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  1. Predictive analytics technology review: Similarity-based modeling and beyond

    Energy Technology Data Exchange (ETDEWEB)

    Herzog, James; Doan, Don; Gandhi, Devang; Nieman, Bill

    2010-09-15

    Over 11 years ago, SmartSignal introduced Predictive Analytics for eliminating equipment failures, using its patented SBM technology. SmartSignal continues to lead and dominate the market and, in 2010, went one step further and introduced Predictive Diagnostics. Now, SmartSignal is combining Predictive Diagnostics with RCM methodology and industry expertise. FMEA logic reengineers maintenance work management, eliminates unneeded inspections, and focuses efforts on the real issues. This integrated solution significantly lowers maintenance costs, protects against critical asset failures, and improves commercial availability, and reduces work orders 20-40%. Learn how.

  2. Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

    Science.gov (United States)

    Sharafoddini, Anis; Dubin, Joel A

    2017-01-01

    Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health

  3. The predictive model on the user reaction time using the information similarity

    International Nuclear Information System (INIS)

    Lee, Sung Jin; Heo, Gyun Young; Chang, Soon Heung

    2005-01-01

    Human performance is frequently degraded because people forget. Memory is one of brain processes that are important when trying to understand how people process information. Although a large number of studies have been made on the human performance, little is known about the similarity effect in human performance. The purpose of this paper is to propose and validate the quantitative and predictive model on the human response time in the user interface with the concept of similarity. However, it is not easy to explain the human performance with only similarity or information amount. We are confronted by two difficulties: making the quantitative model on the human response time with the similarity and validating the proposed model by experimental work. We made the quantitative model based on the Hick's law and the law of practice. In addition, we validated the model with various experimental conditions by measuring participants' response time in the environment of computer-based display. Experimental results reveal that the human performance is improved by the user interface's similarity. We think that the proposed model is useful for the user interface design and evaluation phases

  4. Predictive modeling of human perception subjectivity: feasibility study of mammographic lesion similarity

    Science.gov (United States)

    Xu, Songhua; Hudson, Kathleen; Bradley, Yong; Daley, Brian J.; Frederick-Dyer, Katherine; Tourassi, Georgia

    2012-02-01

    The majority of clinical content-based image retrieval (CBIR) studies disregard human perception subjectivity, aiming to duplicate the consensus expert assessment of the visual similarity on example cases. The purpose of our study is twofold: i) discern better the extent of human perception subjectivity when assessing the visual similarity of two images with similar semantic content, and (ii) explore the feasibility of personalized predictive modeling of visual similarity. We conducted a human observer study in which five observers of various expertise were shown ninety-nine triplets of mammographic masses with similar BI-RADS descriptors and were asked to select the two masses with the highest visual relevance. Pairwise agreement ranged between poor and fair among the five observers, as assessed by the kappa statistic. The observers' self-consistency rate was remarkably low, based on repeated questions where either the orientation or the presentation order of a mass was changed. Various machine learning algorithms were explored to determine whether they can predict each observer's personalized selection using textural features. Many algorithms performed with accuracy that exceeded each observer's self-consistency rate, as determined using a cross-validation scheme. This accuracy was statistically significantly higher than would be expected by chance alone (two-tailed p-value ranged between 0.001 and 0.01 for all five personalized models). The study confirmed that human perception subjectivity should be taken into account when developing CBIR-based medical applications.

  5. Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.

    Science.gov (United States)

    van Westen, Gerard J P; Bender, Andreas; Overington, John P

    2014-10-01

    Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of 'orthogonally resistant' agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed 'proteochemometric modelling' (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

  6. Accuracy test for link prediction in terms of similarity index: The case of WS and BA models

    Science.gov (United States)

    Ahn, Min-Woo; Jung, Woo-Sung

    2015-07-01

    Link prediction is a technique that uses the topological information in a given network to infer the missing links in it. Since past research on link prediction has primarily focused on enhancing performance for given empirical systems, negligible attention has been devoted to link prediction with regard to network models. In this paper, we thus apply link prediction to two network models: The Watts-Strogatz (WS) model and Barabási-Albert (BA) model. We attempt to gain a better understanding of the relation between accuracy and each network parameter (mean degree, the number of nodes and the rewiring probability in the WS model) through network models. Six similarity indices are used, with precision and area under the ROC curve (AUC) value as the accuracy metrics. We observe a positive correlation between mean degree and accuracy, and size independence of the AUC value.

  7. Similarity-based multi-model ensemble approach for 1-15-day advance prediction of monsoon rainfall over India

    Science.gov (United States)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati

    2018-04-01

    The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.

  8. The effects of gravity on human walking: a new test of the dynamic similarity hypothesis using a predictive model.

    Science.gov (United States)

    Raichlen, David A

    2008-09-01

    The dynamic similarity hypothesis (DSH) suggests that differences in animal locomotor biomechanics are due mostly to differences in size. According to the DSH, when the ratios of inertial to gravitational forces are equal between two animals that differ in size [e.g. at equal Froude numbers, where Froude = velocity2/(gravity x hip height)], their movements can be made similar by multiplying all time durations by one constant, all forces by a second constant and all linear distances by a third constant. The DSH has been generally supported by numerous comparative studies showing that as inertial forces differ (i.e. differences in the centripetal force acting on the animal due to variation in hip heights), animals walk with dynamic similarity. However, humans walking in simulated reduced gravity do not walk with dynamically similar kinematics. The simulated gravity experiments did not completely account for the effects of gravity on all body segments, and the importance of gravity in the DSH requires further examination. This study uses a kinematic model to predict the effects of gravity on human locomotion, taking into account both the effects of gravitational forces on the upper body and on the limbs. Results show that dynamic similarity is maintained in altered gravitational environments. Thus, the DSH does account for differences in the inertial forces governing locomotion (e.g. differences in hip height) as well as differences in the gravitational forces governing locomotion.

  9. A study of the predictive model on the user reaction time using the information amount and similarity

    International Nuclear Information System (INIS)

    Lee, Sungjin; Heo, Gyunyoung; Chang, S.H.

    2004-01-01

    Human operations through a user interface are divided into two types. The one is the single operation that is performed on a static interface. The other is the sequential operation that achieves a goal by handling several displays through operator's navigation in the crt-based console. Sequential operation has similar meaning with continuous task. Most operations in recently developed computer applications correspond to the sequential operation, and the single operation can be considered as a part of the sequential operation. In the area of HCI (human computer interaction) evaluation, the Hick-Hyman law counts as the most powerful theory. The most important factor in the equation of Hick-Hyman law about choice reaction time is the quantified amount of information conveyed by a statement, stimulus, or event. Generally, we can expect that if there are some similarities between a series of interfaces, human operator is able to use his attention resource effectively. That is the performance of human operator is increased by the similarity. The similarity may be able to affect the allocation of attention resource based on separate STSS (short-term sensory store) and long-term memory. There are theories related with this concept, which are task switching paradigm and the law of practice. However, it is not easy to explain the human operator performance with only the similarity or the information amount. There are few theories to explain the performance with the combination of the similarity and the information amount. The objective of this paper is to purpose and validate the quantitative and predictive model on the user reaction time in CRT-based displays. Another objective is to validate various theories related with human cognition and perception, which are Hick-Hyman law and the law of practice as representative theories. (author)

  10. Lagrangian-similarity diffusion-deposition model

    International Nuclear Information System (INIS)

    Horst, T.W.

    1979-01-01

    A Lagrangian-similarity diffusion model has been incorporated into the surface-depletion deposition model. This model predicts vertical concentration profiles far downwind of the source that agree with those of a one-dimensional gradient-transfer model

  11. Modeling of similar economies

    Directory of Open Access Journals (Sweden)

    Sergey B. Kuznetsov

    2017-06-01

    Full Text Available Objective to obtain dimensionless criteria ndash economic indices characterizing the national economy and not depending on its size. Methods mathematical modeling theory of dimensions processing statistical data. Results basing on differential equations describing the national economy with the account of economical environment resistance two dimensionless criteria are obtained which allow to compare economies regardless of their sizes. With the theory of dimensions we show that the obtained indices are not accidental. We demonstrate the implementation of the obtained dimensionless criteria for the analysis of behavior of certain countriesrsquo economies. Scientific novelty the dimensionless criteria are obtained ndash economic indices which allow to compare economies regardless of their sizes and to analyze the dynamic changes in the economies with time. nbsp Practical significance the obtained results can be used for dynamic and comparative analysis of different countriesrsquo economies regardless of their sizes.

  12. Self-similar cosmological models

    Energy Technology Data Exchange (ETDEWEB)

    Chao, W Z [Cambridge Univ. (UK). Dept. of Applied Mathematics and Theoretical Physics

    1981-07-01

    The kinematics and dynamics of self-similar cosmological models are discussed. The degrees of freedom of the solutions of Einstein's equations for different types of models are listed. The relation between kinematic quantities and the classifications of the self-similarity group is examined. All dust local rotational symmetry models have been found.

  13. Predicting the performance of fingerprint similarity searching.

    Science.gov (United States)

    Vogt, Martin; Bajorath, Jürgen

    2011-01-01

    Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

  14. A study of the predictive model on the user reaction time using the information amount and its similarity

    International Nuclear Information System (INIS)

    Lee, Sung Jin; Heo, Gyun Young; Chang, Soon Heung

    2004-01-01

    There are lots of studies on the user interface evaluation since it started. Recent studies focus on the contextual information of the user interface. We knew that the user reaction time increases as the amount of information increases. But, the relation between the contextual information and the user reaction time may be unknown. In this study, we proposed the similarity as one of the contextual information. We can expect that the similarity decreases the user reaction time. The goal of this study is to find some correlation about the user reaction time with both the information amount and the similarity. The experiment was performed with 20 participants. The results of experiment demonstrated our proposals

  15. A COMPARISON OF SEMANTIC SIMILARITY MODELS IN EVALUATING CONCEPT SIMILARITY

    Directory of Open Access Journals (Sweden)

    Q. X. Xu

    2012-08-01

    Full Text Available The semantic similarities are important in concept definition, recognition, categorization, interpretation, and integration. Many semantic similarity models have been established to evaluate semantic similarities of objects or/and concepts. To find out the suitability and performance of different models in evaluating concept similarities, we make a comparison of four main types of models in this paper: the geometric model, the feature model, the network model, and the transformational model. Fundamental principles and main characteristics of these models are introduced and compared firstly. Land use and land cover concepts of NLCD92 are employed as examples in the case study. The results demonstrate that correlations between these models are very high for a possible reason that all these models are designed to simulate the similarity judgement of human mind.

  16. A Novel Hybrid Similarity Calculation Model

    Directory of Open Access Journals (Sweden)

    Xiaoping Fan

    2017-01-01

    Full Text Available This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.

  17. Modeling Timbre Similarity of Short Music Clips.

    Science.gov (United States)

    Siedenburg, Kai; Müllensiefen, Daniel

    2017-01-01

    There is evidence from a number of recent studies that most listeners are able to extract information related to song identity, emotion, or genre from music excerpts with durations in the range of tenths of seconds. Because of these very short durations, timbre as a multifaceted auditory attribute appears as a plausible candidate for the type of features that listeners make use of when processing short music excerpts. However, the importance of timbre in listening tasks that involve short excerpts has not yet been demonstrated empirically. Hence, the goal of this study was to develop a method that allows to explore to what degree similarity judgments of short music clips can be modeled with low-level acoustic features related to timbre. We utilized the similarity data from two large samples of participants: Sample I was obtained via an online survey, used 16 clips of 400 ms length, and contained responses of 137,339 participants. Sample II was collected in a lab environment, used 16 clips of 800 ms length, and contained responses from 648 participants. Our model used two sets of audio features which included commonly used timbre descriptors and the well-known Mel-frequency cepstral coefficients as well as their temporal derivates. In order to predict pairwise similarities, the resulting distances between clips in terms of their audio features were used as predictor variables with partial least-squares regression. We found that a sparse selection of three to seven features from both descriptor sets-mainly encoding the coarse shape of the spectrum as well as spectrotemporal variability-best predicted similarities across the two sets of sounds. Notably, the inclusion of non-acoustic predictors of musical genre and record release date allowed much better generalization performance and explained up to 50% of shared variance ( R 2 ) between observations and model predictions. Overall, the results of this study empirically demonstrate that both acoustic features related

  18. Similarity relations in visual search predict rapid visual categorization

    Science.gov (United States)

    Mohan, Krithika; Arun, S. P.

