. Algorithms for Earthquake Statistics and Prediction, Vol. 6. IASPEI Software Library]. ?? 1999 Elsevier Science B.V. All rights reserved.

Hybrid Bearing Prognostic Test Rig

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Certo, Joseph M.; Handschuh, Robert F.; Dimofte, Florin

2005-01-01

The NASA Glenn Research Center has developed a new Hybrid Bearing Prognostic Test Rig to evaluate the performance of sensors and algorithms in predicting failures of rolling element bearings for aeronautics and space applications. The failure progression of both conventional and hybrid (ceramic rolling elements, metal races) bearings can be tested from fault initiation to total failure. The effects of different lubricants on bearing life can also be evaluated. Test conditions monitored and recorded during the test include load, oil temperature, vibration, and oil debris. New diagnostic research instrumentation will also be evaluated for hybrid bearing damage detection. This paper summarizes the capabilities of this new test rig.

Analysis of algorithms for predicting canopy fuel

Treesearch

Katharine L. Gray; Elizabeth Reinhardt

2003-01-01

We compared observed canopy fuel characteristics with those predicted by existing biomass algorithms. We specifically examined the accuracy of the biomass equations developed by Brown (1978. We used destructively sampled data obtained at 5 different study areas. We compared predicted and observed quantities of foliage and crown biomass for individual trees in our study...

Cost-effectiveness of HIV and syphilis antenatal screening: a modeling study

PubMed Central

Bristow, Claire C.; Larson, Elysia; Anderson, Laura J.; Klausner, Jeffrey D.

2016-01-01

Objectives The World Health Organization called for the elimination of maternal-to-child transmission (MTCT) of HIV and syphilis, a harmonized approach for the improvement of health outcomes for mothers and children. Testing early in pregnancy, treating seropositive pregnant women, and preventing syphilis re-infection can prevent MTCT of HIV and syphilis. We assessed the health and economic outcomes of a dual testing strategy in a simulated cohort of 100,000 antenatal care patients in Malawi. Methods We compared four screening algorithms: (1) HIV rapid test only, (2) dual HIV and syphilis rapid tests, (3) single rapid tests for HIV and syphilis, and (4) HIV rapid and syphilis laboratory tests. We calculated the expected number of adverse pregnancy outcomes, the expected costs, and the expected newborn disability adjusted life years (DALYs) for each screening algorithm. The estimated costs and DALYs for each screening algorithm were assessed from a societal perspective using Markov progression models. Additionally, we conducted a Monte Carlo multi-way sensitivity analysis, allowing for ranges of inputs. Results Our cohort decision model predicted the lowest number of adverse pregnancy outcomes in the dual HIV and syphilis rapid test strategy. Additionally, from the societal perspective, the costs of prevention and care using a dual HIV and syphilis rapid testing strategy was both the least costly ($226.92 per pregnancy) and resulted in the fewest DALYs (116,639) per 100,000 pregnancies. In the Monte Carlo simulation the dual HIV and syphilis algorithm was always cost saving and almost always reduced disability adjusted life years (DALYs) compared to HIV testing alone. Conclusion The results of the cost-effectiveness analysis showed that a dual HIV and syphilis test was cost saving compared to all other screening strategies. Adding dual rapid testing to the existing prevention of mother-to-child HIV transmission programs in Malawi and similar countries is likely to be advantageous. PMID:26920867

Algorithm for Lossless Compression of Calibrated Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2010-01-01

A two-stage predictive method was developed for lossless compression of calibrated hyperspectral imagery. The first prediction stage uses a conventional linear predictor intended to exploit spatial and/or spectral dependencies in the data. The compressor tabulates counts of the past values of the difference between this initial prediction and the actual sample value. To form the ultimate predicted value, in the second stage, these counts are combined with an adaptively updated weight function intended to capture information about data regularities introduced by the calibration process. Finally, prediction residuals are losslessly encoded using adaptive arithmetic coding. Algorithms of this type are commonly tested on a readily available collection of images from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral imager. On the standard calibrated AVIRIS hyperspectral images that are most widely used for compression benchmarking, the new compressor provides more than 0.5 bits/sample improvement over the previous best compression results. The algorithm has been implemented in Mathematica. The compression algorithm was demonstrated as beneficial on 12-bit calibrated AVIRIS images.

Personalized Physical Activity Coaching: A Machine Learning Approach

PubMed Central

Dijkhuis, Talko B.; van Ittersum, Miriam W.; Velthuijsen, Hugo

2018-01-01

Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant’s progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time. PMID:29463052

Algorithm aversion: people erroneously avoid algorithms after seeing them err.

PubMed

Dietvorst, Berkeley J; Simmons, Joseph P; Massey, Cade

2015-02-01

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

Predicting the onset of hazardous alcohol drinking in primary care: development and validation of a simple risk algorithm

PubMed Central

Bellón, Juan Ángel; de Dios Luna, Juan; King, Michael; Nazareth, Irwin; Motrico, Emma; GildeGómez-Barragán, María Josefa; Torres-González, Francisco; Montón-Franco, Carmen; Sánchez-Celaya, Marta; Díaz-Barreiros, Miguel Ángel; Vicens, Catalina; Moreno-Peral, Patricia

2017-01-01

Background Little is known about the risk of progressing to hazardous alcohol use in abstinent or low-risk drinkers. Aim To develop and validate a simple brief risk algorithm for the onset of hazardous alcohol drinking (HAD) over 12 months for use in primary care. Design and setting Prospective cohort study in 32 health centres from six Spanish provinces, with evaluations at baseline, 6 months, and 12 months. Method Forty-one risk factors were measured and multilevel logistic regression and inverse probability weighting were used to build the risk algorithm. The outcome was new occurrence of HAD during the study, as measured by the AUDIT. Results From the lists of 174 GPs, 3954 adult abstinent or low-risk drinkers were recruited. The ‘predictAL-10’ risk algorithm included just nine variables (10 questions): province, sex, age, cigarette consumption, perception of financial strain, having ever received treatment for an alcohol problem, childhood sexual abuse, AUDIT-C, and interaction AUDIT-C*Age. The c-index was 0.886 (95% CI = 0.854 to 0.918). The optimal cutoff had a sensitivity of 0.83 and specificity of 0.80. Excluding childhood sexual abuse from the model (the ‘predictAL-9’), the c-index was 0.880 (95% CI = 0.847 to 0.913), sensitivity 0.79, and specificity 0.81. There was no statistically significant difference between the c-indexes of predictAL-10 and predictAL-9. Conclusion The predictAL-10/9 is a simple and internally valid risk algorithm to predict the onset of hazardous alcohol drinking over 12 months in primary care attendees; it is a brief tool that is potentially useful for primary prevention of hazardous alcohol drinking. PMID:28360074

The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion.

PubMed

Borkowski, Piotr

2017-06-20

It is essential for the marine navigator conducting maneuvers of his ship at sea to know future positions of himself and target ships in a specific time span to effectively solve collision situations. This article presents an algorithm of ship movement trajectory prediction, which, through data fusion, takes into account measurements of the ship's current position from a number of doubled autonomous devices. This increases the reliability and accuracy of prediction. The algorithm has been implemented in NAVDEC, a navigation decision support system and practically used on board ships.

XtalOpt  version r9: An open-source evolutionary algorithm for crystal structure prediction

DOE PAGES

Falls, Zackary; Lonie, David C.; Avery, Patrick; ...

2015-10-23

This is a new version of XtalOpt, an evolutionary algorithm for crystal structure prediction available for download from the CPC library or the XtalOpt website, http://xtalopt.github.io. XtalOpt is published under the Gnu Public License (GPL), which is an open source license that is recognized by the Open Source Initiative. We have detailed the new version incorporates many bug-fixes and new features here and predict the crystal structure of a system from its stoichiometry alone, using evolutionary algorithms.

The Ship Movement Trajectory Prediction Algorithm Using Navigational Data Fusion

PubMed Central

Borkowski, Piotr

2017-01-01

It is essential for the marine navigator conducting maneuvers of his ship at sea to know future positions of himself and target ships in a specific time span to effectively solve collision situations. This article presents an algorithm of ship movement trajectory prediction, which, through data fusion, takes into account measurements of the ship’s current position from a number of doubled autonomous devices. This increases the reliability and accuracy of prediction. The algorithm has been implemented in NAVDEC, a navigation decision support system and practically used on board ships. PMID:28632176

A recurrence-weighted prediction algorithm for musical analysis

NASA Astrophysics Data System (ADS)

Colucci, Renato; Leguizamon Cucunuba, Juan Sebastián; Lloyd, Simon

2018-03-01

Forecasting the future behaviour of a system using past data is an important topic. In this article we apply nonlinear time series analysis in the context of music, and present new algorithms for extending a sample of music, while maintaining characteristics similar to the original piece. By using ideas from ergodic theory, we adapt the classical prediction method of Lorenz analogues so as to take into account recurrence times, and demonstrate with examples, how the new algorithm can produce predictions with a high degree of similarity to the original sample.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

An O(n(5)) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids.

PubMed

Chen, Ho-Lin; Condon, Anne; Jabbari, Hosna

2009-06-01

Efficient methods for prediction of minimum free energy (MFE) nucleic secondary structures are widely used, both to better understand structure and function of biological RNAs and to design novel nano-structures. Here, we present a new algorithm for MFE secondary structure prediction, which significantly expands the class of structures that can be handled in O(n(5)) time. Our algorithm can handle H-type pseudoknotted structures, kissing hairpins, and chains of four overlapping stems, as well as nested substructures of these types.

DVD-COOP: Innovative Conjunction Prediction Using Voronoi-filter based on the Dynamic Voronoi Diagram of 3D Spheres

NASA Astrophysics Data System (ADS)

Cha, J.; Ryu, J.; Lee, M.; Song, C.; Cho, Y.; Schumacher, P.; Mah, M.; Kim, D.

Conjunction prediction is one of the critical operations in space situational awareness (SSA). For geospace objects, common algorithms for conjunction prediction are usually based on all-pairwise check, spatial hash, or kd-tree. Computational load is usually reduced through some filters. However, there exists a good chance of missing potential collisions between space objects. We present a novel algorithm which both guarantees no missing conjunction and is efficient to answer to a variety of spatial queries including pairwise conjunction prediction. The algorithm takes only O(k log N) time for N objects in the worst case to answer conjunctions where k is a constant which is linear to prediction time length. The proposed algorithm, named DVD-COOP (Dynamic Voronoi Diagram-based Conjunctive Orbital Object Predictor), is based on the dynamic Voronoi diagram of moving spherical balls in 3D space. The algorithm has a preprocessing which consists of two steps: The construction of an initial Voronoi diagram (taking O(N) time on average) and the construction of a priority queue for the events of topology changes in the Voronoi diagram (taking O(N log N) time in the worst case). The scalability of the proposed algorithm is also discussed. We hope that the proposed Voronoi-approach will change the computational paradigm in spatial reasoning among space objects.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

RNA design using simulated SHAPE data.

PubMed

Lotfi, Mohadeseh; Zare-Mirakabad, Fatemeh; Montaseri, Soheila

2018-05-03

It has long been established that in addition to being involved in protein translation, RNA plays essential roles in numerous other cellular processes, including gene regulation and DNA replication. Such roles are known to be dictated by higher-order structures of RNA molecules. It is therefore of prime importance to find an RNA sequence that can fold to acquire a particular function that is desirable for use in pharmaceuticals and basic research. The challenge of finding an RNA sequence for a given structure is known as the RNA design problem. Although there are several algorithms to solve this problem, they mainly consider hard constraints, such as minimum free energy, to evaluate the predicted sequences. Recently, SHAPE data has emerged as a new soft constraint for RNA secondary structure prediction. To take advantage of this new experimental constraint, we report here a new method for accurate design of RNA sequences based on their secondary structures using SHAPE data as pseudo-free energy. We then compare our algorithm with four others: INFO-RNA, ERD, MODENA and RNAifold 2.0. Our algorithm precisely predicts 26 out of 29 new sequences for the structures extracted from the Rfam dataset, while the other four algorithms predict no more than 22 out of 29. The proposed algorithm is comparable to the above algorithms on RNA-SSD datasets, where they can predict up to 33 appropriate sequences for RNA secondary structures out of 34.

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.

PubMed

Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch

2017-06-06

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

PubMed Central

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

PubMed

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

PubMed

Muhlestein, Whitney E; Akagi, Dallin S; Kallos, Justiss A; Morone, Peter J; Weaver, Kyle D; Thompson, Reid C; Chambless, Lola B

2018-04-01

Objective  Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods  A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results  Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p  = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion  Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity.

PubMed

Webb, Samuel J; Hanser, Thierry; Howlin, Brendan; Krause, Paul; Vessey, Jonathan D

2014-03-25

A new algorithm has been developed to enable the interpretation of black box models. The developed algorithm is agnostic to learning algorithm and open to all structural based descriptors such as fragments, keys and hashed fingerprints. The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.A fragmentation algorithm is utilised to investigate the model's behaviour on specific substructures present in the query. An output is formulated summarising causes of activation and deactivation. The algorithm is able to identify multiple causes of activation or deactivation in addition to identifying localised deactivations where the prediction for the query is active overall. No loss in performance is seen as there is no change in the prediction; the interpretation is produced directly on the model's behaviour for the specific query. Models have been built using multiple learning algorithms including support vector machine and random forest. The models were built on public Ames mutagenicity data and a variety of fingerprint descriptors were used. These models produced a good performance in both internal and external validation with accuracies around 82%. The models were used to evaluate the interpretation algorithm. Interpretation was revealed that links closely with understood mechanisms for Ames mutagenicity. This methodology allows for a greater utilisation of the predictions made by black box models and can expedite further study based on the output for a (quantitative) structure activity model. Additionally the algorithm could be utilised for chemical dataset investigation and knowledge extraction/human SAR development.

Mobile Sensing in Environmental Health and Neighborhood Research.