    2012-01-01

    How do we perform rapid visual categorization?It is widely thought that categorization involves evaluating the similarity of an object to other category items, but the underlying features and similarity relations remain unknown. Here, we hypothesized that categorization performance is based on perceived similarity relations between items within and outside the category. To this end, we measured the categorization performance of human subjects on three diverse visual categories (animals, vehicles, and tools) and across three hierarchical levels (superordinate, basic, and subordinate levels among animals). For the same subjects, we measured their perceived pair-wise similarities between objects using a visual search task. Regardless of category and hierarchical level, we found that the time taken to categorize an object could be predicted using its similarity to members within and outside its category. We were able to account for several classic categorization phenomena, such as (a) the longer times required to reject category membership; (b) the longer times to categorize atypical objects; and (c) differences in performance across tasks and across hierarchical levels. These categorization times were also accounted for by a model that extracts coarse structure from an image. The striking agreement observed between categorization and visual search suggests that these two disparate tasks depend on a shared coarse object representation. PMID:23092947

  19. Notions of similarity for systems biology models.

    Science.gov (United States)

    Henkel, Ron; Hoehndorf, Robert; Kacprowski, Tim; Knüpfer, Christian; Liebermeister, Wolfram; Waltemath, Dagmar

    2018-01-01

    Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users' intuition about model similarity, and to support complex model searches in databases. © The Author 2016. Published by Oxford University Press.

  20. Notions of similarity for computational biology models

    KAUST Repository

    Waltemath, Dagmar

    2016-03-21

    Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users\\' intuition about model similarity, and to support complex model searches in databases.

  1. Notions of similarity for computational biology models

    KAUST Repository

    Waltemath, Dagmar; Henkel, Ron; Hoehndorf, Robert; Kacprowski, Tim; Knuepfer, Christian; Liebermeister, Wolfram

    2016-01-01

    Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of similarity may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users' intuition about model similarity, and to support complex model searches in databases.

  2. On self-similar Tolman models

    International Nuclear Information System (INIS)

    Maharaj, S.D.

    1988-01-01

    The self-similar spherically symmetric solutions of the Einstein field equation for the case of dust are identified. These form a subclass of the Tolman models. These self-similar models contain the solution recently presented by Chi [J. Math. Phys. 28, 1539 (1987)], thereby refuting the claim of having found a new solution to the Einstein field equations

  3. Do number of days with low back pain and patterns of episodes of pain have similar outcomes in a biopsychosocial prediction model?

    DEFF Research Database (Denmark)

    Lemeunier, N; Leboeuf-Yde, C; Gagey, O

    2016-01-01

    are similar, regardless which of the two classifications is used. METHOD: During 1 year, 49- or 50-year-old people from the Danish general population were sent fortnightly automated text messages (SMS-Track) asking them if they had any LBP in the past fortnight. Responses for the whole year were......PURPOSES: We used two different methods to classify low back pain (LBP) in the general population (1) to assess the overlapping of individuals within the different subgroups in those two classifications, (2) to explore if the associations between LBP and some selected bio-psychosocial factors...... with a questionnaire at baseline 9 years earlier, were entered into regression models to investigate their associations with the subgroups of the two classifications of LBP and the results compared. RESULTS: The percentage of agreement between categories of the two classification systems was above 68 % (Kappa 0...

  4. Similarity and Modeling in Science and Engineering

    CERN Document Server

    Kuneš, Josef

    2012-01-01

    The present text sets itself in relief to other titles on the subject in that it addresses the means and methodologies versus a narrow specific-task oriented approach. Concepts and their developments which evolved to meet the changing needs of applications are addressed. This approach provides the reader with a general tool-box to apply to their specific needs. Two important tools are presented: dimensional analysis and the similarity analysis methods. The fundamental point of view, enabling one to sort all models, is that of information flux between a model and an original expressed by the similarity and abstraction. Each chapter includes original examples and ap-plications. In this respect, the models can be divided into several groups. The following models are dealt with separately by chapter; mathematical and physical models, physical analogues, deterministic, stochastic, and cybernetic computer models. The mathematical models are divided into asymptotic and phenomenological models. The phenomenological m...

  5. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  6. Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors

    Science.gov (United States)

    Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean

    2016-01-01

    A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?

  7. Similarities and Differences Between Warped Linear Prediction and Laguerre Linear Prediction

    NARCIS (Netherlands)

    Brinker, Albertus C. den; Krishnamoorthi, Harish; Verbitskiy, Evgeny A.

    2011-01-01

    Linear prediction has been successfully applied in many speech and audio processing systems. This paper presents the similarities and differences between two classes of linear prediction schemes, namely, Warped Linear Prediction (WLP) and Laguerre Linear Prediction (LLP). It is shown that both

  8. A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

    Science.gov (United States)

    Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

    2017-12-14

    Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

  9. Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

    Directory of Open Access Journals (Sweden)

    Joon Lee

    Full Text Available Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1 to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2 to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made.We deployed a cosine-similarity-based patient similarity metric (PSM to an intensive care unit (ICU database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care.The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR systems, our novel medical data analytics

  10. Personalized Mortality Prediction Driven by Electronic Medical Data and a Patient Similarity Metric

    Science.gov (United States)

    Lee, Joon; Maslove, David M.; Dubin, Joel A.

    2015-01-01

    Background Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. Methods and Findings We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. Conclusions The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our

  11. Personalized mortality prediction driven by electronic medical data and a patient similarity metric.

    Science.gov (United States)

    Lee, Joon; Maslove, David M; Dubin, Joel A

    2015-01-01

    Clinical outcome prediction normally employs static, one-size-fits-all models that perform well for the average patient but are sub-optimal for individual patients with unique characteristics. In the era of digital healthcare, it is feasible to dynamically personalize decision support by identifying and analyzing similar past patients, in a way that is analogous to personalized product recommendation in e-commerce. Our objectives were: 1) to prove that analyzing only similar patients leads to better outcome prediction performance than analyzing all available patients, and 2) to characterize the trade-off between training data size and the degree of similarity between the training data and the index patient for whom prediction is to be made. We deployed a cosine-similarity-based patient similarity metric (PSM) to an intensive care unit (ICU) database to identify patients that are most similar to each patient and subsequently to custom-build 30-day mortality prediction models. Rich clinical and administrative data from the first day in the ICU from 17,152 adult ICU admissions were analyzed. The results confirmed that using data from only a small subset of most similar patients for training improves predictive performance in comparison with using data from all available patients. The results also showed that when too few similar patients are used for training, predictive performance degrades due to the effects of small sample sizes. Our PSM-based approach outperformed well-known ICU severity of illness scores. Although the improved prediction performance is achieved at the cost of increased computational burden, Big Data technologies can help realize personalized data-driven decision support at the point of care. The present study provides crucial empirical evidence for the promising potential of personalized data-driven decision support systems. With the increasing adoption of electronic medical record (EMR) systems, our novel medical data analytics contributes to

  12. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  13. Similarity search of business process models

    NARCIS (Netherlands)

    Dumas, M.; García-Bañuelos, L.; Dijkman, R.M.

    2009-01-01

    Similarity search is a general class of problems in which a given object, called a query object, is compared against a collection of objects in order to retrieve those that most closely resemble the query object. This paper reviews recent work on an instance of this class of problems, where the

  14. Measuring similarity between business process models

    NARCIS (Netherlands)

    Dongen, van B.F.; Dijkman, R.M.; Mendling, J.

    2007-01-01

    Quality aspects become increasingly important when business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding

  15. Quasi-Similarity Model of Synthetic Jets

    Czech Academy of Sciences Publication Activity Database

    Tesař, Václav; Kordík, Jozef

    2009-01-01

    Roč. 149, č. 2 (2009), s. 255-265 ISSN 0924-4247 R&D Projects: GA AV ČR IAA200760705; GA ČR GA101/07/1499 Institutional research plan: CEZ:AV0Z20760514 Keywords : jets * synthetic jets * similarity solution Subject RIV: BK - Fluid Dynamics Impact factor: 1.674, year: 2009 http://www.sciencedirect.com

  16. Exploiting similarity in turbulent shear flows for turbulence modeling

    Science.gov (United States)

    Robinson, David F.; Harris, Julius E.; Hassan, H. A.

    1992-01-01

    It is well known that current k-epsilon models cannot predict the flow over a flat plate and its wake. In an effort to address this issue and other issues associated with turbulence closure, a new approach for turbulence modeling is proposed which exploits similarities in the flow field. Thus, if we consider the flow over a flat plate and its wake, then in addition to taking advantage of the log-law region, we can exploit the fact that the flow becomes self-similar in the far wake. This latter behavior makes it possible to cast the governing equations as a set of total differential equations. Solutions of this set and comparison with measured shear stress and velocity profiles yields the desired set of model constants. Such a set is, in general, different from other sets of model constants. The rational for such an approach is that if we can correctly model the flow over a flat plate and its far wake, then we can have a better chance of predicting the behavior in between. It is to be noted that the approach does not appeal, in any way, to the decay of homogeneous turbulence. This is because the asymptotic behavior of the flow under consideration is not representative of the decay of homogeneous turbulence.

  17. Exploiting similarity in turbulent shear flows for turbulence modeling

    Science.gov (United States)

    Robinson, David F.; Harris, Julius E.; Hassan, H. A.

    1992-12-01

    It is well known that current k-epsilon models cannot predict the flow over a flat plate and its wake. In an effort to address this issue and other issues associated with turbulence closure, a new approach for turbulence modeling is proposed which exploits similarities in the flow field. Thus, if we consider the flow over a flat plate and its wake, then in addition to taking advantage of the log-law region, we can exploit the fact that the flow becomes self-similar in the far wake. This latter behavior makes it possible to cast the governing equations as a set of total differential equations. Solutions of this set and comparison with measured shear stress and velocity profiles yields the desired set of model constants. Such a set is, in general, different from other sets of model constants. The rational for such an approach is that if we can correctly model the flow over a flat plate and its far wake, then we can have a better chance of predicting the behavior in between. It is to be noted that the approach does not appeal, in any way, to the decay of homogeneous turbulence. This is because the asymptotic behavior of the flow under consideration is not representative of the decay of homogeneous turbulence.

  18. Self-similar two-particle separation model

    DEFF Research Database (Denmark)

    Lüthi, Beat; Berg, Jacob; Ott, Søren

    2007-01-01

    .g.; in the inertial range as epsilon−1/3r2/3. Particle separation is modeled as a Gaussian process without invoking information of Eulerian acceleration statistics or of precise shapes of Eulerian velocity distribution functions. The time scale is a function of S2(r) and thus of the Lagrangian evolving separation......We present a new stochastic model for relative two-particle separation in turbulence. Inspired by material line stretching, we suggest that a similar process also occurs beyond the viscous range, with time scaling according to the longitudinal second-order structure function S2(r), e....... The model predictions agree with numerical and experimental results for various initial particle separations. We present model results for fixed time and fixed scale statistics. We find that for the Richardson-Obukhov law, i.e., =gepsilont3, to hold and to also be observed in experiments, high Reynolds...

  19. Basketball predictions in the NCAAB and NBA: Similarities and differences

    OpenAIRE

    Zimmermann , Albrecht

    2016-01-01

    International audience; Most work on predicting the outcome of basketball matches so far has focused on NCAAB games. Since NCAAB and professional (NBA) basketball have a number of differences, it is not clear to what degree these results can be transferred. We explore a number of different representations, training settings, and classifiers, and contrast their results on NCAAB and NBA data. We find that adjusted efficiencies work well for the NBA, that the NCAAB regular season is not ideal fo...

  20. Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography.

    Science.gov (United States)

    Park, Soo Hyun; Talebi, Mohammad; Amos, Ruth I J; Tyteca, Eva; Haddad, Paul R; Szucs, Roman; Pohl, Christopher A; Dolan, John W

    2017-11-10

    Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Q ext(F2) 2 >0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min. Crown Copyright

  1. The continuous similarity model of bulk soil-water evaporation

    Science.gov (United States)

    Clapp, R. B.

    1983-01-01

    The continuous similarity model of evaporation is described. In it, evaporation is conceptualized as a two stage process. For an initially moist soil, evaporation is first climate limited, but later it becomes soil limited. During the latter stage, the evaporation rate is termed evaporability, and mathematically it is inversely proportional to the evaporation deficit. A functional approximation of the moisture distribution within the soil column is also included in the model. The model was tested using data from four experiments conducted near Phoenix, Arizona; and there was excellent agreement between the simulated and observed evaporation. The model also predicted the time of transition to the soil limited stage reasonably well. For one of the experiments, a third stage of evaporation, when vapor diffusion predominates, was observed. The occurrence of this stage was related to the decrease in moisture at the surface of the soil. The continuous similarity model does not account for vapor flow. The results show that climate, through the potential evaporation rate, has a strong influence on the time of transition to the soil limited stage. After this transition, however, bulk evaporation is independent of climate until the effects of vapor flow within the soil predominate.

  2. Does implied community size predict likeability of a similar stranger?

    Science.gov (United States)

    Launay, Jacques; Dunbar, Robin I M

    2015-01-01

    Homophily, the tendency for people to cluster with similar others, has primarily been studied in terms of proximal, psychological causes, such as a tendency to have positive associations with people who share traits with us. Here we investigate whether homophily could be correlated with perceived group membership, given that sharing traits with other people might signify membership of a specific community. In order to investigate this, we tested whether the amount of homophily that occurs between strangers is dependent on the number of people they believe share the common trait (i.e. the size of group that the trait identifies). In two experiments, we show that more exclusive (smaller) groups evoke more positive ratings of the likeability of a stranger. When groups appear to be too inclusive (i.e. large) homophily no longer occurs, suggesting that it is not only positive associations with a trait that cause homophily, but a sense of the exclusiveness of a group is also important. These results suggest that group membership based on a variety of traits can encourage cohesion between people from diverse backgrounds, and may be a useful tool in overcoming differences between groups.

  3. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  4. Prediction of the human response time with the similarity and quantity of information

    International Nuclear Information System (INIS)

    Lee, Sungjin; Heo, Gyunyoung; Chang, Soon Heung

    2006-01-01

    Memory is one of brain processes that are important when trying to understand how people process information. Although a large number of studies have been made on the human performance, little is known about the similarity effect in human performance. The purpose of this paper is to propose and validate the quantitative and predictive model on the human response time in the user interface with the concept of similarity. However, it is not easy to explain the human performance with only similarity or information amount. We are confronted by two difficulties: making the quantitative model on the human response time with the similarity and validating the proposed model by experimental work. We made the quantitative model based on the Hick's law and the law of practice. In addition, we validated the model with various experimental conditions by measuring participants' response time in the environment of computer-based display. Experimental results reveal that the human performance is improved by the user interface's similarity. We think that the proposed model is useful for the user interface design and evaluation phases

  5. K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

    Directory of Open Access Journals (Sweden)

    Lv Tao

    2017-01-01

    Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.

  6. Vere-Jones' self-similar branching model

    International Nuclear Information System (INIS)

    Saichev, A.; Sornette, D.

    2005-01-01

    Motivated by its potential application to earthquake statistics as well as for its intrinsic interest in the theory of branching processes, we study the exactly self-similar branching process introduced recently by Vere-Jones. This model extends the ETAS class of conditional self-excited branching point-processes of triggered seismicity by removing the problematic need for a minimum (as well as maximum) earthquake size. To make the theory convergent without the need for the usual ultraviolet and infrared cutoffs, the distribution of magnitudes m ' of daughters of first-generation of a mother of magnitude m has two branches m ' ' >m with exponent β+d, where β and d are two positive parameters. We investigate the condition and nature of the subcritical, critical, and supercritical regime in this and in an extended version interpolating smoothly between several models. We predict that the distribution of magnitudes of events triggered by a mother of magnitude m over all generations has also two branches m ' ' >m with exponent β+h, with h=d√(1-s), where s is the fraction of triggered events. This corresponds to a renormalization of the exponent d into h by the hierarchy of successive generations of triggered events. For a significant part of the parameter space, the distribution of magnitudes over a full catalog summed over an average steady flow of spontaneous sources (immigrants) reproduces the distribution of the spontaneous sources with a single branch and is blind to the exponents β,d of the distribution of triggered events. Since the distribution of earthquake magnitudes is usually obtained with catalogs including many sequences, we conclude that the two branches of the distribution of aftershocks are not directly observable and the model is compatible with real seismic catalogs. In summary, the exactly self-similar Vere-Jones model provides an attractive new approach to model triggered seismicity, which alleviates delicate questions on the role of

  7. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  8. Applicability of the theory of thermodynamic similarity to predict the enthalpies of vaporization of aliphatic aldehydes

    Science.gov (United States)

    Esina, Z. N.; Korchuganova, M. R.

    2015-06-01

    The theory of thermodynamic similarity is used to predict the enthalpies of vaporization of aliphatic aldehydes. The predicted data allow us to calculate the phase diagrams of liquid-vapor equilibrium in a binary water-aliphatic aldehyde system.

  9. QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

    Science.gov (United States)

    Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

    2016-01-01

    Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor.

  10. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  11. Similar words analysis based on POS-CBOW language model

    Directory of Open Access Journals (Sweden)

    Dongru RUAN

    2015-10-01

    Full Text Available Similar words analysis is one of the important aspects in the field of natural language processing, and it has important research and application values in text classification, machine translation and information recommendation. Focusing on the features of Sina Weibo's short text, this paper presents a language model named as POS-CBOW, which is a kind of continuous bag-of-words language model with the filtering layer and part-of-speech tagging layer. The proposed approach can adjust the word vectors' similarity according to the cosine similarity and the word vectors' part-of-speech metrics. It can also filter those similar words set on the base of the statistical analysis model. The experimental result shows that the similar words analysis algorithm based on the proposed POS-CBOW language model is better than that based on the traditional CBOW language model.

  12. Self-Similar Symmetry Model and Cosmic Microwave Background

    Directory of Open Access Journals (Sweden)

    Tomohide eSonoda

    2016-05-01

    Full Text Available In this paper, we present the self-similar symmetry (SSS model that describes the hierarchical structure of the universe. The model is based on the concept of self-similarity, which explains the symmetry of the cosmic microwave background (CMB. The approximate length and time scales of the six hierarchies of the universe---grand unification, electroweak unification, the atom, the pulsar, the solar system, and the galactic system---are derived from the SSS model. In addition, the model implies that the electron mass and gravitational constant could vary with the CMB radiation temperature.

  13. Simulation and similarity using models to understand the world

    CERN Document Server

    Weisberg, Michael

    2013-01-01

    In the 1950s, John Reber convinced many Californians that the best way to solve the state's water shortage problem was to dam up the San Francisco Bay. Against massive political pressure, Reber's opponents persuaded lawmakers that doing so would lead to disaster. They did this not by empirical measurement alone, but also through the construction of a model. Simulation and Similarity explains why this was a good strategy while simultaneously providing an account of modeling and idealization in modern scientific practice. Michael Weisberg focuses on concrete, mathematical, and computational models in his consideration of the nature of models, the practice of modeling, and nature of the relationship between models and real-world phenomena. In addition to a careful analysis of physical, computational, and mathematical models, Simulation and Similarity offers a novel account of the model/world relationship. Breaking with the dominant tradition, which favors the analysis of this relation through logical notions suc...

  14. Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

    Science.gov (United States)

    Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

    2018-05-12

    Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

  15. Inter Genre Similarity Modelling For Automatic Music Genre Classification

    OpenAIRE

    Bagci, Ulas; Erzin, Engin

    2009-01-01

    Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population....

  16. Perception of similarity: a model for social network dynamics

    International Nuclear Information System (INIS)

    Javarone, Marco Alberto; Armano, Giuliano

    2013-01-01

    Some properties of social networks (e.g., the mixing patterns and the community structure) appear deeply influenced by the individual perception of people. In this work we map behaviors by considering similarity and popularity of people, also assuming that each person has his/her proper perception and interpretation of similarity. Although investigated in different ways (depending on the specific scientific framework), from a computational perspective similarity is typically calculated as a distance measure. In accordance with this view, to represent social network dynamics we developed an agent-based model on top of a hyperbolic space on which individual distance measures are calculated. Simulations, performed in accordance with the proposed model, generate small-world networks that exhibit a community structure. We deem this model to be valuable for analyzing the relevant properties of real social networks. (paper)

  17. Algorithmic prediction of inter-song similarity in Western popular music

    NARCIS (Netherlands)

    Novello, A.; Par, van de S.L.J.D.E.; McKinney, M.F.; Kohlrausch, A.G.

    2013-01-01

    We investigate a method for automatic extraction of inter-song similarity for songs selected from several genres of Western popular music. The specific purpose of this approach is to evaluate the predictive power of different feature extraction sets based on human perception of music similarity and

  18. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  19. Brazilian N2 laser similar to imported models

    International Nuclear Information System (INIS)

    Santos, P.A.M. dos; Tavares Junior, A.D.; Silva Reis, H. da; Tagliaferri, A.A.; Massone, C.A.

    1981-09-01

    The development of a high power N 2 Laser, similar to imported models but built enterely with Brazilian materials is described. The prototype shows pulse repetitivity that varies from 1 to 50 per second and has a peak power of 500 kW. (Author) [pt

  20. Bianchi VI0 and III models: self-similar approach

    International Nuclear Information System (INIS)

    Belinchon, Jose Antonio

    2009-01-01

    We study several cosmological models with Bianchi VI 0 and III symmetries under the self-similar approach. We find new solutions for the 'classical' perfect fluid model as well as for the vacuum model although they are really restrictive for the equation of state. We also study a perfect fluid model with time-varying constants, G and Λ. As in other studied models we find that the behaviour of G and Λ are related. If G behaves as a growing time function then Λ is a positive decreasing time function but if G is decreasing then Λ 0 is negative. We end by studying a massive cosmic string model, putting special emphasis in calculating the numerical values of the equations of state. We show that there is no SS solution for a string model with time-varying constants.

  1. High-Throughput Gene Expression Profiles to Define Drug Similarity and Predict Compound Activity.

    Science.gov (United States)

    De Wolf, Hans; Cougnaud, Laure; Van Hoorde, Kirsten; De Bondt, An; Wegner, Joerg K; Ceulemans, Hugo; Göhlmann, Hinrich

    2018-04-01

    By adding biological information, beyond the chemical properties and desired effect of a compound, uncharted compound areas and connections can be explored. In this study, we add transcriptional information for 31K compounds of Janssen's primary screening deck, using the HT L1000 platform and assess (a) the transcriptional connection score for generating compound similarities, (b) machine learning algorithms for generating target activity predictions, and (c) the scaffold hopping potential of the resulting hits. We demonstrate that the transcriptional connection score is best computed from the significant genes only and should be interpreted within its confidence interval for which we provide the stats. These guidelines help to reduce noise, increase reproducibility, and enable the separation of specific and promiscuous compounds. The added value of machine learning is demonstrated for the NR3C1 and HSP90 targets. Support Vector Machine models yielded balanced accuracy values ≥80% when the expression values from DDIT4 & SERPINE1 and TMEM97 & SPR were used to predict the NR3C1 and HSP90 activity, respectively. Combining both models resulted in 22 new and confirmed HSP90-independent NR3C1 inhibitors, providing two scaffolds (i.e., pyrimidine and pyrazolo-pyrimidine), which could potentially be of interest in the treatment of depression (i.e., inhibiting the glucocorticoid receptor (i.e., NR3C1), while leaving its chaperone, HSP90, unaffected). As such, the initial hit rate increased by a factor 300, as less, but more specific chemistry could be screened, based on the upfront computed activity predictions.

  2. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  3. Morphological similarities between DBM and a microeconomic model of sprawl

    Science.gov (United States)

    Caruso, Geoffrey; Vuidel, Gilles; Cavailhès, Jean; Frankhauser, Pierre; Peeters, Dominique; Thomas, Isabelle

    2011-03-01

    We present a model that simulates the growth of a metropolitan area on a 2D lattice. The model is dynamic and based on microeconomics. Households show preferences for nearby open spaces and neighbourhood density. They compete on the land market. They travel along a road network to access the CBD. A planner ensures the connectedness and maintenance of the road network. The spatial pattern of houses, green spaces and road network self-organises, emerging from agents individualistic decisions. We perform several simulations and vary residential preferences. Our results show morphologies and transition phases that are similar to Dieletric Breakdown Models (DBM). Such similarities were observed earlier by other authors, but we show here that it can be deducted from the functioning of the land market and thus explicitly connected to urban economic theory.