PubMed

Chaix, Basile

2018-04-01

Public health research has witnessed a rapid development in the use of location, environmental, behavioral, and biophysical sensors that provide high-resolution objective time-stamped data. This burgeoning field is stimulated by the development of novel multisensor devices that collect data for an increasing number of channels and algorithms that predict relevant dimensions from one or several data channels. Global positioning system (GPS) tracking, which enables geographic momentary assessment, permits researchers to assess multiplace personal exposure areas and the algorithm-based identification of trips and places visited, eventually validated and complemented using a GPS-based mobility survey. These methods open a new space-time perspective that considers the full dynamic of residential and nonresidential momentary exposures; spatially and temporally disaggregates the behavioral and health outcomes, thus replacing them in their immediate environmental context; investigates complex time sequences; explores the interplay among individual, environmental, and situational predictors; performs life-segment analyses considering infraindividual statistical units using case-crossover models; and derives recommendations for just-in-time interventions.

An unsupervised classification scheme for improving predictions of prokaryotic TIS.

PubMed

Tech, Maike; Meinicke, Peter

2006-03-09

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

Crack Modelling for Radiography

NASA Astrophysics Data System (ADS)

Chady, T.; Napierała, L.

2010-02-01

In this paper, possibility of creation of three-dimensional crack models, both random type and based on real-life radiographic images is discussed. Method for storing cracks in a number of two-dimensional matrices, as well algorithm for their reconstruction into three-dimensional objects is presented. Also the possibility of using iterative algorithm for matching simulated images of cracks to real-life radiographic images is discussed.

Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

PubMed Central

Tighe, Patrick J.; Harle, Christopher A.; Hurley, Robert W.; Aytug, Haldun; Boezaart, Andre P.; Fillingim, Roger B.

2015-01-01

Background Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain. Methods Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor, with logistic regression included for baseline comparison. Results In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-nearest neighbor algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3. Conclusions Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction. PMID:26031220

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast.

PubMed

Lai, Fu-Jou; Chang, Hong-Tsun; Wu, Wei-Sheng

2015-01-01

Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs.

PCTFPeval: a web tool for benchmarking newly developed algorithms for predicting cooperative transcription factor pairs in yeast

PubMed Central

2015-01-01

Background Computational identification of cooperative transcription factor (TF) pairs helps understand the combinatorial regulation of gene expression in eukaryotic cells. Many advanced algorithms have been proposed to predict cooperative TF pairs in yeast. However, it is still difficult to conduct a comprehensive and objective performance comparison of different algorithms because of lacking sufficient performance indices and adequate overall performance scores. To solve this problem, in our previous study (published in BMC Systems Biology 2014), we adopted/proposed eight performance indices and designed two overall performance scores to compare the performance of 14 existing algorithms for predicting cooperative TF pairs in yeast. Most importantly, our performance comparison framework can be applied to comprehensively and objectively evaluate the performance of a newly developed algorithm. However, to use our framework, researchers have to put a lot of effort to construct it first. To save researchers time and effort, here we develop a web tool to implement our performance comparison framework, featuring fast data processing, a comprehensive performance comparison and an easy-to-use web interface. Results The developed tool is called PCTFPeval (Predicted Cooperative TF Pair evaluator), written in PHP and Python programming languages. The friendly web interface allows users to input a list of predicted cooperative TF pairs from their algorithm and select (i) the compared algorithms among the 15 existing algorithms, (ii) the performance indices among the eight existing indices, and (iii) the overall performance scores from two possible choices. The comprehensive performance comparison results are then generated in tens of seconds and shown as both bar charts and tables. The original comparison results of each compared algorithm and each selected performance index can be downloaded as text files for further analyses. Conclusions Allowing users to select eight existing performance indices and 15 existing algorithms for comparison, our web tool benefits researchers who are eager to comprehensively and objectively evaluate the performance of their newly developed algorithm. Thus, our tool greatly expedites the progress in the research of computational identification of cooperative TF pairs. PMID:26677932

Accounting of fundamental components of the rotation parameters of the Earth in the formation of a high-accuracy orbit of navigation satellites

NASA Astrophysics Data System (ADS)

Markov, Yu. G.; Mikhailov, M. V.; Pochukaev, V. N.

2012-07-01

An analysis of perturbing factors influencing the motion of a navigation satellite (NS) is carried out, and the degree of influence of each factor on the GLONASS orbit is estimated. It is found that fundamental components of the Earth's rotation parameters (ERP) are one substantial factor commensurable with maximum perturbations. Algorithms for the calculation of orbital perturbations caused by these parameters are given; these algorithms can be implemented in a consumer's equipment. The daily prediction of NS coordinates is performed on the basis of real GLONASS satellite ephemerides transmitted to a consumer, using the developed prediction algorithms taking the ERP into account. The obtained accuracy of the daily prediction of GLONASS ephemerides exceeds by tens of times the accuracy of the daily prediction performed using algorithms recommended in interface control documents.

The Breakthrough Listen Search for Intelligent Life: Radio Frequency Interference in the Green Bank Telescope

NASA Astrophysics Data System (ADS)

Dana, Ryan

2018-01-01

In the search for extra terrestrial intelligence, the vast majority of our “signals of interest,” are simply satellite radio frequency interference. The goal to my research, therefore, was to accurately predict the exact locations of satellites in our sky to analyze specific satellites causing the interference as well as potentially predict when satellites will cross in the way of our beams so that we can further optimize our scripts and get more usable data.I have built an algorithm that plots the exact location in altitude and azimuth of any grouping of satellites that you want in the sky from any position on earth in latitude, longitude, and elevation. From there, you can input a specific right ascension and declination of the location you are trying to track in the sky with a telescope. Using these inputs, we can calculate the angular and positional distance of certain satellites to our beam to further analyze satellite radio frequency interference.The process begins by importing a list of Two Line Element information that the algorithm reads in. Two Line Elements are how Satellites are organized and are updated frequently. They give a variety of information ranging from the Satellite ID to its Mean Motion or anomaly. From there, the code breaks up the information given by these elements to predict their location. The algorithm can also plot in 3D coordinates around an earthlike sphere to conceptualize the route that each Satellite has taken.The code has been used in a variety of ways but most notably to identify satellites interfering with the beam for Arecibo’s Ross 128 Candidate signal. From here, the code will be the backbone to calculating drift rates, Doppler shifts and intensity of certain satellites and why our team consistently receives estranged satellite signals of interest. Furthermore, in the case of a serious candidate signal in the near future, it will be important to analyze satellites interfering in the beam.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

PubMed

Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Carson, John M., III

2007-01-01

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

Demonstration of the use of ADAPT to derive predictive maintenance algorithms for the KSC central heat plant

NASA Technical Reports Server (NTRS)

Hunter, H. E.

1972-01-01

The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.

Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis.

PubMed

Peng, Jiangtao; Peng, Silong; Xie, Qiong; Wei, Jiping

2011-04-01

In order to eliminate the lower order polynomial interferences, a new quantitative calibration algorithm "Baseline Correction Combined Partial Least Squares (BCC-PLS)", which combines baseline correction and conventional PLS, is proposed. By embedding baseline correction constraints into PLS weights selection, the proposed calibration algorithm overcomes the uncertainty in baseline correction and can meet the requirement of on-line attenuated total reflectance Fourier transform infrared (ATR-FTIR) quantitative analysis. The effectiveness of the algorithm is evaluated by the analysis of glucose and marzipan ATR-FTIR spectra. BCC-PLS algorithm shows improved prediction performance over PLS. The root mean square error of cross-validation (RMSECV) on marzipan spectra for the prediction of the moisture is found to be 0.53%, w/w (range 7-19%). The sugar content is predicted with a RMSECV of 2.04%, w/w (range 33-68%). Copyright © 2011 Elsevier B.V. All rights reserved.

Algorithms and the Future of Music Education: A Response to Shuler

ERIC Educational Resources Information Center

Thibeault, Matthew D.

2014-01-01

This article is a response to Shuler's 2001 article predicting the future of music education. The respondent assesses Shuler's predictions, finding that many have come true but critiquing Shuler's overall positive assessment. The respondent then goes on to make one prediction about the future of music education: that algorithms will…

Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

PubMed

Schreuder, Jan J; Castiglioni, Alessandro; Donelli, Andrea; Maisano, Francesco; Jansen, Jos R C; Hanania, Ramzi; Hanlon, Pat; Bovelander, Jan; Alfieri, Ottavio

2005-03-01

The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission: A Multicenter Prospective Study.

PubMed

Mearelli, Filippo; Fiotti, Nicola; Giansante, Carlo; Casarsa, Chiara; Orso, Daniele; De Helmersen, Marco; Altamura, Nicola; Ruscio, Maurizio; Castello, Luigi Mario; Colonetti, Efrem; Marino, Rossella; Barbati, Giulia; Bregnocchi, Andrea; Ronco, Claudio; Lupia, Enrico; Montrucchio, Giuseppe; Muiesan, Maria Lorenza; Di Somma, Salvatore; Avanzi, Gian Carlo; Biolo, Gianni

2018-05-07

To derive and validate a predictive algorithm integrating a nomogram-based prediction of the pretest probability of infection with a panel of serum biomarkers, which could robustly differentiate sepsis/septic shock from noninfectious systemic inflammatory response syndrome. Multicenter prospective study. At emergency department admission in five University hospitals. Nine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort. None. A nomogram, including age, Sequential Organ Failure Assessment score, recent antimicrobial therapy, hyperthermia, leukocytosis, and high C-reactive protein values, was built in order to take data from 716 infected patients and 120 patients with noninfectious systemic inflammatory response syndrome to predict pretest probability of infection. Then, the best combination of procalcitonin, soluble phospholypase A2 group IIA, presepsin, soluble interleukin-2 receptor α, and soluble triggering receptor expressed on myeloid cell-1 was applied in order to categorize patients as "likely" or "unlikely" to be infected. The predictive algorithm required only procalcitonin backed up with soluble phospholypase A2 group IIA determined in 29% of the patients to rule out sepsis/septic shock with a negative predictive value of 93%. In a validation cohort of 158 patients, predictive algorithm reached 100% of negative predictive value requiring biomarker measurements in 18% of the population. We have developed and validated a high-performing, reproducible, and parsimonious algorithm to assist emergency department physicians in distinguishing sepsis/septic shock from noninfectious systemic inflammatory response syndrome.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.

PubMed

Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan

2012-01-01

Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

High-speed prediction of crystal structures for organic molecules

NASA Astrophysics Data System (ADS)

Obata, Shigeaki; Goto, Hitoshi

2015-02-01

We developed a master-worker type parallel algorithm for allocating tasks of crystal structure optimizations to distributed compute nodes, in order to improve a performance of simulations for crystal structure predictions. The performance experiments were demonstrated on TUT-ADSIM supercomputer system (HITACHI HA8000-tc/HT210). The experimental results show that our parallel algorithm could achieve speed-ups of 214 and 179 times using 256 processor cores on crystal structure optimizations in predictions of crystal structures for 3-aza-bicyclo(3.3.1)nonane-2,4-dione and 2-diazo-3,5-cyclohexadiene-1-one, respectively. We expect that this parallel algorithm is always possible to reduce computational costs of any crystal structure predictions.

Ultrasonic prediction of term birth weight in Hispanic women. Accuracy in an outpatient clinic.

PubMed

Nahum, Gerard G; Pham, Krystle Q; McHugh, John P

2003-01-01

To investigate the accuracy of ultrasonic fetal biometric algorithms for estimating term fetal weight. Ultrasonographic fetal biometric assessments were made in 74 Hispanic women who delivered at 37-42 weeks of gestation. Measurements were taken of the fetal biparietal diameter, head circumference, abdominal circumference and femur length. Twenty-seven standard fetal biometric algorithms were assessed for their accuracy in predicting fetal weight. Results were compared to those obtained by merely guessing the mean term birth weight in each case. The correlation between ultrasonically predicted and actual birth weights ranged from 0.52 to 0.79. The different ultrasonic algorithms estimated fetal weight to within +/- 8.6-15.0% (+/- 295-520 g) of actual birth weight as compared with +/- 13.6% (+/- 449 g) for guessing the mean birth weight in each case (mean +/- SD). The mean absolute prediction errors for 17 of the ultrasonic equations (63%) were superior to those obtained by guessing the mean birth weight by 3.2-5.0% (96-154 g) (P < .05). Fourteen algorithms (52%) were more accurate for predicting fetal weight to within +/- 15%, and 20 algorithms (74%) were more accurate for predicting fetal weight to within +/- 10% of actual birth weight than simply guessing the mean birth weight (P < .05). Ten ultrasonic equations (37%) showed significant utility for predicting fetal weight > 4,000 g (likelihood ratio > 5.0). Term fetal weight predictions using the majority of sonographic fetal biometric equations are more accurate, by up to 154 g and 5%, than simply guessing the population-specific mean birth weight.

A utility/cost analysis of breast cancer risk prediction algorithms

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

2016-03-01

Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests.

PubMed

Hu, Chen; Steingrimsson, Jon Arni

2018-01-01

A crucial component of making individualized treatment decisions is to accurately predict each patient's disease risk. In clinical oncology, disease risks are often measured through time-to-event data, such as overall survival and progression/recurrence-free survival, and are often subject to censoring. Risk prediction models based on recursive partitioning methods are becoming increasingly popular largely due to their ability to handle nonlinear relationships, higher-order interactions, and/or high-dimensional covariates. The most popular recursive partitioning methods are versions of the Classification and Regression Tree (CART) algorithm, which builds a simple interpretable tree structured model. With the aim of increasing prediction accuracy, the random forest algorithm averages multiple CART trees, creating a flexible risk prediction model. Risk prediction models used in clinical oncology commonly use both traditional demographic and tumor pathological factors as well as high-dimensional genetic markers and treatment parameters from multimodality treatments. In this article, we describe the most commonly used extensions of the CART and random forest algorithms to right-censored outcomes. We focus on how they differ from the methods for noncensored outcomes, and how the different splitting rules and methods for cost-complexity pruning impact these algorithms. We demonstrate these algorithms by analyzing a randomized Phase III clinical trial of breast cancer. We also conduct Monte Carlo simulations to compare the prediction accuracy of survival forests with more commonly used regression models under various scenarios. These simulation studies aim to evaluate how sensitive the prediction accuracy is to the underlying model specifications, the choice of tuning parameters, and the degrees of missing covariates.

Estimation of State Transition Probabilities: A Neural Network Model

NASA Astrophysics Data System (ADS)

Saito, Hiroshi; Takiyama, Ken; Okada, Masato

2015-12-01

Humans and animals can predict future states on the basis of acquired knowledge. This prediction of the state transition is important for choosing the best action, and the prediction is only possible if the state transition probability has already been learned. However, how our brains learn the state transition probability is unknown. Here, we propose a simple algorithm for estimating the state transition probability by utilizing the state prediction error. We analytically and numerically confirmed that our algorithm is able to learn the probability completely with an appropriate learning rate. Furthermore, our learning rule reproduced experimentally reported psychometric functions and neural activities in the lateral intraparietal area in a decision-making task. Thus, our algorithm might describe the manner in which our brains learn state transition probabilities and predict future states.

Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder.

PubMed

Bartsch, Georg; Mitra, Anirban P; Mitra, Sheetal A; Almal, Arpit A; Steven, Kenneth E; Skinner, Donald G; Fry, David W; Lenehan, Peter F; Worzel, William P; Cote, Richard J

2016-02-01

Due to the high recurrence risk of nonmuscle invasive urothelial carcinoma it is crucial to distinguish patients at high risk from those with indolent disease. In this study we used a machine learning algorithm to identify the genes in patients with nonmuscle invasive urothelial carcinoma at initial presentation that were most predictive of recurrence. We used the genes in a molecular signature to predict recurrence risk within 5 years after transurethral resection of bladder tumor. Whole genome profiling was performed on 112 frozen nonmuscle invasive urothelial carcinoma specimens obtained at first presentation on Human WG-6 BeadChips (Illumina®). A genetic programming algorithm was applied to evolve classifier mathematical models for outcome prediction. Cross-validation based resampling and gene use frequencies were used to identify the most prognostic genes, which were combined into rules used in a voting algorithm to predict the sample target class. Key genes were validated by quantitative polymerase chain reaction. The classifier set included 21 genes that predicted recurrence. Quantitative polymerase chain reaction was done for these genes in a subset of 100 patients. A 5-gene combined rule incorporating a voting algorithm yielded 77% sensitivity and 85% specificity to predict recurrence in the training set, and 69% and 62%, respectively, in the test set. A singular 3-gene rule was constructed that predicted recurrence with 80% sensitivity and 90% specificity in the training set, and 71% and 67%, respectively, in the test set. Using primary nonmuscle invasive urothelial carcinoma from initial occurrences genetic programming identified transcripts in reproducible fashion, which were predictive of recurrence. These findings could potentially impact nonmuscle invasive urothelial carcinoma management. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Network-based ranking methods for prediction of novel disease associated microRNAs.

PubMed

Le, Duc-Hau

2015-10-01

Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer. Network-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Single-step methods for predicting orbital motion considering its periodic components

NASA Astrophysics Data System (ADS)

Lavrov, K. N.

1989-01-01

Modern numerical methods for integration of ordinary differential equations can provide accurate and universal solutions to celestial mechanics problems. The implicit single sequence algorithms of Everhart and multiple step computational schemes using a priori information on periodic components can be combined to construct implicit single sequence algorithms which combine their advantages. The construction and analysis of the properties of such algorithms are studied, utilizing trigonometric approximation of the solutions of differential equations containing periodic components. The algorithms require 10 percent more machine memory than the Everhart algorithms, but are twice as fast, and yield short term predictions valid for five to ten orbits with good accuracy and five to six times faster than algorithms using other methods.

Real Time Monitoring and Prediction of the Monsoon Intraseasonal Oscillations: An index based on Nonlinear Laplacian Spectral Analysis Technique

NASA Astrophysics Data System (ADS)

Cherumadanakadan Thelliyil, S.; Ravindran, A. M.; Giannakis, D.; Majda, A.

2016-12-01

An improved index for real time monitoring and forecast verification of monsoon intraseasonal oscillations (MISO) is introduced using the recently developed Nonlinear Laplacian Spectral Analysis (NLSA) algorithm. Previous studies has demonstrated the proficiency of NLSA in capturing low frequency variability and intermittency of a time series. Using NLSA a hierarchy of Laplace-Beltrami (LB) eigen functions are extracted from the unfiltered daily GPCP rainfall data over the south Asian monsoon region. Two modes representing the full life cycle of complex northeastward propagating boreal summer MISO are identified from the hierarchy of Laplace-Beltrami eigen functions. These two MISO modes have a number of advantages over the conventionally used Extended Empirical Orthogonal Function (EEOF) MISO modes including higher memory and better predictability, higher fractional variance over the western Pacific, Western Ghats and adjoining Arabian Sea regions and more realistic representation of regional heat sources associated with the MISO. The skill of NLSA based MISO indices in real time prediction of MISO is demonstrated using hindcasts of CFSv2 extended range prediction runs. It is shown that these indices yield a higher prediction skill than the other conventional indices supporting the use of NLSA in real time prediction of MISO. Real time monitoring and prediction of MISO finds its application in agriculture, construction and hydro-electric power sectors and hence an important component of monsoon prediction.

An algorithm for direct causal learning of influences on patient outcomes.

PubMed

Rathnam, Chandramouli; Lee, Sanghoon; Jiang, Xia

2017-01-01

This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms. The DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14,400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-score based fast greedy search (FGS) algorithm, and the partial ancestral graphs algorithm fast causal inference (FCI). In addition, we extend our comparison of all five algorithms to both a real GWAS dataset and real breast cancer datasets over various time-points in order to observe how effective they are at predicting the causal influences of Alzheimer's disease and breast cancer survival. DCL consistently outperforms FGS, PC, CPC, and FCI in discovering the parents of the target for the datasets simulated using a simple network. Overall, DCL predicts significantly more datasets correctly (McNemar's test significance: p<0.0001) than any of the other algorithms for these network types. For example, when assessing overall performance (simple and complex network results combined), DCL correctly predicts approximately 1400 more datasets than the top FGS method, 1600 more datasets than the top CPC method, 4500 more datasets than the top PC method, and 5600 more datasets than the top FCI method. Although FGS did correctly predict more datasets than DCL for the complex networks, and DCL correctly predicted only a few more datasets than CPC for these networks, there is no significant difference in performance between these three algorithms for this network type. However, when we use a more continuous measure of accuracy, we find that all the DCL methods are able to better partially predict more direct causes than FGS and CPC for the complex networks. In addition, DCL consistently had faster runtimes than the other algorithms. In the application to the real datasets, DCL identified rs6784615, located on the NISCH gene, and rs10824310, located on the PRKG1 gene, as direct causes of late onset Alzheimer's disease (LOAD) development. In addition, DCL identified ER category as a direct predictor of breast cancer mortality within 5 years, and HER2 status as a direct predictor of 10-year breast cancer mortality. These predictors have been identified in previous studies to have a direct causal relationship with their respective phenotypes, supporting the predictive power of DCL. When the other algorithms discovered predictors from the real datasets, these predictors were either also found by DCL or could not be supported by previous studies. Our results show that DCL outperforms FGS, PC, CPC, and FCI in almost every case, demonstrating its potential to advance causal learning. Furthermore, our DCL algorithm effectively identifies direct causes in the LOAD and Metabric GWAS datasets, which indicates its potential for clinical applications. Copyright © 2016 Elsevier B.V. All rights reserved.

Research on conceptual/innovative design for the life cycle

NASA Technical Reports Server (NTRS)

Cagan, Jonathan; Agogino, Alice M.

1990-01-01

The goal of this research is developing and integrating qualitative and quantitative methods for life cycle design. The definition of the problem includes formal computer-based methods limited to final detailing stages of design; CAD data bases do not capture design intent or design history; and life cycle issues were ignored during early stages of design. Viewgraphs outline research in conceptual design; the SYMON (SYmbolic MONotonicity analyzer) algorithm; multistart vector quantization optimization algorithm; intelligent manufacturing: IDES - Influence Diagram Architecture; and 1st PRINCE (FIRST PRINciple Computational Evaluator).

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

PubMed

Sartori, Juliana M; Reckziegel, Ramiro; Passos, Ives Cavalcante; Czepielewski, Leticia S; Fijtman, Adam; Sodré, Leonardo A; Massuda, Raffael; Goi, Pedro D; Vianna-Sulzbach, Miréia; Cardoso, Taiane de Azevedo; Kapczinski, Flávio; Mwangi, Benson; Gama, Clarissa S

2018-08-01

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Life Extending Control. [mechanical fatigue in reusable rocket engines

NASA Technical Reports Server (NTRS)

Lorenzo, Carl F.; Merrill, Walter C.

1991-01-01

The concept of Life Extending Control is defined. Life is defined in terms of mechanical fatigue life. A brief description is given of the current approach to life prediction using a local, cyclic, stress-strain approach for a critical system component. An alternative approach to life prediction based on a continuous functional relationship to component performance is proposed. Based on cyclic life prediction, an approach to life extending control, called the Life Management Approach, is proposed. A second approach, also based on cyclic life prediction, called the implicit approach, is presented. Assuming the existence of the alternative functional life prediction approach, two additional concepts for Life Extending Control are presented.

Life extending control: A concept paper

NASA Technical Reports Server (NTRS)

Lorenzo, Carl F.; Merrill, Walter C.

1991-01-01

The concept of Life Extending Control is defined. Life is defined in terms of mechanical fatigue life. A brief description is given of the current approach to life prediction using a local, cyclic, stress-strain approach for a critical system component. An alternative approach to life prediction based on a continuous functional relationship to component performance is proposed.Base on cyclic life prediction an approach to Life Extending Control, called the Life Management Approach is proposed. A second approach, also based on cyclic life prediction, called the Implicit Approach, is presented. Assuming the existence of the alternative functional life prediction approach, two additional concepts for Life Extending Control are presented.

Planning Readings: A Comparative Exploration of Basic Algorithms

ERIC Educational Resources Information Center

Piater, Justus H.

2009-01-01

Conventional introduction to computer science presents individual algorithmic paradigms in the context of specific, prototypical problems. To complement this algorithm-centric instruction, this study additionally advocates problem-centric instruction. I present an original problem drawn from students' life that is simply stated but provides rich…

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is suited for nonlinear optimization problems. The parallel computation of the algorithm exploits iterative linear algebra methods for the main linear algebra computations in the algorithm. We show that the splitting of the algorithm is flexible and can thus be applied to various distributed platform configurations. The two proposed algorithms are applied to two main energy and transportation control problems. The first application is energy efficient building control. Buildings represent 40% of energy consumption in the United States. Thus, it is significant to improve the energy efficiency of buildings. The goal is to minimize energy consumption subject to the physics of the building (e.g. heat transfer laws), the constraints of the actuators as well as the desired operating constraints (thermal comfort of the occupants), and heat load on the system. In this thesis, we describe the control systems of forced air building systems in practice. We discuss the "Trim and Respond" algorithm which is a distributed control algorithm that is used in practice, and show that it performs similarly to a one-step explicit DMPC algorithm. Then, we apply the novel distributed primal-dual active-set method and provide extensive numerical results for the building MPC problem. The second main application is the control of ramp metering signals to optimize traffic flow through a freeway system. This application is particularly important since urban congestion has more than doubled in the past few decades. The ramp metering problem is to maximize freeway throughput subject to freeway dynamics (derived from mass conservation), actuation constraints, freeway capacity constraints, and predicted traffic demand. In this thesis, we develop a hybrid model predictive controller for ramp metering that is guaranteed to be persistently feasible and stable. This contrasts to previous work on MPC for ramp metering where such guarantees are absent. We apply a smoothing method to the hybrid model predictive controller and apply the inexact interior point method to this nonlinear non-convex ramp metering problem.

Metaheuristic optimization approaches to predict shear-wave velocity from conventional well logs in sandstone and carbonate case studies

NASA Astrophysics Data System (ADS)

Emami Niri, Mohammad; Amiri Kolajoobi, Rasool; Khodaiy Arbat, Mohammad; Shahbazi Raz, Mahdi

2018-06-01

Seismic wave velocities, along with petrophysical data, provide valuable information during the exploration and development stages of oil and gas fields. The compressional-wave velocity (VP ) is acquired using conventional acoustic logging tools in many drilled wells. But the shear-wave velocity (VS ) is recorded using advanced logging tools only in a limited number of wells, mainly because of the high operational costs. In addition, laboratory measurements of seismic velocities on core samples are expensive and time consuming. So, alternative methods are often used to estimate VS . Heretofore, several empirical correlations that predict VS by using well logging measurements and petrophysical data such as VP , porosity and density are proposed. However, these empirical relations can only be used in limited cases. The use of intelligent systems and optimization algorithms are inexpensive, fast and efficient approaches for predicting VS. In this study, in addition to the widely used Greenberg–Castagna empirical method, we implement three relatively recently developed metaheuristic algorithms to construct linear and nonlinear models for predicting VS : teaching–learning based optimization, imperialist competitive and artificial bee colony algorithms. We demonstrate the applicability and performance of these algorithms to predict Vs using conventional well logs in two field data examples, a sandstone formation from an offshore oil field and a carbonate formation from an onshore oil field. We compared the estimated VS using each of the employed metaheuristic approaches with observed VS and also with those predicted by Greenberg–Castagna relations. The results indicate that, for both sandstone and carbonate case studies, all three implemented metaheuristic algorithms are more efficient and reliable than the empirical correlation to predict VS . The results also demonstrate that in both sandstone and carbonate case studies, the performance of an artificial bee colony algorithm in VS prediction is slightly higher than two other alternative employed approaches.

Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness.

PubMed

Muza, Stephen R

2018-03-01

This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO 2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-02-01

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

TargetSpy: a supervised machine learning approach for microRNA target prediction.

PubMed

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

TargetSpy: a supervised machine learning approach for microRNA target prediction

PubMed Central

2010-01-01

Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org. PMID:20509939

Serious injury prediction algorithm based on large-scale data and under-triage control.