  4. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  5. Numerical study of similarity in prototype and model pumped turbines

    International Nuclear Information System (INIS)

    Li, Z J; Wang, Z W; Bi, H L

    2014-01-01

    Similarity study of prototype and model pumped turbines are performed by numerical simulation and the partial discharge case is analysed in detail. It is found out that in the RSI (rotor-stator interaction) region where the flow is convectively accelerated with minor flow separation, a high level of similarity in flow patterns and pressure fluctuation appear with relative pressure fluctuation amplitude of model turbine slightly higher than that of prototype turbine. As for the condition in the runner where the flow is convectively accelerated with severe separation, similarity fades substantially due to different topology of flow separation and vortex formation brought by distinctive Reynolds numbers of the two turbines. In the draft tube where the flow is diffusively decelerated, similarity becomes debilitated owing to different vortex rope formation impacted by Reynolds number. It is noted that the pressure fluctuation amplitude and characteristic frequency of model turbine are larger than those of prototype turbine. The differences in pressure fluctuation characteristics are discussed theoretically through dimensionless Navier-Stokes equation. The above conclusions are all made based on simulation without regard to the penstock response and resonance

  6. Spatiao – Temporal Evaluation and Comparison of MM5 Model using Similarity Algorithm

    Directory of Open Access Journals (Sweden)

    N. Siabi

    2016-02-01

    Full Text Available Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by numerical prediction models over time and in different locations and can be provided as spatial zoning maps with interpolation methods such as geostatistics (16, 6. But these maps are comparable to each other as visual, qualitative and univariate for a limited number of maps (15. To resolve this problem the similarity algorithm is used. This algorithm is a simultaneous comparison method to a large number of data (18. Numerical prediction models such as MM5 were used in different studies (10, 22, and 23. But a little research is done to compare the spatio-temporal similarity of the models with real data quantitatively. The purpose of this paper is to integrate geostatistical techniques with similarity algorithm to study the spatial and temporal MM5 model predicted results with real data. Materials and Methods The study area is north east of Iran. 55 to 61 degrees of longitude and latitude is 30 to 38 degrees. Monthly and annual temperature and precipitation actual data for the period of 1990-2010 was received from the Meteorological Agency and Department of Energy. MM5 Model Data, with a spatial resolution 0.5 × 0.5 degree were downloaded from the NASA website (5. GS+ and ArcGis software were used to produce each variable map. We used multivariate methods co-kriging and kriging with an external drift by applying topography and height as a secondary variable via implementing Digital Elevation Model. (6,12,14. Then the standardize and similarity algorithms (9,11 was applied by programming in MATLAB software to each map grid point. The spatial and temporal similarities between data collections and model results were obtained by F values. These values are between 0 and 0.5 where the value below 0.2 indicates good similarity and above 0.5 shows very poor similarity. The results were plotted on maps by MATLAB

  7. APPLICABILITY OF SIMILARITY CONDITIONS TO ANALOGUE MODELLING OF TECTONIC STRUCTURES

    Directory of Open Access Journals (Sweden)

    Mikhail A. Goncharov

    2010-01-01

    Full Text Available The publication is aimed at comparing concepts of V.V. Belousov and M.V. Gzovsky, outstanding researchers who established fundamentals of tectonophysics in Russia, specifically similarity conditions in application to tectonophysical modeling. Quotations from their publications illustrate differences in their views. In this respect, we can reckon V.V. Belousov as a «realist» as he supported «the liberal point of view» [Methods of modelling…, 1988, p. 21–22], whereas M.V. Gzovsky can be regarded as an «idealist» as he believed that similarity conditions should be mandatorily applied to ensure correctness of physical modeling of tectonic deformations and structures [Gzovsky, 1975, pp. 88 and 94].Objectives of the present publication are (1 to be another reminder about desirability of compliance with similarity conditions in experimental tectonics; (2 to point out difficulties in ensuring such compliance; (3 to give examples which bring out the fact that similarity conditions are often met per se, i.e. automatically observed; (4 to show that modeling can be simplified in some cases without compromising quantitative estimations of parameters of structure formation.(1 Physical modelling of tectonic deformations and structures should be conducted, if possible, in compliance with conditions of geometric and physical similarity between experimental models and corresponding natural objects. In any case, a researcher should have a clear vision of conditions applicable to each particular experiment.(2 Application of similarity conditions is often challenging due to unavoidable difficulties caused by the following: a Imperfection of experimental equipment and technologies (Fig. 1 to 3; b uncertainties in estimating parameters of formation of natural structures, including main ones: structure size (Fig. 4, time of formation (Fig. 5, deformation properties of the medium wherein such structures are formed, including, first of all, viscosity (Fig. 6

  8. A comprehensive comparison of network similarities for link prediction and spurious link elimination

    Science.gov (United States)

    Zhang, Peng; Qiu, Dan; Zeng, An; Xiao, Jinghua

    2018-06-01

    Identifying missing interactions in complex networks, known as link prediction, is realized by estimating the likelihood of the existence of a link between two nodes according to the observed links and nodes' attributes. Similar approaches have also been employed to identify and remove spurious links in networks which is crucial for improving the reliability of network data. In network science, the likelihood for two nodes having a connection strongly depends on their structural similarity. The key to address these two problems thus becomes how to objectively measure the similarity between nodes in networks. In the literature, numerous network similarity metrics have been proposed and their accuracy has been discussed independently in previous works. In this paper, we systematically compare the accuracy of 18 similarity metrics in both link prediction and spurious link elimination when the observed networks are very sparse or consist of inaccurate linking information. Interestingly, some methods have high prediction accuracy, they tend to perform low accuracy in identification spurious interaction. We further find that methods can be classified into several cluster according to their behaviors. This work is useful for guiding future use of these similarity metrics for different purposes.

  9. MAC/FAC: A Model of Similarity-Based Retrieval

    Science.gov (United States)

    1994-10-01

    Grapes (0.28) 327 Sour Grapes, analog The Taming of the Shrew (0.22), Merry Wives 251 (0.18), S[11 stories], Sour Grapes (-0.19) Sour Grapes, literal... The Institute for the 0 1 Learning Sciences Northwestern University CD• 00 MAC/FAC: A MODEL OF SIMILARITY-BASED RETRIEVAL Kenneth D. Forbus Dedre...Gentner Keith Law Technical Report #59 • October 1994 94-35188 wit Establisthed in 1989 with the support of Andersen Consulting Form Approved REPORT

  10. Quality of courses evaluated by 'predictions' rather than opinions: Fewer respondents needed for similar results.

    Science.gov (United States)

    Cohen-Schotanus, Janke; Schönrock-Adema, Johanna; Schmidt, Henk G

    2010-01-01

    A well-known problem with student surveys is a too low response rate. Experiences with predicting electoral outcomes, which required much smaller sample sizes, inspired us to adopt a similar approach to course evaluation. We expected that having respondents estimate the average opinions of their peers required fewer respondents for comparable outcomes than giving own opinions. Two course evaluation studies were performed among successive first-year medical students (N = 380 and 450, respectively). Study 1: Half the cohort gave opinions on nine questions, while the other half predicted the average outcomes. A prize was offered for the three best predictions (motivational remedy). Study 2: Half the cohort gave opinions, a quarter made predictions without a prize and a quarter made predictions with previous year's results as prior knowledge (cognitive remedy). The numbers of respondents required for stable outcomes were determined following an iterative process. Differences between numbers of respondents required and between average scores were analysed with ANOVA. In both studies, the prediction conditions required significantly fewer respondents (p < 0.001) for comparable outcomes. The informed prediction condition required the fewest respondents (N < 20). Problems with response rates can be reduced by asking respondents to predict evaluation outcomes rather than giving opinions.

  11. Learning Similar Actions by Reinforcement or Sensory-Prediction Errors Rely on Distinct Physiological Mechanisms.

    Science.gov (United States)

    Uehara, Shintaro; Mawase, Firas; Celnik, Pablo

    2017-09-14

    Humans can acquire knowledge of new motor behavior via different forms of learning. The two forms most commonly studied have been the development of internal models based on sensory-prediction errors (error-based learning) and success-based feedback (reinforcement learning). Human behavioral studies suggest these are distinct learning processes, though the neurophysiological mechanisms that are involved have not been characterized. Here, we evaluated physiological markers from the cerebellum and the primary motor cortex (M1) using noninvasive brain stimulations while healthy participants trained finger-reaching tasks. We manipulated the extent to which subjects rely on error-based or reinforcement by providing either vector or binary feedback about task performance. Our results demonstrated a double dissociation where learning the task mainly via error-based mechanisms leads to cerebellar plasticity modifications but not long-term potentiation (LTP)-like plasticity changes in M1; while learning a similar action via reinforcement mechanisms elicited M1 LTP-like plasticity but not cerebellar plasticity changes. Our findings indicate that learning complex motor behavior is mediated by the interplay of different forms of learning, weighing distinct neural mechanisms in M1 and the cerebellum. Our study provides insights for designing effective interventions to enhance human motor learning. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Birds of a feather sit together: physical similarity predicts seating choice.

    Science.gov (United States)

    Mackinnon, Sean P; Jordan, Christian H; Wilson, Anne E

    2011-07-01

    Across four studies, people sat (or reported they would sit) closer to physically similar others. Study 1 revealed significant aggregation in seating patterns on two easily observed characteristics: glasses wearing and sex. Study 2 replicated this finding with a wider variety of physical traits: race, sex, glasses wearing, hair length, and hair color. The overall tendency for people to sit beside physically similar others remained significant when controlling for sex and race, suggesting people aggregate on physical dimensions other than broad social categories. Study 3 conceptually replicated these results in a laboratory setting. The more physically similar participants were to a confederate, the closer they sat before an anticipated interaction when controlling for sex, race, and attractiveness similarity. In Study 4, overall physical similarity and glasses wearing similarity predicted self-reported seating distance. These effects were mediated by perceived attitudinal similarity. Liking and inferred acceptance also received support as mediators for glasses wearing similarity. © 2011 by the Society for Personality and Social Psychology, Inc

  13. Predicting human age using regional morphometry and inter-regional morphological similarity

    Science.gov (United States)

    Wang, Xun-Heng; Li, Lihua

    2016-03-01

    The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

  14. Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

    Directory of Open Access Journals (Sweden)

    Kihara Daisuke

    2010-05-01

    Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP

  15. A self-similar magnetohydrodynamic model for ball lightnings

    International Nuclear Information System (INIS)

    Tsui, K. H.

    2006-01-01

    Ball lightning is modeled by magnetohydrodynamic (MHD) equations in two-dimensional spherical geometry with azimuthal symmetry. Dynamic evolutions in the radial direction are described by the self-similar evolution function y(t). The plasma pressure, mass density, and magnetic fields are solved in terms of the radial label η. This model gives spherical MHD plasmoids with axisymmetric force-free magnetic field, and spherically symmetric plasma pressure and mass density, which self-consistently determine the polytropic index γ. The spatially oscillating nature of the radial and meridional field structures indicate embedded regions of closed field lines. These regions are named secondary plasmoids, whereas the overall self-similar spherical structure is named the primary plasmoid. According to this model, the time evolution function allows the primary plasmoid expand outward in two modes. The corresponding ejection of the embedded secondary plasmoids results in ball lightning offering an answer as how they come into being. The first is an accelerated expanding mode. This mode appears to fit plasmoids ejected from thundercloud tops with acceleration to ionosphere seen in high altitude atmospheric observations of sprites and blue jets. It also appears to account for midair high-speed ball lightning overtaking airplanes, and ground level high-speed energetic ball lightning. The second is a decelerated expanding mode, and it appears to be compatible to slowly moving ball lightning seen near ground level. The inverse of this second mode corresponds to an accelerated inward collapse, which could bring ball lightning to an end sometimes with a cracking sound

  16. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  17. Towards Modelling Variation in Music as Foundation for Similarity

    NARCIS (Netherlands)

    Volk, A.; de Haas, W.B.; van Kranenburg, P.; Cambouropoulos, E.; Tsougras, C.; Mavromatis, P.; Pastiadis, K.