PubMed

Nishimoto, Tetsuya; Mukaigawa, Kosuke; Tominaga, Shigeru; Lubbe, Nils; Kiuchi, Toru; Motomura, Tomokazu; Matsumoto, Hisashi

2017-01-01

The present study was undertaken to construct an algorithm for an advanced automatic collision notification system based on national traffic accident data compiled by Japanese police. While US research into the development of a serious-injury prediction algorithm is based on a logistic regression algorithm using the National Automotive Sampling System/Crashworthiness Data System, the present injury prediction algorithm was based on comprehensive police data covering all accidents that occurred across Japan. The particular focus of this research is to improve the rescue of injured vehicle occupants in traffic accidents, and the present algorithm assumes the use of an onboard event data recorder data from which risk factors such as pseudo delta-V, vehicle impact location, seatbelt wearing or non-wearing, involvement in a single impact or multiple impact crash and the occupant's age can be derived. As a result, a simple and handy algorithm suited for onboard vehicle installation was constructed from a sample of half of the available police data. The other half of the police data was applied to the validation testing of this new algorithm using receiver operating characteristic analysis. An additional validation was conducted using in-depth investigation of accident injuries in collaboration with prospective host emergency care institutes. The validated algorithm, named the TOYOTA-Nihon University algorithm, proved to be as useful as the US URGENCY and other existing algorithms. Furthermore, an under-triage control analysis found that the present algorithm could achieve an under-triage rate of less than 10% by setting a threshold of 8.3%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics.

PubMed

Duconge, Jorge; Ramos, Alga S; Claudio-Campos, Karla; Rivera-Miranda, Giselle; Bermúdez-Bosch, Luis; Renta, Jessicca Y; Cadilla, Carmen L; Cruz, Iadelisse; Feliu, Juan F; Vergara, Cunegundo; Ruaño, Gualberto

2016-01-01

This study is aimed at developing a novel admixture-adjusted pharmacogenomic approach to individually refine warfarin dosing in Caribbean Hispanic patients. A multiple linear regression analysis of effective warfarin doses versus relevant genotypes, admixture, clinical and demographic factors was performed in 255 patients and further validated externally in another cohort of 55 individuals. The admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. For patients at the highest risk of adverse events, 45.5% of the dose predictions using the developed pharmacogenetic model resulted in ideal dose as compared with only 29% when using the clinical non-genetic algorithm (p<0.001). The admixture-driven pharmacogenetic algorithm predicted 58% of warfarin dose variance when externally validated in 55 individuals from an independent validation cohort (MAE = 0.89 mg/day, 24% mean bias). Results supported our rationale to incorporate individual's genotypes and unique admixture metrics into pharmacogenetic refinement models in order to increase predictability when expanding them to admixed populations like Caribbean Hispanics. ClinicalTrials.gov NCT01318057.

Predicting Survey Responses: How and Why Semantics Shape Survey Statistics on Organizational Behaviour

PubMed Central

Arnulf, Jan Ketil; Larsen, Kai Rune; Martinsen, Øyvind Lund; Bong, Chih How

2014-01-01

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60–86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain. PMID:25184672

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour.

PubMed

Arnulf, Jan Ketil; Larsen, Kai Rune; Martinsen, Øyvind Lund; Bong, Chih How

2014-01-01

Some disciplines in the social sciences rely heavily on collecting survey responses to detect empirical relationships among variables. We explored whether these relationships were a priori predictable from the semantic properties of the survey items, using language processing algorithms which are now available as new research methods. Language processing algorithms were used to calculate the semantic similarity among all items in state-of-the-art surveys from Organisational Behaviour research. These surveys covered areas such as transformational leadership, work motivation and work outcomes. This information was used to explain and predict the response patterns from real subjects. Semantic algorithms explained 60-86% of the variance in the response patterns and allowed remarkably precise prediction of survey responses from humans, except in a personality test. Even the relationships between independent and their purported dependent variables were accurately predicted. This raises concern about the empirical nature of data collected through some surveys if results are already given a priori through the way subjects are being asked. Survey response patterns seem heavily determined by semantics. Language algorithms may suggest these prior to administering a survey. This study suggests that semantic algorithms are becoming new tools for the social sciences, opening perspectives on survey responses that prevalent psychometric theory cannot explain.

Information processing for aerospace structural health monitoring

NASA Astrophysics Data System (ADS)

Lichtenwalner, Peter F.; White, Edward V.; Baumann, Erwin W.

1998-06-01

Structural health monitoring (SHM) technology provides a means to significantly reduce life cycle of aerospace vehicles by eliminating unnecessary inspections, minimizing inspection complexity, and providing accurate diagnostics and prognostics to support vehicle life extension. In order to accomplish this, a comprehensive SHM system will need to acquire data from a wide variety of diverse sensors including strain gages, accelerometers, acoustic emission sensors, crack growth gages, corrosion sensors, and piezoelectric transducers. Significant amounts of computer processing will then be required to convert this raw sensor data into meaningful information which indicates both the diagnostics of the current structural integrity as well as the prognostics necessary for planning and managing the future health of the structure in a cost effective manner. This paper provides a description of the key types of information processing technologies required in an effective SHM system. These include artificial intelligence techniques such as neural networks, expert systems, and fuzzy logic for nonlinear modeling, pattern recognition, and complex decision making; signal processing techniques such as Fourier and wavelet transforms for spectral analysis and feature extraction; statistical algorithms for optimal detection, estimation, prediction, and fusion; and a wide variety of other algorithms for data analysis and visualization. The intent of this paper is to provide an overview of the role of information processing for SHM, discuss various technologies which can contribute to accomplishing this role, and present some example applications of information processing for SHM implemented at the Boeing Company.

Arrhythmia Evaluation in Wearable ECG Devices

PubMed Central

Sadrawi, Muammar; Lin, Chien-Hung; Hsieh, Yita; Kuo, Chia-Chun; Chien, Jen Chien; Haraikawa, Koichi; Abbod, Maysam F.; Shieh, Jiann-Shing

2017-01-01

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. PMID:29068369

Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

PubMed Central

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance

ERIC Educational Resources Information Center

Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui

2015-01-01

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

The Design and Implementation of a Read Prediction Buffer

DTIC Science & Technology

1992-12-01

City, State, and ZIP Code) 7b ADDRESS (City, State. and ZIP Code) 8a. NAME OF FUNDING /SPONSORING 8b. OFFICE SYMBOL 9 PROCUREMENT INSTRUMENT... 9 E. THESIS STRUCTURE.. . .... ............... 9 II. READ PREDICTION ALGORITHM AND BUFFER DESIGN 10 A. THE READ PREDICTION ALGORITHM...29 Figure 9 . Basic Multiplexer Cell .... .......... .. 30 Figure 10. Block Diagram Simulation Labels ......... 38 viii I. INTRODUCTION A

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

NASA Astrophysics Data System (ADS)

Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

2018-01-01

The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

A Third Approach to Gene Prediction Suggests Thousands of Additional Human Transcribed Regions

PubMed Central

Glusman, Gustavo; Qin, Shizhen; El-Gewely, M. Raafat; Siegel, Andrew F; Roach, Jared C; Hood, Leroy; Smit, Arian F. A

2006-01-01

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent “genomic deserts.” PMID:16543943

Development and validation of a shared decision-making instrument for health-related quality of life one year after total hip replacement based on quality registries data.

PubMed

Nemes, Szilard; Rolfson, Ola; Garellick, Göran

2018-02-01

Clinicians considering improvements in health-related quality of life (HRQoL) after total hip replacement (THR) must account for multiple pieces of information. Evidence-based decisions are important to best assess the effect of THR on HRQoL. This work aims at constructing a shared decision-making tool that helps clinicians assessing the future benefits of THR by offering predictions of 1-year postoperative HRQoL of THR patients. We used data from the Swedish Hip Arthroplasty Register. Data from 2008 were used as training set and data from 2009 to 2012 as validation set. We adopted two approaches. First, we assumed a continuous distribution for the EQ-5D index and modelled the postoperative EQ-5D index with regression models. Second, we modelled the five dimensions of the EQ-5D and weighted together the predictions using the UK Time Trade-Off value set. As predictors, we used preoperative EQ-5D dimensions and the EQ-5D index, EQ visual analogue scale, visual analogue scale pain, Charnley classification, age, gender, body mass index, American Society of Anesthesiologists, surgical approach and prosthesis type. Additionally, the tested algorithms were combined in a single predictive tool by stacking. Best predictive power was obtained by the multivariate adaptive regression splines (R 2  = 0.158). However, this was not significantly better than the predictive power of linear regressions (R 2  = 0.157). The stacked model had a predictive power of 17%. Successful implementation of a shared decision-making tool that can aid clinicians and patients in understanding expected improvement in HRQoL following THR would require higher predictive power than we achieved. For a shared decision-making tool to succeed, further variables, such as socioeconomics, need to be considered. © 2016 John Wiley & Sons, Ltd.

Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.

PubMed

Nabavi-Pelesaraei, Ashkan; Rafiee, Shahin; Mohtasebi, Seyed Saeid; Hosseinzadeh-Bandbafha, Homa; Chau, Kwok-Wing

2018-08-01

Prediction of agricultural energy output and environmental impacts play important role in energy management and conservation of environment as it can help us to evaluate agricultural energy efficiency, conduct crops production system commissioning, and detect and diagnose faults of crop production system. Agricultural energy output and environmental impacts can be readily predicted by artificial intelligence (AI), owing to the ease of use and adaptability to seek optimal solutions in a rapid manner as well as the use of historical data to predict future agricultural energy use pattern under constraints. This paper conducts energy output and environmental impact prediction of paddy production in Guilan province, Iran based on two AI methods, artificial neural networks (ANNs), and adaptive neuro fuzzy inference system (ANFIS). The amounts of energy input and output are 51,585.61MJkg -1 and 66,112.94MJkg -1 , respectively, in paddy production. Life Cycle Assessment (LCA) is used to evaluate environmental impacts of paddy production. Results show that, in paddy production, in-farm emission is a hotspot in global warming, acidification and eutrophication impact categories. ANN model with 12-6-8-1 structure is selected as the best one for predicting energy output. The correlation coefficient (R) varies from 0.524 to 0.999 in training for energy input and environmental impacts in ANN models. ANFIS model is developed based on a hybrid learning algorithm, with R for predicting output energy being 0.860 and, for environmental impacts, varying from 0.944 to 0.997. Results indicate that the multi-level ANFIS is a useful tool to managers for large-scale planning in forecasting energy output and environmental indices of agricultural production systems owing to its higher speed of computation processes compared to ANN model, despite ANN's higher accuracy. Copyright © 2018 Elsevier B.V. All rights reserved.

A novel acenocoumarol pharmacogenomic dosing algorithm for the Greek population of EU-PACT trial.

PubMed

Ragia, Georgia; Kolovou, Vana; Kolovou, Genovefa; Konstantinides, Stavros; Maltezos, Efstratios; Tavridou, Anna; Tziakas, Dimitrios; Maitland-van der Zee, Anke H; Manolopoulos, Vangelis G

2017-01-01

To generate and validate a pharmacogenomic-guided (PG) dosing algorithm for acenocoumarol in the Greek population. To compare its performance with other PG algorithms developed for the Greek population. A total of 140 Greek patients participants of the EU-PACT trial for acenocoumarol, a randomized clinical trial that prospectively compared the effect of a PG dosing algorithm with a clinical dosing algorithm on the percentage of time within INR therapeutic range, who reached acenocoumarol stable dose were included in the study. CYP2C9 and VKORC1 genotypes, age and weight affected acenocoumarol dose and predicted 53.9% of its variability. EU-PACT PG algorithm overestimated acenocoumarol dose across all different CYP2C9/VKORC1 functional phenotype bins (predicted dose vs stable dose in normal responders 2.31 vs 2.00 mg/day, p = 0.028, in sensitive responders 1.72 vs 1.50 mg/day, p = 0.003, in highly sensitive responders 1.39 vs 1.00 mg/day, p = 0.029). The PG algorithm previously developed for the Greek population overestimated the dose in normal responders (2.51 vs 2.00 mg/day, p < 0.001). Ethnic-specific dosing algorithm is suggested for better prediction of acenocoumarol dosage requirements in patients of Greek origin.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity

PubMed Central

Whittington, James C. R.; Bogacz, Rafal

2017-01-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output. PMID:28333583

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.

PubMed

Whittington, James C R; Bogacz, Rafal

2017-05-01

To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction.

PubMed

Miranian, A; Abdollahzade, M

2013-02-01

Local modeling approaches, owing to their ability to model different operating regimes of nonlinear systems and processes by independent local models, seem appealing for modeling, identification, and prediction applications. In this paper, we propose a local neuro-fuzzy (LNF) approach based on the least-squares support vector machines (LSSVMs). The proposed LNF approach employs LSSVMs, which are powerful in modeling and predicting time series, as local models and uses hierarchical binary tree (HBT) learning algorithm for fast and efficient estimation of its parameters. The HBT algorithm heuristically partitions the input space into smaller subdomains by axis-orthogonal splits. In each partitioning, the validity functions automatically form a unity partition and therefore normalization side effects, e.g., reactivation, are prevented. Integration of LSSVMs into the LNF network as local models, along with the HBT learning algorithm, yield a high-performance approach for modeling and prediction of complex nonlinear time series. The proposed approach is applied to modeling and predictions of different nonlinear and chaotic real-world and hand-designed systems and time series. Analysis of the prediction results and comparisons with recent and old studies demonstrate the promising performance of the proposed LNF approach with the HBT learning algorithm for modeling and prediction of nonlinear and chaotic systems and time series.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

PubMed Central

Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients. PMID:29904574

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

PubMed

Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca

2017-01-01

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Predicting intensity ranks of peptide fragment ions.

PubMed

Frank, Ari M

2009-05-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Predicting Intensity Ranks of Peptide Fragment Ions

PubMed Central

Frank, Ari M.

2009-01-01

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm in to models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal MRM transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html. PMID:19256476

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

NASA Technical Reports Server (NTRS)

Kim, Kyu-Myong; Lau, William K. M.; Li, Guilong; Shen, Samuel S. P.; Lau, William K. M. (Technical Monitor)

2001-01-01

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

2009-07-01

Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

Effects of artificial lighting on the detection of plant stress with spectral reflectance remote sensing in bioregenerative life support systems

NASA Astrophysics Data System (ADS)

Schuerger, Andrew C.; Richards, Jeffrey T.

2006-09-01

Plant-based life support systems that utilize bioregenerative technologies have been proposed for long-term human missions to both the Moon and Mars. Bioregenerative life support systems will utilize higher plants to regenerate oxygen, water, and edible biomass for crews, and are likely to significantly lower the ‘equivalent system mass’ of crewed vehicles. As part of an ongoing effort to begin the development of an automatic remote sensing system to monitor plant health in bioregenerative life support modules, we tested the efficacy of seven artificial illumination sources on the remote detection of plant stresses. A cohort of pepper plants (Capsicum annuum L.) were grown 42 days at 25 °C, 70% relative humidity, and 300 μmol m-2 s-1 of photosynthetically active radiation (PAR; from 400 to 700 nm). Plants were grown under nutritional stresses induced by irrigating subsets of the plants with 100, 50, 25, or 10% of a standard nutrient solution. Reflectance spectra of the healthy and stressed plants were collected under seven artificial lamps including two tungsten halogen lamps, plus high pressure sodium, metal halide, fluorescent, microwave, and red/blue light emitting diode (LED) sources. Results indicated that several common algorithms used to estimate biomass and leaf chlorophyll content were effective in predicting plant stress under all seven illumination sources. However, the two types of tungsten halogen lamps and the microwave illumination source yielded linear models with the highest residuals and thus the highest predictive capabilities of all lamps tested. The illumination sources with the least predictive capabilities were the red/blue LEDs and fluorescent lamps. Although the red/blue LEDs yielded the lowest residuals for linear models derived from the remote sensing data, the LED arrays used in these experiments were optimized for plant productivity and not the collection of remote sensing data. Thus, we propose that if adjusted to optimize the collectio n of remote sensing information from plants, LEDs remain the best candidates for illumination sources for monitoring plant stresses in bioregenerative life support systems.

A survey of provably correct fault-tolerant clock synchronization techniques

NASA Technical Reports Server (NTRS)

Butler, Ricky W.

1988-01-01

Six provably correct fault-tolerant clock synchronization algorithms are examined. These algorithms are all presented in the same notation to permit easier comprehension and comparison. The advantages and disadvantages of the different techniques are examined and issues related to the implementation of these algorithms are discussed. The paper argues for the use of such algorithms in life-critical applications.

Use of sexually transmitted disease risk assessment algorithms for selection of intrauterine device candidates.

PubMed

Morrison, C S; Sekadde-Kigondu, C; Miller, W C; Weiner, D H; Sinei, S K

1999-02-01

Sexually transmitted diseases (STD) are an important contraindication for intrauterine device (IUD) insertion. Nevertheless, laboratory testing for STD is not possible in many settings. The objective of this study is to evaluate the use of risk assessment algorithms to predict STD and subsequent IUD-related complications among IUD candidates. Among 615 IUD users in Kenya, the following algorithms were evaluated: 1) an STD algorithm based on US Agency for International Development (USAID) Technical Working Group guidelines: 2) a Centers for Disease Control and Prevention (CDC) algorithm for management of chlamydia; and 3) a data-derived algorithm modeled from study data. Algorithms were evaluated for prediction of chlamydial and gonococcal infection at 1 month and complications (pelvic inflammatory disease [PID], IUD removals, and IUD expulsions) over 4 months. Women with STD were more likely to develop complications than women without STD (19% vs 6%; risk ratio = 2.9; 95% CI 1.3-6.5). For STD prediction, the USAID algorithm was 75% sensitive and 48% specific, with a positive likelihood ratio (LR+) of 1.4. The CDC algorithm was 44% sensitive and 72% specific, LR+ = 1.6. The data-derived algorithm was 91% sensitive and 56% specific, with LR+ = 2.0 and LR- = 0.2. Category-specific LR for this algorithm identified women with very low (< 1%) and very high (29%) infection probabilities. The data-derived algorithm was also the best predictor of IUD-related complications. These results suggest that use of STD algorithms may improve selection of IUD users. Women at high risk for STD could be counseled to avoid IUD, whereas women at moderate risk should be monitored closely and counseled to use condoms.

Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording

PubMed Central

Eliseyev, Andrey; Aksenova, Tetiana

2016-01-01

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience. PMID:27196417

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

Dall-Anese, Emiliano; Simonetto, Andrea

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are establishedmore » to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.« less

Fast prediction of RNA-RNA interaction using heuristic algorithm.

PubMed

Montaseri, Soheila

2015-01-01

Interaction between two RNA molecules plays a crucial role in many medical and biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. Some algorithms have been formed to predict the structure of the RNA-RNA interaction. High computational time is a common challenge in most of the presented algorithms. In this context, a heuristic method is introduced to accurately predict the interaction between two RNAs based on minimum free energy (MFE). This algorithm uses a few dot matrices for finding the secondary structure of each RNA and binding sites between two RNAs. Furthermore, a parallel version of this method is presented. We describe the algorithm's concurrency and parallelism for a multicore chip. The proposed algorithm has been performed on some datasets including CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS, and IncRNA54-RepZ in Escherichia coli bacteria. The method has high validity and efficiency, and it is run in low computational time in comparison to other approaches.

Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

NASA Astrophysics Data System (ADS)

Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

2018-03-01

In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

Machine learning for prediction of 30-day mortality after ST elevation myocardial infraction: An Acute Coronary Syndrome Israeli Survey data mining study.

PubMed

Shouval, Roni; Hadanny, Amir; Shlomo, Nir; Iakobishvili, Zaza; Unger, Ron; Zahger, Doron; Alcalai, Ronny; Atar, Shaul; Gottlieb, Shmuel; Matetzky, Shlomi; Goldenberg, Ilan; Beigel, Roy

2017-11-01

Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach. To evaluate an array of machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores. This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered. Prediction models for overall mortality 30days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores. Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p<0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age. We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

PubMed Central

Karatsidis, Angelos; Bellusci, Giovanni; Schepers, H. Martin; de Zee, Mark; Andersen, Michael S.; Veltink, Peter H.

2016-01-01

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory. PMID:28042857

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

PubMed Central

2012-01-01

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Inaccuracy of Wolff-Parkinson-white accessory pathway localization algorithms in children and patients with congenital heart defects.

PubMed

Bar-Cohen, Yaniv; Khairy, Paul; Morwood, James; Alexander, Mark E; Cecchin, Frank; Berul, Charles I

2006-07-01

ECG algorithms used to localize accessory pathways (AP) in patients with Wolff-Parkinson-White (WPW) syndrome have been validated in adults, but less is known of their use in children, especially in patients with congenital heart disease (CHD). We hypothesize that these algorithms have low diagnostic accuracy in children and even lower in those with CHD. Pre-excited ECGs in 43 patients with WPW and CHD (median age 5.4 years [0.9-32 years]) were evaluated and compared to 43 consecutive WPW control patients without CHD (median age 14.5 years [1.8-18 years]). Two blinded observers predicted AP location using 2 adult and 1 pediatric WPW algorithms, and a third blinded observer served as a tiebreaker. Predicted locations were compared with ablation-verified AP location to identify (a) exact match for AP location and (b) match for laterality (left-sided vs right-sided AP). In control children, adult algorithms were accurate in only 56% and 60%, while the pediatric algorithm was correct in 77%. In 19 patients with Ebstein's anomaly, diagnostic accuracy was similar to controls with at times an even better ability to predict laterality. In non-Ebstein's CHD, however, the algorithms were markedly worse (29% for the adult algorithms and 42% for the pediatric algorithms). A relatively large degree of interobserver variability was seen (kappa values from 0.30 to 0.58). Adult localization algorithms have poor diagnostic accuracy in young patients with and without CHD. Both adult and pediatric algorithms are particularly misleading in non-Ebstein's CHD patients and should be interpreted with caution.

Consumer visual appraisal and shelf life of leg chops from suckling kids raised with natural milk or milk replacer.

PubMed

Ripoll, Guillermo; Alcalde, María J; Argüello, Anastasio; Córdoba, María G; Panea, Begoña

2018-05-01

The use of milk replacers to feed suckling kids could affect the shelf life and appearance of the meat. Leg chops were evaluated by consumers and the instrumental color was measured. A machine learning algorithm was used to relate them. The aim of this experiment was to study the shelf life of the meat of kids reared with dam's milk or milk replacers and to ascertain which illuminant and instrumental color variables are used by consumers as criteria to evaluate that visual appraisal. Meat from kids reared with milk replacers was more valuable and had a longer shelf life than meat from kids reared with natural milk. Consumers used the color of the whole surface of the leg chop to assess the appearance of meat. Lightness and hue angle were the prime cues used to evaluate the appearance of meat. Illuminant D65 was more useful for relating the visual appraisal with the instrumental color using a machine learning algorithm. The machine learning algorithms showed that the underlying rules used by consumers to evaluate the appearance of suckling kid meat are not at all linear and can be computationally schematized into a simple algorithm. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Predicting coronary artery disease using different artificial neural network models.

PubMed

Colak, M Cengiz; Colak, Cemil; Kocatürk, Hasan; Sağiroğlu, Seref; Barutçu, Irfan

2008-08-01

Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

Historical maintenance relevant information road-map for a self-learning maintenance prediction procedural approach

NASA Astrophysics Data System (ADS)

Morales, Francisco J.; Reyes, Antonio; Cáceres, Noelia; Romero, Luis M.; Benitez, Francisco G.; Morgado, Joao; Duarte, Emanuel; Martins, Teresa

2017-09-01

A large percentage of transport infrastructures are composed of linear assets, such as roads and rail tracks. The large social and economic relevance of these constructions force the stakeholders to ensure a prolonged health/durability. Even though, inevitable malfunctioning, breaking down, and out-of-service periods arise randomly during the life cycle of the infrastructure. Predictive maintenance techniques tend to diminish the appearance of unpredicted failures and the execution of needed corrective interventions, envisaging the adequate interventions to be conducted before failures show up. This communication presents: i) A procedural approach, to be conducted, in order to collect the relevant information regarding the evolving state condition of the assets involved in all maintenance interventions; this reported and stored information constitutes a rich historical data base to train Machine Learning algorithms in order to generate reliable predictions of the interventions to be carried out in further time scenarios. ii) A schematic flow chart of the automatic learning procedure. iii) Self-learning rules from automatic learning from false positive/negatives. The description, testing, automatic learning approach and the outcomes of a pilot case are presented; finally some conclusions are outlined regarding the methodology proposed for improving the self-learning predictive capability.

Experimental validation of clock synchronization algorithms

NASA Technical Reports Server (NTRS)

Palumbo, Daniel L.; Graham, R. Lynn

1992-01-01

The objective of this work is to validate mathematically derived clock synchronization theories and their associated algorithms through experiment. Two theories are considered, the Interactive Convergence Clock Synchronization Algorithm and the Midpoint Algorithm. Special clock circuitry was designed and built so that several operating conditions and failure modes (including malicious failures) could be tested. Both theories are shown to predict conservative upper bounds (i.e., measured values of clock skew were always less than the theory prediction). Insight gained during experimentation led to alternative derivations of the theories. These new theories accurately predict the behavior of the clock system. It is found that a 100 percent penalty is paid to tolerate worst-case failures. It is also shown that under optimal conditions (with minimum error and no failures) the clock skew can be as much as three clock ticks. Clock skew grows to six clock ticks when failures are present. Finally, it is concluded that one cannot rely solely on test procedures or theoretical analysis to predict worst-case conditions.

Verification of Pharmacogenetics-Based Warfarin Dosing Algorithms in Han-Chinese Patients Undertaking Mechanic Heart Valve Replacement

PubMed Central

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

Objective To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. Methods We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. Results A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88–4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. Conclusions All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement. PMID:24728385

Verification of pharmacogenetics-based warfarin dosing algorithms in Han-Chinese patients undertaking mechanic heart valve replacement.

PubMed

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88-4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement.

An Impact-Location Estimation Algorithm for Subsonic Uninhabited Aircraft

NASA Technical Reports Server (NTRS)

Bauer, Jeffrey E.; Teets, Edward

1997-01-01

An impact-location estimation algorithm is being used at the NASA Dryden Flight Research Center to support range safety for uninhabited aerial vehicle flight tests. The algorithm computes an impact location based on the descent rate, mass, and altitude of the vehicle and current wind information. The predicted impact location is continuously displayed on the range safety officer's moving map display so that the flightpath of the vehicle can be routed to avoid ground assets if the flight must be terminated. The algorithm easily adapts to different vehicle termination techniques and has been shown to be accurate to the extent required to support range safety for subsonic uninhabited aerial vehicles. This paper describes how the algorithm functions, how the algorithm is used at NASA Dryden, and how various termination techniques are handled by the algorithm. Other approaches to predicting the impact location and the reasons why they were not selected for real-time implementation are also discussed.

Optimal input selection for neural machine interfaces predicting multiple non-explicit outputs.

PubMed

Krepkovich, Eileen T; Perreault, Eric J

2008-01-01

This study implemented a novel algorithm that optimally selects inputs for neural machine interface (NMI) devices intended to control multiple outputs and evaluated its performance on systems lacking explicit output. NMIs often incorporate signals from multiple physiological sources and provide predictions for multidimensional control, leading to multiple-input multiple-output systems. Further, NMIs often are used with subjects who have motor disabilities and thus lack explicit motor outputs. Our algorithm was tested on simulated multiple-input multiple-output systems and on electromyogram and kinematic data collected from healthy subjects performing arm reaches. Effects of output noise in simulated systems indicated that the algorithm could be useful for systems with poor estimates of the output states, as is true for systems lacking explicit motor output. To test efficacy on physiological data, selection was performed using inputs from one subject and outputs from a different subject. Selection was effective for these cases, again indicating that this algorithm will be useful for predictions where there is no motor output, as often is the case for disabled subjects. Further, prediction results generalized for different movement types not used for estimation. These results demonstrate the efficacy of this algorithm for the development of neural machine interfaces.