    2012-01-01

    This paper investigates the concept of variation in music from the perspective of music similarity. Music similarity is a central concept in Music Information Retrieval (MIR), however there exists no comprehensive approach to music similarity yet. As a consequence, MIR faces the challenge on how to

  18. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  19. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  20. Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

    Directory of Open Access Journals (Sweden)

    Iris I A Groen

    Full Text Available The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis. Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

  1. Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories

    Science.gov (United States)

    Groen, Iris I. A.; Ghebreab, Sennay; Lamme, Victor A. F.; Scholte, H. Steven

    2012-01-01

    The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task. PMID:23093921

  2. Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

    Directory of Open Access Journals (Sweden)

    David A Bridwell

    Full Text Available Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33% and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%, with accuracies ranging from 52.9% (Silver Linings Playbook to 11.75% (Seinfeld within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

  3. Cortical Response Similarities Predict which Audiovisual Clips Individuals Viewed, but Are Unrelated to Clip Preference.

    Science.gov (United States)

    Bridwell, David A; Roth, Cullen; Gupta, Cota Navin; Calhoun, Vince D

    2015-01-01

    Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

  4. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    African Journals Online (AJOL)

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  5. Model predictive control classical, robust and stochastic

    CERN Document Server

    Kouvaritakis, Basil

    2016-01-01

    For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...

  6. QSAR models based on quantum topological molecular similarity.

    Science.gov (United States)

    Popelier, P L A; Smith, P J

    2006-07-01

    A new method called quantum topological molecular similarity (QTMS) was fairly recently proposed [J. Chem. Inf. Comp. Sc., 41, 2001, 764] to construct a variety of medicinal, ecological and physical organic QSAR/QSPRs. QTMS method uses quantum chemical topology (QCT) to define electronic descriptors drawn from modern ab initio wave functions of geometry-optimised molecules. It was shown that the current abundance of computing power can be utilised to inject realistic descriptors into QSAR/QSPRs. In this article we study seven datasets of medicinal interest : the dissociation constants (pK(a)) for a set of substituted imidazolines , the pK(a) of imidazoles , the ability of a set of indole derivatives to displace [(3)H] flunitrazepam from binding to bovine cortical membranes , the influenza inhibition constants for a set of benzimidazoles , the interaction constants for a set of amides and the enzyme liver alcohol dehydrogenase , the natriuretic activity of sulphonamide carbonic anhydrase inhibitors and the toxicity of a series of benzyl alcohols. A partial least square analysis in conjunction with a genetic algorithm delivered excellent models. They are also able to highlight the active site, of the ligand or the molecule whose structure determines the activity. The advantages and limitations of QTMS are discussed.

  7. Similarity-Based Prediction of Travel Times for Vehicles Traveling on Known Routes

    DEFF Research Database (Denmark)

    Tiesyte, Dalia; Jensen, Christian Søndergaard

    2008-01-01

    , historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory......The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular...... of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable...

  8. Environmental niche models for riverine desert fishes and their similarity according to phylogeny and functionality

    Science.gov (United States)

    Whitney, James E.; Whittier, Joanna B.; Paukert, Craig P.

    2017-01-01

    Environmental filtering and competitive exclusion are hypotheses frequently invoked in explaining species' environmental niches (i.e., geographic distributions). A key assumption in both hypotheses is that the functional niche (i.e., species traits) governs the environmental niche, but few studies have rigorously evaluated this assumption. Furthermore, phylogeny could be associated with these hypotheses if it is predictive of functional niche similarity via phylogenetic signal or convergent evolution, or of environmental niche similarity through phylogenetic attraction or repulsion. The objectives of this study were to investigate relationships between environmental niches, functional niches, and phylogenies of fishes of the Upper (UCRB) and Lower (LCRB) Colorado River Basins of southwestern North America. We predicted that functionally similar species would have similar environmental niches (i.e., environmental filtering) and that closely related species would be functionally similar (i.e., phylogenetic signal) and possess similar environmental niches (i.e., phylogenetic attraction). Environmental niches were quantified using environmental niche modeling, and functional similarity was determined using functional trait data. Nonnatives in the UCRB provided the only support for environmental filtering, which resulted from several warmwater nonnatives having dam number as a common predictor of their distributions, whereas several cool- and coldwater nonnatives shared mean annual air temperature as an important distributional predictor. Phylogenetic signal was supported for both natives and nonnatives in both basins. Lastly, phylogenetic attraction was only supported for native fishes in the LCRB and for nonnative fishes in the UCRB. Our results indicated that functional similarity was heavily influenced by evolutionary history, but that phylogenetic relationships and functional traits may not always predict the environmental distribution of species. However, the

  9. Visual reconciliation of alternative similarity spaces in climate modeling

    Science.gov (United States)

    J Poco; A Dasgupta; Y Wei; William Hargrove; C.R. Schwalm; D.N. Huntzinger; R Cook; E Bertini; C.T. Silva

    2015-01-01

    Visual data analysis often requires grouping of data objects based on their similarity. In many application domains researchers use algorithms and techniques like clustering and multidimensional scaling to extract groupings from data. While extracting these groups using a single similarity criteria is relatively straightforward, comparing alternative criteria poses...

  10. Self-similar solution for coupled thermal electromagnetic model ...

    African Journals Online (AJOL)

    An investigation into the existence and uniqueness solution of self-similar solution for the coupled Maxwell and Pennes Bio-heat equations have been done. Criteria for existence and uniqueness of self-similar solution are revealed in the consequent theorems. Journal of the Nigerian Association of Mathematical Physics ...

  11. A Model-Based Approach to Constructing Music Similarity Functions

    Science.gov (United States)

    West, Kris; Lamere, Paul

    2006-12-01

    Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.

  12. Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

    Science.gov (United States)

    Niknazar, Hamid; Nasrabadi, Ali Motie

    Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

  13. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  14. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

    Directory of Open Access Journals (Sweden)

    Gomez Shawn M

    2010-04-01

    Full Text Available Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

  15. Similar predictions of etravirine sensitivity regardless of genotypic testing method used: comparison of available scoring systems.

    Science.gov (United States)

    Vingerhoets, Johan; Nijs, Steven; Tambuyzer, Lotke; Hoogstoel, Annemie; Anderson, David; Picchio, Gaston

    2012-01-01

    The aims of this study were to compare various genotypic scoring systems commonly used to predict virological outcome to etravirine, and examine their concordance with etravirine phenotypic susceptibility. Six etravirine genotypic scoring systems were assessed: Tibotec 2010 (based on 20 mutations; TBT 20), Monogram, Stanford HIVdb, ANRS, Rega (based on 37, 30, 27 and 49 mutations, respectively) and virco(®)TYPE HIV-1 (predicted fold change based on genotype). Samples from treatment-experienced patients who participated in the DUET trials and with both genotypic and phenotypic data (n=403) were assessed using each scoring system. Results were retrospectively correlated with virological response in DUET. κ coefficients were calculated to estimate the degree of correlation between the different scoring systems. Correlation between the five scoring systems and the TBT 20 system was approximately 90%. Virological response by etravirine susceptibility was comparable regardless of which scoring system was utilized, with 70-74% of DUET patients determined as susceptible to etravirine by the different scoring systems achieving plasma viral load <50 HIV-1 RNA copies/ml. In samples classed as phenotypically susceptible to etravirine (fold change in 50% effective concentration ≤3), correlations with genotypic score were consistently high across scoring systems (≥70%). In general, the etravirine genotypic scoring systems produced similar results, and genotype-phenotype concordance was high. As such, phenotypic interpretations, and in their absence all genotypic scoring systems investigated, may be used to reliably predict the activity of etravirine.

  16. A Model-Based Approach to Constructing Music Similarity Functions

    Directory of Open Access Journals (Sweden)

    Lamere Paul

    2007-01-01

    Full Text Available Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.

  17. Quality assessment of protein model-structures based on structural and functional similarities.

    Science.gov (United States)

    Konopka, Bogumil M; Nebel, Jean-Christophe; Kotulska, Malgorzata

    2012-09-21

    Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and

  18. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  19. Modeling Recognition Memory Using the Similarity Structure of Natural Input

    Science.gov (United States)

    Lacroix, Joyca P. W.; Murre, Jaap M. J.; Postma, Eric O.; van den Herik, H. Jaap

    2006-01-01

    The natural input memory (NAM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed…

  20. LDA-Based Unified Topic Modeling for Similar TV User Grouping and TV Program Recommendation.

    Science.gov (United States)

    Pyo, Shinjee; Kim, Eunhui; Kim, Munchurl

    2015-08-01

    Social TV is a social media service via TV and social networks through which TV users exchange their experiences about TV programs that they are viewing. For social TV service, two technical aspects are envisioned: grouping of similar TV users to create social TV communities and recommending TV programs based on group and personal interests for personalizing TV. In this paper, we propose a unified topic model based on grouping of similar TV users and recommending TV programs as a social TV service. The proposed unified topic model employs two latent Dirichlet allocation (LDA) models. One is a topic model of TV users, and the other is a topic model of the description words for viewed TV programs. The two LDA models are then integrated via a topic proportion parameter for TV programs, which enforces the grouping of similar TV users and associated description words for watched TV programs at the same time in a unified topic modeling framework. The unified model identifies the semantic relation between TV user groups and TV program description word groups so that more meaningful TV program recommendations can be made. The unified topic model also overcomes an item ramp-up problem such that new TV programs can be reliably recommended to TV users. Furthermore, from the topic model of TV users, TV users with similar tastes can be grouped as topics, which can then be recommended as social TV communities. To verify our proposed method of unified topic-modeling-based TV user grouping and TV program recommendation for social TV services, in our experiments, we used real TV viewing history data and electronic program guide data from a seven-month period collected by a TV poll agency. The experimental results show that the proposed unified topic model yields an average 81.4% precision for 50 topics in TV program recommendation and its performance is an average of 6.5% higher than that of the topic model of TV users only. For TV user prediction with new TV programs, the average

  1. Analysis and Modeling of Time-Correlated Characteristics of Rainfall-Runoff Similarity in the Upstream Red River Basin

    Directory of Open Access Journals (Sweden)

    Xiuli Sang

    2012-01-01

    Full Text Available We constructed a similarity model (based on Euclidean distance between rainfall and runoff to study time-correlated characteristics of rainfall-runoff similar patterns in the upstream Red River Basin and presented a detailed evaluation of the time correlation of rainfall-runoff similarity. The rainfall-runoff similarity was used to determine the optimum similarity. The results showed that a time-correlated model was found to be capable of predicting the rainfall-runoff similarity in the upstream Red River Basin in a satisfactory way. Both noised and denoised time series by thresholding the wavelet coefficients were applied to verify the accuracy of model. And the corresponding optimum similar sets obtained as the equation solution conditions showed an interesting and stable trend. On the whole, the annual mean similarity presented a gradually rising trend, for quantitatively estimating comprehensive influence of climate change and of human activities on rainfall-runoff similarity.

  2. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  4. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  5. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  6. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

  7. Modeling recognition memory using the similarity structure of natural input

    NARCIS (Netherlands)

    Lacroix, J.P.W.; Murre, J.M.J.; Postma, E.O.; van den Herik, H.J.

    2006-01-01

    The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During

  8. Approximate self-similarity in models of geological folding

    NARCIS (Netherlands)

    Budd, C.J.; Peletier, M.A.

    2000-01-01

    We propose a model for the folding of rock under the compression of tectonic plates. This models an elastic rock layer imbedded in a viscous foundation by a fourth-order parabolic equation with a nonlinear constraint. The large-time behavior of solutions of this problem is examined and found to be

  9. Similar patterns of neural activity predict memory function during encoding and retrieval.

    Science.gov (United States)

    Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

    2017-07-15

    Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Similarity solutions for systems arising from an Aedes aegypti model

    Science.gov (United States)

    Freire, Igor Leite; Torrisi, Mariano

    2014-04-01

    In a recent paper a new model for the Aedes aegypti mosquito dispersal dynamics was proposed and its Lie point symmetries were investigated. According to the carried group classification, the maximal symmetry Lie algebra of the nonlinear cases is reached whenever the advection term vanishes. In this work we analyze the family of systems obtained when the wind effects on the proposed model are neglected. Wide new classes of solutions to the systems under consideration are obtained.

  11. Similarity analysis and prediction for data of structural acoustic and vibration

    International Nuclear Information System (INIS)

    Mei Liquan; Ding Xuemei; Zhang Shujuan

    2010-01-01

    Support vector machine (SVM) is a learning machine based on statistical learning theory, which can get a model having good generalization. It can solve 'learning more' when dealing with small size. It can also avoid 'dimensional disaster' when solving nonlinear problems. This paper works on the parameters optimization for support vector regression machine (SVRM) and its applications. Solution path algorithm can save much CPU time when it is employed to optimize the regularization parameter of SVRM. Simulated annealing algorithm has good ability of finding global optimal solution. An improved solution path algorithm and simulated annealing algorithm are combined to optimize parameters of SVRM in the regression analysis of the acoustic and vibration data for complex practical problems. The numerical results show the model has good predictive capability. (authors)

  12. Mental models accurately predict emotion transitions.

    Science.gov (United States)

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  13. Mental models accurately predict emotion transitions

    Science.gov (United States)

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  14. Similarities between obesity in pets and children: the addiction model.

    Science.gov (United States)

    Pretlow, Robert A; Corbee, Ronald J

    2016-09-01

    Obesity in pets is a frustrating, major health problem. Obesity in human children is similar. Prevailing theories accounting for the rising obesity rates - for example, poor nutrition and sedentary activity - are being challenged. Obesity interventions in both pets and children have produced modest short-term but poor long-term results. New strategies are needed. A novel theory posits that obesity in pets and children is due to 'treats' and excessive meal amounts given by the 'pet-parent' and child-parent to obtain affection from the pet/child, which enables 'eating addiction' in the pet/child and results in parental 'co-dependence'. Pet-parents and child-parents may even become hostage to the treats/food to avoid the ire of the pet/child. Eating addiction in the pet/child also may be brought about by emotional factors such as stress, independent of parental co-dependence. An applicable treatment for child obesity has been trialled using classic addiction withdrawal/abstinence techniques, as well as behavioural addiction methods, with significant results. Both the child and the parent progress through withdrawal from specific 'problem foods', next from snacking (non-specific foods) and finally from excessive portions at meals (gradual reductions). This approach should adapt well for pets and pet-parents. Pet obesity is more 'pure' than child obesity, in that contributing factors and treatment points are essentially under the control of the pet-parent. Pet obesity might thus serve as an ideal test bed for the treatment and prevention of child obesity, with focus primarily on parental behaviours. Sharing information between the fields of pet and child obesity would be mutually beneficial.

  15. Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

    Directory of Open Access Journals (Sweden)

    Geoff Boeing

    2016-11-01

    Full Text Available Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.

  16. Predicting behavior change from persuasive messages using neural representational similarity and social network analyses.

    Science.gov (United States)

    Pegors, Teresa K; Tompson, Steven; O'Donnell, Matthew Brook; Falk, Emily B

    2017-08-15

    Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

    Science.gov (United States)

    Chen, Lei; Zeng, Wei-Ming; Cai, Yu-Dong; Feng, Kai-Yan; Chou, Kuo-Chen

    2012-01-01

    The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels. PMID:22514724

  18. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico

    2009-01-01

    The model quality assessment problem consists in the a priori estimation of the overall and per-residue accuracy of protein structure predictions. Over the past years, a number of methods have been developed to address this issue and CASP established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic servers. Estimates could apply to both whole models and individual amino acids. Groups involved in the tertiary structure prediction categories were also asked to assign local error estimates to each predicted residue in their own models and their results are also discussed here. The correlation between the predicted and observed correctness measures was the basis of the assessment of the results. We observe that consensus-based methods still perform significantly better than those accepting single models, similarly to what was concluded in the previous edition of the experiment. © 2009 WILEY-LISS, INC.

  19. How glitter relates to gold : Similarity-dependent reward prediction errors in the human striatum

    NARCIS (Netherlands)

    Kahnt, T.; Park, S.Q.; Burke, C.; Tobler, P.N.

    2012-01-01

    Optimal choices benefit from previous learning. However, it is not clear how previously learned stimuli influence behavior to novel but similar stimuli. One possibility is to generalize based on the similarity between learned and current stimuli. Here, we use neuroscientific methods and a novel

  20. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  1. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM

    Directory of Open Access Journals (Sweden)

    Yunyun Liang

    2015-01-01

    Full Text Available Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM. Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS, segmented PsePSSM, and segmented autocovariance transformation (ACT based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640 are adopted in this paper. Then a 700-dimensional (700D feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA. To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

  2. Model-observer similarity, error modeling and social learning in rhesus macaques.

    Directory of Open Access Journals (Sweden)

    Elisabetta Monfardini

    Full Text Available Monkeys readily learn to discriminate between rewarded and unrewarded items or actions by observing their conspecifics. However, they do not systematically learn from humans. Understanding what makes human-to-monkey transmission of knowledge work or fail could help identify mediators and moderators of social learning that operate regardless of language or culture, and transcend inter-species differences. Do monkeys fail to learn when human models show a behavior too dissimilar from the animals' own, or when they show a faultless performance devoid of error? To address this question, six rhesus macaques trained to find which object within a pair concealed a food reward were successively tested with three models: a familiar conspecific, a 'stimulus-enhancing' human actively drawing the animal's attention to one object of the pair without actually performing the task, and a 'monkey-like' human performing the task in the same way as the monkey model did. Reward was manipulated to ensure that all models showed equal proportions of errors and successes. The 'monkey-like' human model improved the animals' subsequent object discrimination learning as much as a conspecific did, whereas the 'stimulus-enhancing' human model tended on the contrary to retard learning. Modeling errors rather than successes optimized learning from the monkey and 'monkey-like' models, while exacerbating the adverse effect of the 'stimulus-enhancing' model. These findings identify error modeling as a moderator of social learning in monkeys that amplifies the models' influence, whether beneficial or detrimental. By contrast, model-observer similarity in behavior emerged as a mediator of social learning, that is, a prerequisite for a model to work in the first place. The latter finding suggests that, as preverbal infants, macaques need to perceive the model as 'like-me' and that, once this condition is fulfilled, any agent can become an effective model.

  3. Model predictive control using fuzzy decision functions

    NARCIS (Netherlands)

    Kaymak, U.; Costa Sousa, da J.M.

    2001-01-01

    Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

  4. A study on the quantitative model of human response time using the amount and the similarity of information

    International Nuclear Information System (INIS)

    Lee, Sung Jin

    2006-02-01

    The mental capacity to retain or recall information, or memory is related to human performance during processing of information. Although a large number of studies have been carried out on human performance, little is known about the similarity effect. The purpose of this study was to propose and validate a quantitative and predictive model on human response time in the user interface with the basic concepts of information amount, similarity and degree of practice. It was difficult to explain human performance by only similarity or information amount. There were two difficulties: constructing a quantitative model on human response time and validating the proposed model by experimental work. A quantitative model based on the Hick's law, the law of practice and similarity theory was developed. The model was validated under various experimental conditions by measuring the participants' response time in the environment of a computer-based display. Human performance was improved by degree of similarity and practice in the user interface. Also we found the age-related human performance which was degraded as he or she was more elder. The proposed model may be useful for training operators who will handle some interfaces and predicting human performance by changing system design

  5. Testing the predictive power of nuclear mass models

    International Nuclear Information System (INIS)

    Mendoza-Temis, J.; Morales, I.; Barea, J.; Frank, A.; Hirsch, J.G.; Vieyra, J.C. Lopez; Van Isacker, P.; Velazquez, V.

    2008-01-01

    A number of tests are introduced which probe the ability of nuclear mass models to extrapolate. Three models are analyzed in detail: the liquid drop model, the liquid drop model plus empirical shell corrections and the Duflo-Zuker mass formula. If predicted nuclei are close to the fitted ones, average errors in predicted and fitted masses are similar. However, the challenge of predicting nuclear masses in a region stabilized by shell effects (e.g., the lead region) is far more difficult. The Duflo-Zuker mass formula emerges as a powerful predictive tool

  6. Stereotype content model across cultures: Towards universal similarities and some differences

    Science.gov (United States)

    Cuddy, Amy J. C.; Fiske, Susan T.; Kwan, Virginia S. Y.; Glick, Peter; Demoulin, Stéphanie; Leyens, Jacques-Philippe; Bond, Michael Harris; Croizet, Jean-Claude; Ellemers, Naomi; Sleebos, Ed; Htun, Tin Tin; Kim, Hyun-Jeong; Maio, Greg; Perry, Judi; Petkova, Kristina; Todorov, Valery; Rodríguez-Bailón, Rosa; Morales, Elena; Moya, Miguel; Palacios, Marisol; Smith, Vanessa; Perez, Rolando; Vala, Jorge; Ziegler, Rene

    2014-01-01

    The stereotype content model (SCM) proposes potentially universal principles of societal stereotypes and their relation to social structure. Here, the SCM reveals theoretically grounded, cross-cultural, cross-groups similarities and one difference across 10 non-US nations. Seven European (individualist) and three East Asian (collectivist) nations (N = 1, 028) support three hypothesized cross-cultural similarities: (a) perceived warmth and competence reliably differentiate societal group stereotypes; (b) many out-groups receive ambivalent stereotypes (high on one dimension; low on the other); and (c) high status groups stereotypically are competent, whereas competitive groups stereotypically lack warmth. Data uncover one consequential cross-cultural difference: (d) the more collectivist cultures do not locate reference groups (in-groups and societal prototype groups) in the most positive cluster (high-competence/high-warmth), unlike individualist cultures. This demonstrates out-group derogation without obvious reference-group favouritism. The SCM can serve as a pancultural tool for predicting group stereotypes from structural relations with other groups in society, and comparing across societies. PMID:19178758

  7. Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity Network

    Directory of Open Access Journals (Sweden)

    Hailin Chen

    2013-01-01

    Full Text Available Increasing evidence has revealed that microRNAs (miRNAs play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.

  8. Similarity-based search of model organism, disease and drug effect phenotypes

    KAUST Repository

    Hoehndorf, Robert; Gruenberger, Michael; Gkoutos, Georgios V; Schofield, Paul N

    2015-01-01

    Background: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions

  9. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

    Science.gov (United States)

    Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. A model to predict the power output from wind farms

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Riso National Lab., Roskilde (Denmark)

    1997-12-31

    This paper will describe a model that can predict the power output from wind farms. To give examples of input the model is applied to a wind farm in Texas. The predictions are generated from forecasts from the NGM model of NCEP. These predictions are made valid at individual sites (wind farms) by applying a matrix calculated by the sub-models of WASP (Wind Atlas Application and Analysis Program). The actual wind farm production is calculated using the Riso PARK model. Because of the preliminary nature of the results, they will not be given. However, similar results from Europe will be given.

  11. Predictive Capability Maturity Model for computational modeling and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.

    2007-10-01

    The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.

  12. Embedding Term Similarity and Inverse Document Frequency into a Logical Model of Information Retrieval.

    Science.gov (United States)

    Losada, David E.; Barreiro, Alvaro

    2003-01-01

    Proposes an approach to incorporate term similarity and inverse document frequency into a logical model of information retrieval. Highlights include document representation and matching; incorporating term similarity into the measure of distance; new algorithms for implementation; inverse document frequency; and logical versus classical models of…

  13. Scanpath Based N-Gram Models for Predicting Reading Behavior

    DEFF Research Database (Denmark)

    Mishra, Abhijit; Bhattacharyya, Pushpak; Carl, Michael

    2013-01-01

    Predicting reading behavior is a difficult task. Reading behavior depends on various linguistic factors (e.g. sentence length, structural complexity etc.) and other factors (e.g individual's reading style, age etc.). Ideally, a reading model should be similar to a language model where the model i...

  14. Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

    Science.gov (United States)

    Kumar, Sujay V.; Wang, Shugong; Mocko, David M.; Peters-Lidard, Christa D.; Xia, Youlong

    2017-11-01

    Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

  15. Predicting the start and maximum amplitude of solar cycle 24 using similar phases and a cycle grouping

    International Nuclear Information System (INIS)

    Wang Jialong; Zong Weiguo; Le Guiming; Zhao Haijuan; Tang Yunqiu; Zhang Yang

    2009-01-01

    We find that the solar cycles 9, 11, and 20 are similar to cycle 23 in their respective descending phases. Using this similarity and the observed data of smoothed monthly mean sunspot numbers (SMSNs) available for the descending phase of cycle 23, we make a date calibration for the average time sequence made of the three descending phases of the three cycles, and predict the start of March or April 2008 for cycle 24. For the three cycles, we also find a linear correlation of the length of the descending phase of a cycle with the difference between the maximum epoch of this cycle and that of its next cycle. Using this relationship along with the known relationship between the rise-time and the maximum amplitude of a slowly rising solar cycle, we predict the maximum SMSN of cycle 24 of 100.2 ± 7.5 to appear during the period from May to October 2012. (letters)

  16. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  17. A new k-epsilon model consistent with Monin-Obukhov similarity theory

    DEFF Research Database (Denmark)

    van der Laan, Paul; Kelly, Mark C.; Sørensen, Niels N.