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space

PubMed Central

Karnik, Rahul; Beer, Michael A.

2015-01-01

The generation of genomic binding or accessibility data from massively parallel sequencing technologies such as ChIP-seq and DNase-seq continues to accelerate. Yet state-of-the-art computational approaches for the identification of DNA binding motifs often yield motifs of weak predictive power. Here we present a novel computational algorithm called MotifSpec, designed to find predictive motifs, in contrast to over-represented sequence elements. The key distinguishing feature of this algorithm is that it uses a dynamic search space and a learned threshold to find discriminative motifs in combination with the modeling of motifs using a full PWM (position weight matrix) rather than k-mer words or regular expressions. We demonstrate that our approach finds motifs corresponding to known binding specificities in several mammalian ChIP-seq datasets, and that our PWMs classify the ChIP-seq signals with accuracy comparable to, or marginally better than motifs from the best existing algorithms. In other datasets, our algorithm identifies novel motifs where other methods fail. Finally, we apply this algorithm to detect motifs from expression datasets in C. elegans using a dynamic expression similarity metric rather than fixed expression clusters, and find novel predictive motifs. PMID:26465884

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

PubMed

Karnik, Rahul; Beer, Michael A

2015-01-01

The generation of genomic binding or accessibility data from massively parallel sequencing technologies such as ChIP-seq and DNase-seq continues to accelerate. Yet state-of-the-art computational approaches for the identification of DNA binding motifs often yield motifs of weak predictive power. Here we present a novel computational algorithm called MotifSpec, designed to find predictive motifs, in contrast to over-represented sequence elements. The key distinguishing feature of this algorithm is that it uses a dynamic search space and a learned threshold to find discriminative motifs in combination with the modeling of motifs using a full PWM (position weight matrix) rather than k-mer words or regular expressions. We demonstrate that our approach finds motifs corresponding to known binding specificities in several mammalian ChIP-seq datasets, and that our PWMs classify the ChIP-seq signals with accuracy comparable to, or marginally better than motifs from the best existing algorithms. In other datasets, our algorithm identifies novel motifs where other methods fail. Finally, we apply this algorithm to detect motifs from expression datasets in C. elegans using a dynamic expression similarity metric rather than fixed expression clusters, and find novel predictive motifs.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

PubMed

Shen, Xianjun; Yi, Li; Jiang, Xingpeng; He, Tingting; Yang, Jincai; Xie, Wei; Hu, Po; Hu, Xiaohua

2017-01-01

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

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

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.« less

Neurologic Complications in Infective Endocarditis

PubMed Central

Morris, Nicholas A.; Matiello, Marcelo; Samuels, Martin A.

2014-01-01

Neurologic complications of infective endocarditis (IE) are common and frequently life threatening. Neurologic events are not always obvious. The prediction and management of neurologic complications of IE are not easily approached algorithmically, and the impact they have on timing and ability to surgically repair or replace the affected valve often requires a painstaking evaluation and joint effort across multiple medical disciplines in order to achieve the best possible outcome. Although specific recommendations are always tailored to the individual patient, there are some guiding principles that can be used to help direct the decision-making process. Herein, we review the pathophysiology, epidemiology, manifestations, and diagnosis of neurological complications of IE and further consider the impact they have on clinical decision making. PMID:25360207

Seizure Forecasting and the Preictal State in Canine Epilepsy.

PubMed

Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M; Worrell, Gregory A; Brinkmann, Benjamin H

2017-02-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

SEIZURE FORECASTING AND THE PREICTAL STATE IN CANINE EPILEPSY

PubMed Central

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent M.; Worrell, Gregory A.; Brinkmann, Benjamin H.

2017-01-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state. PMID:27464854

Real-time prediction and gating of respiratory motion in 3D space using extended Kalman filters and Gaussian process regression network

NASA Astrophysics Data System (ADS)

Bukhari, W.; Hong, S.-M.

2016-03-01

The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient’s breathing cycle. The algorithm, named EKF-GPRN+ , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN+ prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN+ implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN+ . The experimental results show that the EKF-GPRN+ algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN+ algorithm can further reduce the prediction error by employing the gating function, albeit at the cost of reduced duty cycle. The error reduction allows the clinical target volume to planning target volume (CTV-PTV) margin to be reduced, leading to decreased normal-tissue toxicity and possible dose escalation. The CTV-PTV margin is also evaluated to quantify clinical benefits of EKF-GPRN+ prediction.

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

PubMed Central

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers. PACS numbers: 87.55.km, 87.56.Fc PMID:26894358

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

PubMed

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in combination with a genetic search algorithm, proved to yield best performance accuracy for selecting optimum markers.

Application of Recursive Partitioning to Derive and Validate a Claims-Based Algorithm for Identifying Keratinocyte Carcinoma (Nonmelanoma Skin Cancer).

PubMed

Chan, An-Wen; Fung, Kinwah; Tran, Jennifer M; Kitchen, Jessica; Austin, Peter C; Weinstock, Martin A; Rochon, Paula A

2016-10-01

Keratinocyte carcinoma (nonmelanoma skin cancer) accounts for substantial burden in terms of high incidence and health care costs but is excluded by most cancer registries in North America. Administrative health insurance claims databases offer an opportunity to identify these cancers using diagnosis and procedural codes submitted for reimbursement purposes. To apply recursive partitioning to derive and validate a claims-based algorithm for identifying keratinocyte carcinoma with high sensitivity and specificity. Retrospective study using population-based administrative databases linked to 602 371 pathology episodes from a community laboratory for adults residing in Ontario, Canada, from January 1, 1992, to December 31, 2009. The final analysis was completed in January 2016. We used recursive partitioning (classification trees) to derive an algorithm based on health insurance claims. The performance of the derived algorithm was compared with 5 prespecified algorithms and validated using an independent academic hospital clinic data set of 2082 patients seen in May and June 2011. Sensitivity, specificity, positive predictive value, and negative predictive value using the histopathological diagnosis as the criterion standard. We aimed to achieve maximal specificity, while maintaining greater than 80% sensitivity. Among 602 371 pathology episodes, 131 562 (21.8%) had a diagnosis of keratinocyte carcinoma. Our final derived algorithm outperformed the 5 simple prespecified algorithms and performed well in both community and hospital data sets in terms of sensitivity (82.6% and 84.9%, respectively), specificity (93.0% and 99.0%, respectively), positive predictive value (76.7% and 69.2%, respectively), and negative predictive value (95.0% and 99.6%, respectively). Algorithm performance did not vary substantially during the 18-year period. This algorithm offers a reliable mechanism for ascertaining keratinocyte carcinoma for epidemiological research in the absence of cancer registry data. Our findings also demonstrate the value of recursive partitioning in deriving valid claims-based algorithms.

Comparison of the Performance of the Warfarin Pharmacogenetics Algorithms in Patients with Surgery of Heart Valve Replacement and Heart Valvuloplasty.

PubMed

Xu, Hang; Su, Shi; Tang, Wuji; Wei, Meng; Wang, Tao; Wang, Dongjin; Ge, Weihong

2015-09-01

A large number of warfarin pharmacogenetics algorithms have been published. Our research was aimed to evaluate the performance of the selected pharmacogenetic algorithms in patients with surgery of heart valve replacement and heart valvuloplasty during the phase of initial and stable anticoagulation treatment. 10 pharmacogenetic algorithms were selected by searching PubMed. We compared the performance of the selected algorithms in a cohort of 193 patients during the phase of initial and stable anticoagulation therapy. Predicted dose was compared to therapeutic dose by using a predicted dose percentage that falls within 20% threshold of the actual dose (percentage within 20%) and mean absolute error (MAE). The average warfarin dose for patients was 3.05±1.23mg/day for initial treatment and 3.45±1.18mg/day for stable treatment. The percentages of the predicted dose within 20% of the therapeutic dose were 44.0±8.8% and 44.6±9.7% for the initial and stable phases, respectively. The MAEs of the selected algorithms were 0.85±0.18mg/day and 0.93±0.19mg/day, respectively. All algorithms had better performance in the ideal group than in the low dose and high dose groups. The only exception is the Wadelius et al. algorithm, which had better performance in the high dose group. The algorithms had similar performance except for the Wadelius et al. and Miao et al. algorithms, which had poor accuracy in our study cohort. The Gage et al. algorithm had better performance in both phases of initial and stable treatment. Algorithms had relatively higher accuracy in the >50years group of patients on the stable phase. Copyright © 2015 Elsevier Ltd. All rights reserved.

Historical feature pattern extraction based network attack situation sensing algorithm.

PubMed

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

PubMed Central

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously. PMID:24892054

Correlation approach to identify coding regions in DNA sequences

NASA Technical Reports Server (NTRS)

Ossadnik, S. M.; Buldyrev, S. V.; Goldberger, A. L.; Havlin, S.; Mantegna, R. N.; Peng, C. K.; Simons, M.; Stanley, H. E.

1994-01-01

Recently, it was observed that noncoding regions of DNA sequences possess long-range power-law correlations, whereas coding regions typically display only short-range correlations. We develop an algorithm based on this finding that enables investigators to perform a statistical analysis on long DNA sequences to locate possible coding regions. The algorithm is particularly successful in predicting the location of lengthy coding regions. For example, for the complete genome of yeast chromosome III (315,344 nucleotides), at least 82% of the predictions correspond to putative coding regions; the algorithm correctly identified all coding regions larger than 3000 nucleotides, 92% of coding regions between 2000 and 3000 nucleotides long, and 79% of coding regions between 1000 and 2000 nucleotides. The predictive ability of this new algorithm supports the claim that there is a fundamental difference in the correlation property between coding and noncoding sequences. This algorithm, which is not species-dependent, can be implemented with other techniques for rapidly and accurately locating relatively long coding regions in genomic sequences.

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

NASA Technical Reports Server (NTRS)

Cull, R. C.; Eltimsahy, A. H.

1983-01-01

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

USGS Publications Warehouse

Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

2014-01-01

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

PubMed

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

Can Mapping Algorithms Based on Raw Scores Overestimate QALYs Gained by Treatment? A Comparison of Mappings Between the Roland-Morris Disability Questionnaire and the EQ-5D-3L Based on Raw and Differenced Score Data.

PubMed

Madan, Jason; Khan, Kamran A; Petrou, Stavros; Lamb, Sarah E

2017-05-01

Mapping algorithms are increasingly being used to predict health-utility values based on responses or scores from non-preference-based measures, thereby informing economic evaluations. We explored whether predictions in the EuroQol 5-dimension 3-level instrument (EQ-5D-3L) health-utility gains from mapping algorithms might differ if estimated using differenced versus raw scores, using the Roland-Morris Disability Questionnaire (RMQ), a widely used health status measure for low back pain, as an example. We estimated algorithms mapping within-person changes in RMQ scores to changes in EQ-5D-3L health utilities using data from two clinical trials with repeated observations. We also used logistic regression models to estimate response mapping algorithms from these data to predict within-person changes in responses to each EQ-5D-3L dimension from changes in RMQ scores. Predicted health-utility gains from these mappings were compared with predictions based on raw RMQ data. Using differenced scores reduced the predicted health-utility gain from a unit decrease in RMQ score from 0.037 (standard error [SE] 0.001) to 0.020 (SE 0.002). Analysis of response mapping data suggests that the use of differenced data reduces the predicted impact of reducing RMQ scores across EQ-5D-3L dimensions and that patients can experience health-utility gains on the EQ-5D-3L 'usual activity' dimension independent from improvements captured by the RMQ. Mappings based on raw RMQ data overestimate the EQ-5D-3L health utility gains from interventions that reduce RMQ scores. Where possible, mapping algorithms should reflect within-person changes in health outcome and be estimated from datasets containing repeated observations if they are to be used to estimate incremental health-utility gains.

Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction

PubMed Central

Ahmed, Shiek S. S. J.; Ramakrishnan, V.

2012-01-01

Background Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. Results The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. Conclusion The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability. PMID:22815781

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

PubMed

Ahmed, Shiek S S J; Ramakrishnan, V

2012-01-01

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

Population-based drug-related anaphylaxis in children and adolescents captured by South Carolina Emergency Room Hospital Discharge Database (SCERHDD) (2000-2002).

PubMed

West, Suzanne L; D'Aloisio, Aimee A; Ringel-Kulka, Tamar; Waller, Anna E; Clayton Bordley, W

2007-12-01

Anaphylaxis is a life-threatening condition; drug-related anaphylaxis represents approximately 10% of all cases. We assessed the utility of a statewide emergency department (ED) database for identifying drug-related anaphylaxis in children by developing and validating an algorithm composed of ICD-9-CM codes. There were 1 314,760 visits to South Carolina (SC) emergency departments (EDs) for patients <19 years in 2000-2002. We used ICD-9-CM disease or external cause of injury codes (E-codes) that suggested drug-related anaphylaxis or a severe drug-related allergic reaction. We found 50 cases classifiable as probable or possible drug-related anaphylaxis and 13 as drug-related allergic reactions. We used clinical evaluation by two pediatricians as the 'alloyed gold standard'1 for estimating sensitivity, specificity, and positive predictive value (PPV) of our algorithm. ED-treated drug-related anaphylaxis in the SC pediatric population was 1.56/100,000 person-years based on the algorithm and 0.50/100,000 person-years based on clinical evaluation. Assuming the disease codes we used identified all potential anaphylaxis cases in the database, the sensitivity was 1.00 (95%CI: 0.79, 1.00), specificity was 0.28 (95%CI: 0.16, 0.43), and the PPV was 0.32 (0.20, 0.47) for the algorithm. Sensitivity analyses improved the measurement properties of the algorithm. E-codes were invaluable for developing an anaphylaxis algorithm although the frequently used code of E947.9 was often incorrectly applied. We believe that our algorithm may have over-ascertained drug-related anaphylaxis patients seen in an ED, but the clinical evaluation may have under-represented this diagnosis due to limited information on the offending agent in the abstracted ED records. Post-marketing drug surveillance using ED records may be viable if clinicians were to document drug-related anaphylaxis in the charts so that billing codes could be assigned properly. Copyright 2007 John Wiley & Sons, Ltd.