    2017-01-01

    A new k-" model is introduced that is consistent with Monin–Obukhov similarity theory (MOST). The proposed k-" model is compared with another k-" model that was developed in an attempt to maintain inlet profiles compatible with MOST. It is shown that the previous k-" model is not consistent with ...

  18. PubMed-supported clinical term weighting approach for improving inter-patient similarity measure in diagnosis prediction.

    Science.gov (United States)

    Chan, Lawrence Wc; Liu, Ying; Chan, Tao; Law, Helen Kw; Wong, S C Cesar; Yeung, Andy Ph; Lo, K F; Yeung, S W; Kwok, K Y; Chan, William Yl; Lau, Thomas Yh; Shyu, Chi-Ren

    2015-06-02

    Similarity-based retrieval of Electronic Health Records (EHRs) from large clinical information systems provides physicians the evidence support in making diagnoses or referring examinations for the suspected cases. Clinical Terms in EHRs represent high-level conceptual information and the similarity measure established based on these terms reflects the chance of inter-patient disease co-occurrence. The assumption that clinical terms are equally relevant to a disease is unrealistic, reducing the prediction accuracy. Here we propose a term weighting approach supported by PubMed search engine to address this issue. We collected and studied 112 abdominal computed tomography imaging examination reports from four hospitals in Hong Kong. Clinical terms, which are the image findings related to hepatocellular carcinoma (HCC), were extracted from the reports. Through two systematic PubMed search methods, the generic and specific term weightings were established by estimating the conditional probabilities of clinical terms given HCC. Each report was characterized by an ontological feature vector and there were totally 6216 vector pairs. We optimized the modified direction cosine (mDC) with respect to a regularization constant embedded into the feature vector. Equal, generic and specific term weighting approaches were applied to measure the similarity of each pair and their performances for predicting inter-patient co-occurrence of HCC diagnoses were compared by using Receiver Operating Characteristics (ROC) analysis. The Areas under the curves (AUROCs) of similarity scores based on equal, generic and specific term weighting approaches were 0.735, 0.728 and 0.743 respectively (p PubMed. Our findings suggest that the optimized similarity measure with specific term weighting to EHRs can improve significantly the accuracy for predicting the inter-patient co-occurrence of diagnosis when compared with equal and generic term weighting approaches.

  19. Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

    NARCIS (Netherlands)

    Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.

    2014-01-01

    In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for

  20. Filling Predictable and Unpredictable Gaps, with and without Similarity-Based Interference: Evidence for LIFG Effects of Dependency Processing.

    Science.gov (United States)

    Leiken, Kimberly; McElree, Brian; Pylkkänen, Liina

    2015-01-01

    One of the most replicated findings in neurolinguistic literature on syntax is the increase of hemodynamic activity in the left inferior frontal gyrus (LIFG) in response to object relative (OR) clauses compared to subject relative clauses. However, behavioral studies have shown that ORs are primarily only costly when similarity-based interference is involved and recently, Leiken and Pylkkänen (2014) showed with magnetoencephalography (MEG) that an LIFG increase at an OR gap is also dependent on such interference. However, since ORs always involve a cue indicating an upcoming dependency formation, OR dependencies could be processed already prior to the gap-site and thus show no sheer dependency effects at the gap itself. To investigate the role of gap predictability in LIFG dependency effects, this MEG study compared ORs to verb phrase ellipsis (VPE), which was used as an example of a non-predictable dependency. Additionally, we explored LIFG sensitivity to filler-gap order by including right node raising structures, in which the order of filler and gap is reverse to that of ORs and VPE. Half of the stimuli invoked similarity-based interference and half did not. Our results demonstrate that LIFG effects of dependency can be elicited regardless of whether the dependency is predictable, the stimulus materials evoke similarity-based interference, or the filler precedes the gap. Thus, contrary to our own prior data, the current findings suggest a highly general role for the LIFG in dependency interpretation that is not limited to environments involving similarity-based interference. Additionally, the millisecond time-resolution of MEG allowed for a detailed characterization of the temporal profiles of LIFG dependency effects across our three constructions, revealing that the timing of these effects is somewhat construction-specific.

  1. Iowa calibration of MEPDG performance prediction models.

    Science.gov (United States)

    2013-06-01

    This study aims to improve the accuracy of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) pavement : performance predictions for Iowa pavement systems through local calibration of MEPDG prediction models. A total of 130 : representative p...

  2. Using self-similarity compensation for improving inter-layer prediction in scalable 3D holoscopic video coding

    Science.gov (United States)

    Conti, Caroline; Nunes, Paulo; Ducla Soares, Luís.

    2013-09-01

    Holoscopic imaging, also known as integral imaging, has been recently attracting the attention of the research community, as a promising glassless 3D technology due to its ability to create a more realistic depth illusion than the current stereoscopic or multiview solutions. However, in order to gradually introduce this technology into the consumer market and to efficiently deliver 3D holoscopic content to end-users, backward compatibility with legacy displays is essential. Consequently, to enable 3D holoscopic content to be delivered and presented on legacy displays, a display scalable 3D holoscopic coding approach is required. Hence, this paper presents a display scalable architecture for 3D holoscopic video coding with a three-layer approach, where each layer represents a different level of display scalability: Layer 0 - a single 2D view; Layer 1 - 3D stereo or multiview; and Layer 2 - the full 3D holoscopic content. In this context, a prediction method is proposed, which combines inter-layer prediction, aiming to exploit the existing redundancy between the multiview and the 3D holoscopic layers, with self-similarity compensated prediction (previously proposed by the authors for non-scalable 3D holoscopic video coding), aiming to exploit the spatial redundancy inherent to the 3D holoscopic enhancement layer. Experimental results show that the proposed combined prediction can improve significantly the rate-distortion performance of scalable 3D holoscopic video coding with respect to the authors' previously proposed solutions, where only inter-layer or only self-similarity prediction is used.

  3. Propriedades termofísicas de soluções-modelo similares a sucos: parte II Thermophysical properties of model solutions similar to juice: part II

    Directory of Open Access Journals (Sweden)

    Sílvia Cristina Sobottka Rolim de Moura

    2005-09-01

    Full Text Available Propriedades termofísicas, densidade e viscosidade de soluções-modelo similares a sucos foram determinadas experimentalmente. Os resultados foram comparados aos preditos por modelos matemáticos (STATISTICA 6.0 e obtidos da literatura em função da sua composição química. Para definição das soluções-modelo, foi realizado um planejamento estrela, mantendo-se fixa a quanti-dade de ácido (1,5% e variando-se a água (82-98,5%, o carboidrato (0-15% e a gordura (0-1,5%. A densidade foi determinada em picnômetro. A viscosidade foi determinada em viscosímetro Brookfield modelo LVF. A condutividade térmica foi calculada com o conhecimento das propriedades difusividade térmica e calor específico (apresentados na Parte I deste trabalho MOURA [7] e da densidade. Os resultados de cada propriedade foram analisados através de superfícies de respostas. Foram encontrados resultados significativos para as propriedades, mostrando que os modelos encontrados representam as mudanças das propriedades térmicas e físicas dos sucos, com alterações na composição e na temperatura.Thermophysical properties, density and viscosity of model solutions similar to juices were experimentally determined. The results were compared to those predicted by mathematical models (STATISTIC 6.0 and to values mentioned in the literature, according to the chemical composition. A star planning was adopted to define model solutions composition; fixing the acid amount in 1.5% and varying water (82-98.5%, carbohydrate (0-15% and fat (0-1.5%. The density was determined by picnometer. The viscosity was determined by Brookfield LVF model viscosimeter. The thermal conductivity was calculated based on thermal diffusivity and specific heat values (presented at the 1st . Part of this paper - MOURA [7] and density. The results of each property were analyzed by the response surface method. The found results were significant, indicating that the models represent the changes of

  4. Assessing intrinsic and specific vulnerability models ability to indicate groundwater vulnerability to groups of similar pesticides: A comparative study

    Science.gov (United States)

    Douglas, Steven; Dixon, Barnali; Griffin, Dale W.

    2018-01-01

    With continued population growth and increasing use of fresh groundwater resources, protection of this valuable resource is critical. A cost effective means to assess risk of groundwater contamination potential will provide a useful tool to protect these resources. Integrating geospatial methods offers a means to quantify the risk of contaminant potential in cost effective and spatially explicit ways. This research was designed to compare the ability of intrinsic (DRASTIC) and specific (Attenuation Factor; AF) vulnerability models to indicate groundwater vulnerability areas by comparing model results to the presence of pesticides from groundwater sample datasets. A logistic regression was used to assess the relationship between the environmental variables and the presence or absence of pesticides within regions of varying vulnerability. According to the DRASTIC model, more than 20% of the study area is very highly vulnerable. Approximately 30% is very highly vulnerable according to the AF model. When groundwater concentrations of individual pesticides were compared to model predictions, the results were mixed. Model predictability improved when concentrations of the group of similar pesticides were compared to model results. Compared to the DRASTIC model, the AF model more accurately predicts the distribution of the number of contaminated wells within each vulnerability class.

  5. Model complexity control for hydrologic prediction

    NARCIS (Netherlands)

    Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.

    2008-01-01

    A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore

  6. Vortex forcing model for turbulent flow over spanwise-heterogeneous topogrpahies: scaling arguments and similarity solution

    Science.gov (United States)

    Anderson, William; Yang, Jianzhi

    2017-11-01

    Spanwise surface heterogeneity beneath high-Reynolds number, fully-rough wall turbulence is known to induce mean secondary flows in the form of counter-rotating streamwise vortices. The secondary flows are a manifestation of Prandtl's secondary flow of the second kind - driven and sustained by spatial heterogeneity of components of the turbulent (Reynolds averaged) stress tensor. The spacing between adjacent surface heterogeneities serves as a control on the spatial extent of the counter-rotating cells, while their intensity is controlled by the spanwise gradient in imposed drag (where larger gradients associated with more dramatic transitions in roughness induce stronger cells). In this work, we have performed an order of magnitude analysis of the mean (Reynolds averaged) streamwise vorticity transport equation, revealing the scaling dependence of circulation upon spanwise spacing. The scaling arguments are supported by simulation data. Then, we demonstrate that mean streamwise velocity can be predicted a priori via a similarity solution to the mean streamwise vorticity transport equation. A vortex forcing term was used to represent the affects of spanwise topographic heterogeneity within the flow. Efficacy of the vortex forcing term was established with large-eddy simulation cases, wherein vortex forcing model parameters were altered to capture different values of spanwise spacing.

  7. Expression Pattern Similarities Support the Prediction of Orthologs Retaining Common Functions after Gene Duplication Events1[OPEN

    Science.gov (United States)

    Haberer, Georg; Panda, Arup; Das Laha, Shayani; Ghosh, Tapas Chandra; Schäffner, Anton R.

    2016-01-01

    The identification of functionally equivalent, orthologous genes (functional orthologs) across genomes is necessary for accurate transfer of experimental knowledge from well-characterized organisms to others. This frequently relies on automated, coding sequence-based approaches such as OrthoMCL, Inparanoid, and KOG, which usually work well for one-to-one homologous states. However, this strategy does not reliably work for plants due to the occurrence of extensive gene/genome duplication. Frequently, for one query gene, multiple orthologous genes are predicted in the other genome, and it is not clear a priori from sequence comparison and similarity which one preserves the ancestral function. We have studied 11 organ-dependent and stress-induced gene expression patterns of 286 Arabidopsis lyrata duplicated gene groups and compared them with the respective Arabidopsis (Arabidopsis thaliana) genes to predict putative expressologs and nonexpressologs based on gene expression similarity. Promoter sequence divergence as an additional tool to substantiate functional orthology only partially overlapped with expressolog classification. By cloning eight A. lyrata homologs and complementing them in the respective four Arabidopsis loss-of-function mutants, we experimentally proved that predicted expressologs are indeed functional orthologs, while nonexpressologs or nonfunctionalized orthologs are not. Our study demonstrates that even a small set of gene expression data in addition to sequence homologies are instrumental in the assignment of functional orthologs in the presence of multiple orthologs. PMID:27303025

  8. Similarity-based search of model organism, disease and drug effect phenotypes

    KAUST Repository

    Hoehndorf, Robert

    2015-02-19

    Background: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results: We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions: Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.