Paving the way for the use of the SDQ in economic evaluations of school-based population health interventions: an empirical analysis of the external validity of SDQ mapping algorithms to the CHU9D in an educational setting.

PubMed

Boyer, Nicole R S; Miller, Sarah; Connolly, Paul; McIntosh, Emma

2016-04-01

The Strengths and Difficulties Questionnaire (SDQ) is a behavioural screening tool for children. The SDQ is increasingly used as the primary outcome measure in population health interventions involving children, but it is not preference based; therefore, its role in allocative economic evaluation is limited. The Child Health Utility 9D (CHU9D) is a generic preference-based health-related quality of-life measure. This study investigates the applicability of the SDQ outcome measure for use in economic evaluations and examines its relationship with the CHU9D by testing previously published mapping algorithms. The aim of the paper is to explore the feasibility of using the SDQ within economic evaluations of school-based population health interventions. Data were available from children participating in a cluster randomised controlled trial of the school-based roots of empathy programme in Northern Ireland. Utility was calculated using the original and alternative CHU9D tariffs along with two SDQ mapping algorithms. t tests were performed for pairwise differences in utility values from the preference-based tariffs and mapping algorithms. Mean (standard deviation) SDQ total difficulties and prosocial scores were 12 (3.2) and 8.3 (2.1). Utility values obtained from the original tariff, alternative tariff, and mapping algorithms using five and three SDQ subscales were 0.84 (0.11), 0.80 (0.13), 0.84 (0.05), and 0.83 (0.04), respectively. Each method for calculating utility produced statistically significantly different values except the original tariff and five SDQ subscale algorithm. Initial evidence suggests the SDQ and CHU9D are related in some of their measurement properties. The mapping algorithm using five SDQ subscales was found to be optimal in predicting mean child health utility. Future research valuing changes in the SDQ scores would contribute to this research.

Design and analysis of a model predictive controller for active queue management.

PubMed

Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan

2012-01-01

Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm.

PubMed

De Beer, Maarten; Lynen, Fréderic; Chen, Kai; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-03-01

Stationary-phase optimized selectivity liquid chromatography (SOS-LC) is a tool in reversed-phase LC (RP-LC) to optimize the selectivity for a given separation by combining stationary phases in a multisegment column. The presently (commercially) available SOS-LC optimization procedure and algorithm are only applicable to isocratic analyses. Step gradient SOS-LC has been developed, but this is still not very elegant for the analysis of complex mixtures composed of components covering a broad hydrophobicity range. A linear gradient prediction algorithm has been developed allowing one to apply SOS-LC as a generic RP-LC optimization method. The algorithm allows operation in isocratic, stepwise, and linear gradient run modes. The features of SOS-LC in the linear gradient mode are demonstrated by means of a mixture of 13 steroids, whereby baseline separation is predicted and experimentally demonstrated.

Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery

NASA Technical Reports Server (NTRS)

Xie, Hua; Klimesh, Matthew A.

2009-01-01

This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

NASA Astrophysics Data System (ADS)

Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I.

2016-01-01

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI  =  58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI  =  69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC  =  0.58, q-value  =  0.33), while the differentiation was significant with other textures (AUC  =  0.71‒0.73, p  <  0.009). Among the deformable algorithms, fast-demons (AUC  =  0.68‒0.70, q-value  <  0.03) and fast-free-form (AUC  =  0.69‒0.74, q-value  <  0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC  =  0.72‒0.78, q-value  <  0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

Refined Genetic Algorithms for Polypeptide Structure Prediction.

DTIC Science & Technology

1996-12-01

16 I I I. Algorithm Analysis, Design , and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 18 3.1 Analysis...21 3.2 Algorithm Design and Implemen tation : : : : : : : : : : : : : : : : : : : : : : : : : 22 3.2.1...26 IV. Exp erimen t Design

Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

PubMed

Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

2012-06-07

We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

Numerical analysis of moving contact line with contact angle hysteresis using feedback deceleration technique

NASA Astrophysics Data System (ADS)

Park, Jun Kwon; Kang, Kwan Hyoung

2012-04-01

Contact angle (CA) hysteresis is important in many natural and engineering wetting processes, but predicting it numerically is difficult. We developed an algorithm that considers CA hysteresis when analyzing the motion of the contact line (CL). This algorithm employs feedback control of CA which decelerates CL speed to make the CL stationary in the hysteretic range of CA, and one control coefficient should be heuristically determined depending on characteristic time of the simulated system. The algorithm requires embedding only a simple additional routine with little modification of a code which considers the dynamic CA. The method is non-iterative and explicit, and also has less computational load than other algorithms. For a drop hanging on a wire, the proposed algorithm accurately predicts the theoretical equilibrium CA. For the drop impacting on a dry surface, the results of the proposed algorithm agree well with experimental results including the intermittent occurrence of the pinning of CL. The proposed algorithm is as accurate as other algorithms, but faster.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy

NASA Astrophysics Data System (ADS)

Obara, Shin'ya

A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data.

Efficient Prediction of Low-Visibility Events at Airports Using Machine-Learning Regression

NASA Astrophysics Data System (ADS)

Cornejo-Bueno, L.; Casanova-Mateo, C.; Sanz-Justo, J.; Cerro-Prada, E.; Salcedo-Sanz, S.

2017-11-01

We address the prediction of low-visibility events at airports using machine-learning regression. The proposed model successfully forecasts low-visibility events in terms of the runway visual range at the airport, with the use of support-vector regression, neural networks (multi-layer perceptrons and extreme-learning machines) and Gaussian-process algorithms. We assess the performance of these algorithms based on real data collected at the Valladolid airport, Spain. We also propose a study of the atmospheric variables measured at a nearby tower related to low-visibility atmospheric conditions, since they are considered as the inputs of the different regressors. A pre-processing procedure of these input variables with wavelet transforms is also described. The results show that the proposed machine-learning algorithms are able to predict low-visibility events well. The Gaussian process is the best algorithm among those analyzed, obtaining over 98% of the correct classification rate in low-visibility events when the runway visual range is {>}1000 m, and about 80% under this threshold. The performance of all the machine-learning algorithms tested is clearly affected in extreme low-visibility conditions ({<}500 m). However, we show improved results of all the methods when data from a neighbouring meteorological tower are included, and also with a pre-processing scheme using a wavelet transform. Also presented are results of the algorithm performance in daytime and nighttime conditions, and for different prediction time horizons.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

NASA Astrophysics Data System (ADS)

Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

2018-04-01

Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics

PubMed Central

Claudio-Campos, Karla; Rivera-Miranda, Giselle; Bermúdez-Bosch, Luis; Renta, Jessicca Y.; Cadilla, Carmen L.; Cruz, Iadelisse; Feliu, Juan F.; Vergara, Cunegundo; Ruaño, Gualberto

2016-01-01

Aim This study is aimed at developing a novel admixture-adjusted pharmacogenomic approach to individually refine warfarin dosing in Caribbean Hispanic patients. Patients & Methods A multiple linear regression analysis of effective warfarin doses versus relevant genotypes, admixture, clinical and demographic factors was performed in 255 patients and further validated externally in another cohort of 55 individuals. Results The admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. For patients at the highest risk of adverse events, 45.5% of the dose predictions using the developed pharmacogenetic model resulted in ideal dose as compared with only 29% when using the clinical non-genetic algorithm (p<0.001). The admixture-driven pharmacogenetic algorithm predicted 58% of warfarin dose variance when externally validated in 55 individuals from an independent validation cohort (MAE = 0.89 mg/day, 24% mean bias). Conclusions Results supported our rationale to incorporate individual’s genotypes and unique admixture metrics into pharmacogenetic refinement models in order to increase predictability when expanding them to admixed populations like Caribbean Hispanics. Trial Registration ClinicalTrials.gov NCT01318057 PMID:26745506

Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy.

PubMed

Holland, Katherine D; Bouley, Thomas M; Horn, Paul S

2017-07-01

Variants in neuronal voltage-gated sodium channel α-subunits genes SCN1A, SCN2A, and SCN8A are common in early onset epileptic encephalopathies and other autosomal dominant childhood epilepsy syndromes. However, in clinical practice, missense variants are often classified as variants of uncertain significance when missense variants are identified but heritability cannot be determined. Genetic testing reports often include results of computational tests to estimate pathogenicity and the frequency of that variant in population-based databases. The objective of this work was to enhance clinicians' understanding of results by (1) determining how effectively computational algorithms predict epileptogenicity of sodium channel (SCN) missense variants; (2) optimizing their predictive capabilities; and (3) determining if epilepsy-associated SCN variants are present in population-based databases. This will help clinicians better understand the results of indeterminate SCN test results in people with epilepsy. Pathogenic, likely pathogenic, and benign variants in SCNs were identified using databases of sodium channel variants. Benign variants were also identified from population-based databases. Eight algorithms commonly used to predict pathogenicity were compared. In addition, logistic regression was used to determine if a combination of algorithms could better predict pathogenicity. Based on American College of Medical Genetic Criteria, 440 variants were classified as pathogenic or likely pathogenic and 84 were classified as benign or likely benign. Twenty-eight variants previously associated with epilepsy were present in population-based gene databases. The output provided by most computational algorithms had a high sensitivity but low specificity with an accuracy of 0.52-0.77. Accuracy could be improved by adjusting the threshold for pathogenicity. Using this adjustment, the Mendelian Clinically Applicable Pathogenicity (M-CAP) algorithm had an accuracy of 0.90 and a combination of algorithms increased the accuracy to 0.92. Potentially pathogenic variants are present in population-based sources. Most computational algorithms overestimate pathogenicity; however, a weighted combination of several algorithms increased classification accuracy to >0.90. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Comparison between the two-step and the three-step algorithms for the detection of toxigenic Clostridium difficile.

PubMed

Qutub, M O; AlBaz, N; Hawken, P; Anoos, A

2011-01-01

To evaluate usefulness of applying either the two-step algorithm (Ag-EIAs and CCNA) or the three-step algorithm (all three assays) for better confirmation of toxigenic Clostridium difficile. The antigen enzyme immunoassays (Ag-EIAs) can accurately identify the glutamate dehydrogenase antigen of toxigenic and nontoxigenic Clostridium difficile. Therefore, it is used in combination with a toxin-detecting assay [cell line culture neutralization assay (CCNA), or the enzyme immunoassays for toxins A and B (TOX-A/BII EIA)] to provide specific evidence of Clostridium difficile-associated diarrhoea. A total of 151 nonformed stool specimens were tested by Ag-EIAs, TOX-A/BII EIA, and CCNA. All tests were performed according to the manufacturer's instructions and the results of Ag-EIAs and TOX-A/BII EIA were read using a spectrophotometer at a wavelength of 450 nm. A total of 61 (40.7%), 38 (25.3%), and 52 (34.7%) specimens tested positive with Ag-EIA, TOX-A/BII EIA, and CCNA, respectively. Overall, the sensitivity, specificity, negative predictive value, and positive predictive value for Ag-EIA were 94%, 87%, 96.6%, and 80.3%, respectively. Whereas for TOX-A/BII EIA, the sensitivity, specificity, negative predictive value, and positive predictive value were 73.1%, 100%, 87.5%, and 100%, respectively. With the two-step algorithm, all 61 Ag-EIAs-positive cases required 2 days for confirmation. With the three-step algorithm, 37 (60.7%) cases were reported immediately, and the remaining 24 (39.3%) required further testing by CCNA. By applying the two-step algorithm, the workload and cost could be reduced by 28.2% compared with the three-step algorithm. The two-step algorithm is the most practical for accurately detecting toxigenic Clostridium difficile, but it is time-consuming.

Spectral Retrieval of Latent Heating Profiles from TRMM PR data. Part 3; Moistening Estimates over Tropical Ocean Regions

NASA Technical Reports Server (NTRS)

Shige, S.; Takayabu, Y.; Tao, W.-K.

2007-01-01

The global hydrological cycle is central to the Earth's climate system, with rainfall and the physics of precipitation formation acting as the key links in the cycle. Two-thirds of global rainfall occurs in the tropics with the associated latent heating (LH) accounting for threefourths of the total heat energy available to the Earth's atmosphere. In the last decade, it has been established that standard products of LH from satellite measurements, particularly TRMM measurements, would be a valuable resource for scientific research and applications. Such products would enable new insights and investigations concerning the complexities of convection system life cycles, the diabatic heating controls and feedbacks related to rne-sosynoptic circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations In environmental prediction models. However, the LH and water vapor profile or budget (called the apparent moisture sink, or Q2) is closely related. This paper presented the development of an algorithm for retrieving Q2 using 'TRMM precipitation radar. Since there is no direct measurement of LH and Q2, the validation of algorithm usually applies a method called consistency check. Consistency checking involving Cloud Resolving Model (CRM)-generated LH and 42 profiles and algorithm-reconstructed is a useful step in evaluating the performance of a given algorithm. In this process, the CRM simulation of a time-dependent precipitation process (multiple-day time series) is used to obtain the required input parameters for a given algorithm. The algorithm is then used to "econsti-LKth"e heating and moisture profiles that the CRM simulation originally produced, and finally both sets of conformal estimates (model and algorithm) are compared each other. The results indicate that discrepancies between the reconstructed and CM-simulated profiles for Q2, especially at low levels, are larger than those for latent heat. Larger discrepancies in Q2 at low levels are due to moistening for non-precipitating region that algorithm cannot reconstruct. Nevertheless, the algorithm-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones.

Rolling Bearing Life Prediction-Past, Present, and Future

NASA Technical Reports Server (NTRS)

Zaretsky, E V; Poplawski, J. V.; Miller, C. R.

2000-01-01

Comparisons were made between the life prediction formulas of Lundberg and Palmgren, Ioannides and Harris, and Zaretsky and full-scale ball and roller bearing life data. The effect of Weibull slope on bearing life prediction was determined. Life factors are proposed to adjust the respective life formulas to the normalized statistical life distribution of each bearing type. The Lundberg-Palmgren method resulted in the most conservative life predictions compared to Ioannides and Harris, and Zaretsky methods which produced statistically similar results. Roller profile can have significant effects on bearing life prediction results. Roller edge loading can reduce life by as much as 98 percent. The resultant predicted life not only depends on the life equation used but on the Weibull slope assumed, the least variation occurring with the Zaretsky equation. The load-life exponent p of 10/3 used in the American National Standards Institute (ANSI)/American Bearing Manufacturers Association (ABMA)/International Organization for Standardization (ISO) standards is inconsistent with the majority roller bearings designed and used today.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Adaptive intercolor error prediction coder for lossless color (rgb) picutre compression

NASA Astrophysics Data System (ADS)

Mann, Y.; Peretz, Y.; Mitchell, Harvey B.

2001-09-01

Most of the current lossless compression algorithms, including the new international baseline JPEG-LS algorithm, do not exploit the interspectral correlations that exist between the color planes in an input color picture. To improve the compression performance (i.e., lower the bit rate) it is necessary to exploit these correlations. A major concern is to find efficient methods for exploiting the correlations that, at the same time, are compatible with and can be incorporated into the JPEG-LS algorithm. One such algorithm is the method of intercolor error prediction (IEP), which when used with the JPEG-LS algorithm, results on average in a reduction of 8% in the overall bit rate. We show how the IEP algorithm can be simply modified and that it nearly doubles the size of the reduction in bit rate to 15%.

A Feasibility Study of Life-Extending Controls for Aircraft Turbine Engines Using a Generic Air Force Model (Preprint)

DTIC Science & Technology

2006-12-01

intelligent control algorithm embedded in the FADEC . This paper evaluates the LEC, based on critical components research, to demonstrate how an...control action, engine component life usage, and designing an intelligent control algorithm embedded in the FADEC . This paper evaluates the LEC, based on...simulation code for each simulator. One is typically configured to operate as a Full- Authority Digital Electronic Controller ( FADEC

Locomotion with loads: practical techniques for predicting performance outcomes

DTIC Science & Technology

including load), speed, and grade algorithms proposed will allow walking metabolic rates to be predicted to within 6.0 and 12.0 in laboratory and field...speeds to be predicted to within6.0 in both laboratory and field settings. Respective load-carriage algorithms for walking energy expenditure and...running speed will be developed and tested( Technical Objectives 1.0 and 2.0) in the laboratory and the field.

Design and experiment of vehicular charger AC/DC system based on predictive control algorithm

NASA Astrophysics Data System (ADS)

He, Guangbi; Quan, Shuhai; Lu, Yuzhang

2018-06-01

For the car charging stage rectifier uncontrollable system, this paper proposes a predictive control algorithm of DC/DC converter based on the prediction model, established by the state space average method and its prediction model, obtained by the optimal mathematical description of mathematical calculation, to analysis prediction algorithm by Simulink simulation. The design of the structure of the car charging, at the request of the rated output power and output voltage adjustable control circuit, the first stage is the three-phase uncontrolled rectifier DC voltage Ud through the filter capacitor, after by using double-phase interleaved buck-boost circuit with wide range output voltage required value, analyzing its working principle and the the parameters for the design and selection of components. The analysis of current ripple shows that the double staggered parallel connection has the advantages of reducing the output current ripple and reducing the loss. The simulation experiment of the whole charging circuit is carried out by software, and the result is in line with the design requirements of the system. Finally combining the soft with hardware circuit to achieve charging of the system according to the requirements, experimental platform proved the feasibility and effectiveness of the proposed predictive control algorithm based on the car charging of the system, which is consistent with the simulation results.

Automated Assessment of Existing Patient's Revised Cardiac Risk Index Using Algorithmic Software.

PubMed

Hofer, Ira S; Cheng, Drew; Grogan, Tristan; Fujimoto, Yohei; Yamada, Takashige; Beck, Lauren; Cannesson, Maxime; Mahajan, Aman

2018-05-25

Previous work in the field of medical informatics has shown that rules-based algorithms can be created to identify patients with various medical conditions; however, these techniques have not been compared to actual clinician notes nor has the ability to predict complications been tested. We hypothesize that a rules-based algorithm can successfully identify patients with the diseases in the Revised Cardiac Risk Index (RCRI). Patients undergoing surgery at the University of California, Los Angeles Health System between April 1, 2013 and July 1, 2016 and who had at least 2 previous office visits were included. For each disease in the RCRI except renal failure-congestive heart failure, ischemic heart disease, cerebrovascular disease, and diabetes mellitus-diagnosis algorithms were created based on diagnostic and standard clinical treatment criteria. For each disease state, the prevalence of the disease as determined by the algorithm, International Classification of Disease (ICD) code, and anesthesiologist's preoperative note were determined. Additionally, 400 American Society of Anesthesiologists classes III and IV cases were randomly chosen for manual review by an anesthesiologist. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were determined using the manual review as a gold standard. Last, the ability of the RCRI as calculated by each of the methods to predict in-hospital mortality was determined, and the time necessary to run the algorithms was calculated. A total of 64,151 patients met inclusion criteria for the study. In general, the incidence of definite or likely disease determined by the algorithms was higher than that detected by the anesthesiologist. Additionally, in all disease states, the prevalence of disease was always lowest for the ICD codes, followed by the preoperative note, followed by the algorithms. In the subset of patients for whom the records were manually reviewed, the algorithms were generally the most sensitive and the ICD codes the most specific. When computing the modified RCRI using each of the methods, the modified RCRI from the algorithms predicted in-hospital mortality with an area under the receiver operating characteristic curve of 0.70 (0.67-0.73), which compared to 0.70 (0.67-0.72) for ICD codes and 0.64 (0.61-0.67) for the preoperative note. On average, the algorithms took 12.64 ± 1.20 minutes to run on 1.4 million patients. Rules-based algorithms for disease in the RCRI can be created that perform with a similar discriminative ability as compared to physician notes and ICD codes but with significantly increased economies of scale.

SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SIFTER search: a web server for accurate phylogeny-based protein function prediction

DOE PAGES

Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

2015-05-15

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

SEC proton prediction model: verification and analysis.

PubMed

Balch, C C

1999-06-01

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Social Media: Menagerie of Metrics

DTIC Science & Technology

2010-01-27

intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm . An EA...Cloning - 22 Animals were cloned to date; genetic algorithms can help prediction (e.g. “elitism” - attempts to ensure selection by including performers...28, 2010 Evolutionary Algorithm • Evolutionary algorithm From Wikipedia, the free encyclopedia Artificial intelligence portal In artificial

Accelerated probabilistic inference of RNA structure evolution

PubMed Central

Holmes, Ian

2005-01-01

Background Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. Results We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. Conclusion A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License. PMID:15790387

The Icarus challenge - Predicting vulnerability to climate change using an algorithm-based species' trait approach

EPA Science Inventory

The Icarus challenge - Predicting vulnerability to climate change using an algorithm-based species’ trait approachHenry Lee II, Christina Folger, Deborah A. Reusser, Patrick Clinton, and Rene Graham1 U.S. EPA, Western Ecology Division, Newport, OR USA E-mail: lee.henry@ep...

NASA Acting Deputy Chief Technologist Briefed on Operation of Sonic Boom Prediction Algorithms

NASA Image and Video Library

2017-08-29

NASA Acting Deputy Chief Technologist Vicki Crips being briefed by Tim Cox, Controls Engineer at NASA’s Armstrong Flight Research Center at Edwards, California, on the operation of the sonic boom prediction algorithms being used in engineering simulation for the NASA Supersonic Quest program.

Validation of the alternating conditional estimation algorithm for estimation of flexible extensions of Cox's proportional hazards model with nonlinear constraints on the parameters.

PubMed

Wynant, Willy; Abrahamowicz, Michal

2016-11-01

Standard optimization algorithms for maximizing likelihood may not be applicable to the estimation of those flexible multivariable models that are nonlinear in their parameters. For applications where the model's structure permits separating estimation of mutually exclusive subsets of parameters into distinct steps, we propose the alternating conditional estimation (ACE) algorithm. We validate the algorithm, in simulations, for estimation of two flexible extensions of Cox's proportional hazards model where the standard maximum partial likelihood estimation does not apply, with simultaneous modeling of (1) nonlinear and time-dependent effects of continuous covariates on the hazard, and (2) nonlinear interaction and main effects of the same variable. We also apply the algorithm in real-life analyses to estimate nonlinear and time-dependent effects of prognostic factors for mortality in colon cancer. Analyses of both simulated and real-life data illustrate good statistical properties of the ACE algorithm and its ability to yield new potentially useful insights about the data structure. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effectiveness of a Predictive Algorithm in the Prevention of Exercise-Induced Hypoglycemia in Type 1 Diabetes.

PubMed

Abraham, Mary B; Davey, Raymond; O'Grady, Michael J; Ly, Trang T; Paramalingam, Nirubasini; Fournier, Paul A; Roy, Anirban; Grosman, Benyamin; Kurtz, Natalie; Fairchild, Janice M; King, Bruce R; Ambler, Geoffrey R; Cameron, Fergus; Jones, Timothy W; Davis, Elizabeth A

2016-09-01

Sensor-augmented pump therapy (SAPT) with a predictive algorithm to suspend insulin delivery has the potential to reduce hypoglycemia, a known obstacle in improving physical activity in patients with type 1 diabetes. The predictive low glucose management (PLGM) system employs a predictive algorithm that suspends basal insulin when hypoglycemia is predicted. The aim of this study was to determine the efficacy of this algorithm in the prevention of exercise-induced hypoglycemia under in-clinic conditions. This was a randomized, controlled cross-over study in which 25 participants performed 2 consecutive sessions of 30 min of moderate-intensity exercise while on basal continuous subcutaneous insulin infusion on 2 study days: a control day with SAPT alone and an intervention day with SAPT and PLGM. The predictive algorithm suspended basal insulin when sensor glucose was predicted to be below the preset hypoglycemic threshold in 30 min. We tested preset hypoglycemic thresholds of 70 and 80 mg/dL. The primary outcome was the requirement for hypoglycemia treatment (symptomatic hypoglycemia with plasma glucose <63 mg/dL or plasma glucose <50 mg/dL) and was compared in both control and intervention arms. Results were analyzed in 19 participants. In the intervention arm with both thresholds, only 6 participants (32%) required treatment for hypoglycemia compared with 17 participants (89%) in the control arm (P = 0.003). In participants with a 2-h pump suspension on intervention days, the plasma glucose was 84 ± 12 and 99 ± 24 mg/dL at thresholds of 70 and 80 mg/dL, respectively. SAPT with PLGM reduced the need for hypoglycemia treatment after moderate-intensity exercise in an in-clinic setting.

Evaluation of the genotypic prediction of HIV-1 coreceptor use versus a phenotypic assay and correlation with the virological response to maraviroc: the ANRS GenoTropism study.

PubMed

Recordon-Pinson, Patricia; Soulié, Cathia; Flandre, Philippe; Descamps, Diane; Lazrek, Mouna; Charpentier, Charlotte; Montes, Brigitte; Trabaud, Mary-Anne; Cottalorda, Jacqueline; Schneider, Véronique; Morand-Joubert, Laurence; Tamalet, Catherine; Desbois, Delphine; Macé, Muriel; Ferré, Virginie; Vabret, Astrid; Ruffault, Annick; Pallier, Coralie; Raymond, Stéphanie; Izopet, Jacques; Reynes, Jacques; Marcelin, Anne-Geneviève; Masquelier, Bernard

2010-08-01

Genotypic algorithms for prediction of HIV-1 coreceptor usage need to be evaluated in a clinical setting. We aimed at studying (i) the correlation of genotypic prediction of coreceptor use in comparison with a phenotypic assay and (ii) the relationship between genotypic prediction of coreceptor use at baseline and the virological response (VR) to a therapy including maraviroc (MVC). Antiretroviral-experienced patients were included in the MVC Expanded Access Program if they had an R5 screening result with Trofile (Monogram Biosciences). V3 loop sequences were determined at screening, and coreceptor use was predicted using 13 genotypic algorithms or combinations of algorithms. Genotypic predictions were compared to Trofile; dual or mixed (D/M) variants were considered as X4 variants. Both genotypic and phenotypic results were obtained for 189 patients at screening, with 54 isolates scored as X4 or D/M and 135 scored as R5 with Trofile. The highest sensitivity (59.3%) for detection of X4 was obtained with the Geno2pheno algorithm, with a false-positive rate set up at 10% (Geno2pheno10). In the 112 patients receiving MVC, a plasma viral RNA load of <50 copies/ml was obtained in 68% of cases at month 6. In multivariate analysis, the prediction of the X4 genotype at baseline with the Geno2pheno10 algorithm including baseline viral load and CD4 nadir was independently associated with a worse VR at months 1 and 3. The baseline weighted genotypic sensitivity score was associated with VR at month 6. There were strong arguments in favor of using genotypic coreceptor use assays for determining which patients would respond to CCR5 antagonist.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

Testing an Earthquake Prediction Algorithm: The 2016 New Zealand and Chile Earthquakes

NASA Astrophysics Data System (ADS)

Kossobokov, Vladimir G.

2017-05-01

The 13 November 2016, M7.8, 54 km NNE of Amberley, New Zealand and the 25 December 2016, M7.6, 42 km SW of Puerto Quellon, Chile earthquakes happened outside the area of the on-going real-time global testing of the intermediate-term middle-range earthquake prediction algorithm M8, accepted in 1992 for the M7.5+ range. Naturally, over the past two decades, the level of registration of earthquakes worldwide has grown significantly and by now is sufficient for diagnosis of times of increased probability (TIPs) by the M8 algorithm on the entire territory of New Zealand and Southern Chile as far as below 40°S. The mid-2016 update of the M8 predictions determines TIPs in the additional circles of investigation (CIs) where the two earthquakes have happened. Thus, after 50 semiannual updates in the real-time prediction mode, we (1) confirm statistically approved high confidence of the M8-MSc predictions and (2) conclude a possibility of expanding the territory of the Global Test of the algorithms M8 and MSc in an apparently necessary revision of the 1992 settings.

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

PubMed Central

Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon

2016-01-01

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

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.