  9. A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD

    Directory of Open Access Journals (Sweden)

    Z. Zhang

    2016-06-01

    Full Text Available This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC. It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.

  10. Propriedades termofísicas de soluções modelo similares a sucos - Parte I Thermophysical properties of model solutions similar to juice - Part I

    Directory of Open Access Journals (Sweden)

    Silvia Cristina Sobottka Rolim de Moura

    2003-04-01

    Full Text Available Propriedades termofísicas, difusividade térmica e calor específico, de soluções modelo similares a sucos, foram determinadas experimentalmente e ajustadas a modelos matemáticos (STATISTICA 6.0, em função da sua composição química. Para definição das soluções modelo foi realizado um planejamento estrela mantendo-se fixa a quantidade de ácido (1,5% e variando-se a água (82-98,5%, o carboidrato (0-15% e a gordura (0-1,5%. A determinação do calor específico foi realizada através do método de Hwang & Hayakawa e a difusividade térmica com base no método de Dickerson. Os resultados de cada propriedade foram analisados através de superfícies de respostas. Foram encontrados resultados significativos para as propriedades, mostrando que os modelos encontrados representam significativamente as mudanças das propriedades térmicas dos sucos, com alterações na composição e na temperatura.Thermophysical properties, thermal diffusivity and specific heat of model solutions similar to juices were experimentally determined and the values obtained compared to those predicted by mathematical models (STATISTIC 6.0 and to values mentioned in the literature, according to the chemical composition. It was adopted a star planning to define the composition of the model solutions fixing the acid amount in 1.5% and varying water (82-98.5%, carboydrate (0-15% and fat (0-1.5%. The specific heat was determined by Hwang & Hayakawa method and the thermal diffusivity was determined by Dickerson method. The results of each property were analysed by the response surface method. The results were significative, indicating that the models represented considerably the changes of thermal properties of juices according to their composition and temperature variations.

  11. Nonlinear chaotic model for predicting storm surges

    Directory of Open Access Journals (Sweden)

    M. Siek

    2010-09-01

    Full Text Available This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.

  12. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  13. Predictive user modeling with actionable attributes

    NARCIS (Netherlands)

    Zliobaite, I.; Pechenizkiy, M.

    2013-01-01

    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target

  14. Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

    Science.gov (United States)

    Radev, Dimitar; Lokshina, Izabella

    2010-11-01

    The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

  15. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  16. Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments

    Science.gov (United States)

    Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke

    2017-01-01

    Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features

  17. Robust predictions of the interacting boson model

    International Nuclear Information System (INIS)

    Casten, R.F.; Koeln Univ.

    1994-01-01

    While most recognized for its symmetries and algebraic structure, the IBA model has other less-well-known but equally intrinsic properties which give unavoidable, parameter-free predictions. These predictions concern central aspects of low-energy nuclear collective structure. This paper outlines these ''robust'' predictions and compares them with the data

  18. Comparison of Prediction-Error-Modelling Criteria

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Jørgensen, Sten Bay

    2007-01-01

    Single and multi-step prediction-error-methods based on the maximum likelihood and least squares criteria are compared. The prediction-error methods studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model, which is a r...

  19. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  20. The prediction of epidemics through mathematical modeling.

    Science.gov (United States)

    Schaus, Catherine

    2014-01-01

    Mathematical models may be resorted to in an endeavor to predict the development of epidemics. The SIR model is one of the applications. Still too approximate, the use of statistics awaits more data in order to come closer to reality.

  1. Calibration of PMIS pavement performance prediction models.

    Science.gov (United States)

    2012-02-01

    Improve the accuracy of TxDOTs existing pavement performance prediction models through calibrating these models using actual field data obtained from the Pavement Management Information System (PMIS). : Ensure logical performance superiority patte...

  2. Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

    Science.gov (United States)

    Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

    2017-12-01

    Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

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

    Science.gov (United States)

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

    2012-01-01

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

  4. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  5. Similarity and accuracy of mental models formed during nursing handovers: A concept mapping approach.

    Science.gov (United States)

    Drach-Zahavy, Anat; Broyer, Chaya; Dagan, Efrat

    2017-09-01

    Shared mental models are crucial for constructing mutual understanding of the patient's condition during a clinical handover. Yet, scant research, if any, has empirically explored mental models of the parties involved in a clinical handover. This study aimed to examine the similarities among mental models of incoming and outgoing nurses, and to test their accuracy by comparing them with mental models of expert nurses. A cross-sectional study, exploring nurses' mental models via the concept mapping technique. 40 clinical handovers. Data were collected via concept mapping of the incoming, outgoing, and expert nurses' mental models (total of 120 concept maps). Similarity and accuracy for concepts and associations indexes were calculated to compare the different maps. About one fifth of the concepts emerged in both outgoing and incoming nurses' concept maps (concept similarity=23%±10.6). Concept accuracy indexes were 35%±18.8 for incoming and 62%±19.6 for outgoing nurses' maps. Although incoming nurses absorbed fewer number of concepts and associations (23% and 12%, respectively), they partially closed the gap (35% and 22%, respectively) relative to expert nurses' maps. The correlations between concept similarities, and incoming as well as outgoing nurses' concept accuracy, were significant (r=0.43, p<0.01; r=0.68 p<0.01, respectively). Finally, in 90% of the maps, outgoing nurses added information concerning the processes enacted during the shift, beyond the expert nurses' gold standard. Two seemingly contradicting processes in the handover were identified. "Information loss", captured by the low similarity indexes among the mental models of incoming and outgoing nurses; and "information restoration", based on accuracy measures indexes among the mental models of the incoming nurses. Based on mental model theory, we propose possible explanations for these processes and derive implications for how to improve a clinical handover. Copyright © 2017 Elsevier Ltd. All

  6. Models for discrete-time self-similar vector processes with application to network traffic

    Science.gov (United States)

    Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh

    2003-07-01

    The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.

  7. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  8. Analysis of metal forming processes by using physical modeling and new plastic similarity condition

    International Nuclear Information System (INIS)

    Gronostajski, Z.; Hawryluk, M.

    2007-01-01

    In recent years many advances have been made in numerical methods, for linear and non-linear problems. However the success of them depends very much on the correctness of the problem formulation and the availability of the input data. Validity of the theoretical results can be verified by an experiment using the real or soft materials. An essential reduction of time and costs of the experiment can be obtained by using soft materials, which behaves in a way analogous to that of real metal during deformation. The advantages of using of the soft materials are closely connected with flow stress 500 to 1000 times lower than real materials. The accuracy of physical modeling depend on the similarity conditions between physical model and real process. The most important similarity conditions are materials similarity in the range of plastic and elastic deformation, geometrical, frictional and thermal similarities. New original plastic similarity condition for physical modeling of metal forming processes is proposed in the paper. It bases on the mathematical description of similarity of the flow stress curves of soft materials and real ones

  9. Incorporating uncertainty in predictive species distribution modelling.

    Science.gov (United States)

    Beale, Colin M; Lennon, Jack J

    2012-01-19

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

  10. Model Predictive Control for Smart Energy Systems

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus

    pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...

  11. From epidemics to information propagation: Striking differences in structurally similar adaptive network models

    Science.gov (United States)

    Trajanovski, Stojan; Guo, Dongchao; Van Mieghem, Piet

    2015-09-01

    The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i) In the ASIS model a link is removed between two nodes if exactly one of the nodes is infected to suppress the epidemic, while a link is created in the AID model to speed up the information diffusion; (ii) a link is created between two susceptible nodes in the ASIS model to strengthen the healthy part of the network, while a link is broken in the AID model due to the lack of interest in informationless nodes. The ASIS and AID models may be considered as first-order models for cascades in real-world networks. While the ASIS model has been exploited in the literature, we show that the AID model is realistic by obtaining a good fit with Facebook data. Contrary to the common belief and intuition for such similar models, we show that the ASIS and AID models exhibit different but not opposite properties. Most remarkably, a unique metastable state always exists in the ASIS model, while there an hourglass-shaped region of instability in the AID model. Moreover, the epidemic threshold is a linear function in the effective link-breaking rate in the AID model, while it is almost constant but noisy in the AID model.

  12. Evaluating the Predictive Value of Growth Prediction Models

    Science.gov (United States)

    Murphy, Daniel L.; Gaertner, Matthew N.

    2014-01-01

    This study evaluates four growth prediction models--projection, student growth percentile, trajectory, and transition table--commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high…

  13. Computational hydrodynamic comparison of a mini vessel and a USP 2 dissolution testing system to predict the dynamic operating conditions for similarity of dissolution performance.

    Science.gov (United States)

    Wang, Bing; Bredael, Gerard; Armenante, Piero M

    2018-03-25

    The hydrodynamic characteristics of a mini vessel and a USP 2 dissolution testing system were obtained and compared to predict the tablet-liquid mass transfer coefficient from velocity distributions near the tablet and establish the dynamic operating conditions under which dissolution in mini vessels could be conducted to generate concentration profiles similar to those in the USP 2. Velocity profiles were obtained experimentally using Particle Image Velocimetry (PIV). Computational Fluid Dynamics (CFD) was used to predict the velocity distribution and strain rate around a model tablet. A CFD-based mass transfer model was also developed. When plotted against strain rate, the predicted tablet-liquid mass transfer coefficient was found to be independent of the system where it was obtained, implying that a tablet would dissolve at the same rate in both systems provided that the concentration gradient between the tablet surface and the bulk is the same, the tablet surface area per unit liquid volume is identical, and the two systems are operated at the appropriate agitation speeds specified in this work. The results of this work will help dissolution scientists operate mini vessels so as to predict the dissolution profiles in the USP 2, especially during the early stages of drug development. Copyright © 2018 Elsevier B.V. All rights reserved.

  14. Modelling the perceptual similarity of facial expressions from image statistics and neural responses.

    Science.gov (United States)

    Sormaz, Mladen; Watson, David M; Smith, William A P; Young, Andrew W; Andrews, Timothy J

    2016-04-01

    The ability to perceive facial expressions of emotion is essential for effective social communication. We investigated how the perception of facial expression emerges from the image properties that convey this important social signal, and how neural responses in face-selective brain regions might track these properties. To do this, we measured the perceptual similarity between expressions of basic emotions, and investigated how this is reflected in image measures and in the neural response of different face-selective regions. We show that the perceptual similarity of different facial expressions (fear, anger, disgust, sadness, happiness) can be predicted by both surface and feature shape information in the image. Using block design fMRI, we found that the perceptual similarity of expressions could also be predicted from the patterns of neural response in the face-selective posterior superior temporal sulcus (STS), but not in the fusiform face area (FFA). These results show that the perception of facial expression is dependent on the shape and surface properties of the image and on the activity of specific face-selective regions. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Genome-Wide Expression Profiling of Five Mouse Models Identifies Similarities and Differences with Human Psoriasis

    Science.gov (United States)

    Swindell, William R.; Johnston, Andrew; Carbajal, Steve; Han, Gangwen; Wohn, Christian; Lu, Jun; Xing, Xianying; Nair, Rajan P.; Voorhees, John J.; Elder, James T.; Wang, Xiao-Jing; Sano, Shigetoshi; Prens, Errol P.; DiGiovanni, John; Pittelkow, Mark R.; Ward, Nicole L.; Gudjonsson, Johann E.

    2011-01-01

    Development of a suitable mouse model would facilitate the investigation of pathomechanisms underlying human psoriasis and would also assist in development of therapeutic treatments. However, while many psoriasis mouse models have been proposed, no single model recapitulates all features of the human disease, and standardized validation criteria for psoriasis mouse models have not been widely applied. In this study, whole-genome transcriptional profiling is used to compare gene expression patterns manifested by human psoriatic skin lesions with those that occur in five psoriasis mouse models (K5-Tie2, imiquimod, K14-AREG, K5-Stat3C and K5-TGFbeta1). While the cutaneous gene expression profiles associated with each mouse phenotype exhibited statistically significant similarity to the expression profile of psoriasis in humans, each model displayed distinctive sets of similarities and differences in comparison to human psoriasis. For all five models, correspondence to the human disease was strong with respect to genes involved in epidermal development and keratinization. Immune and inflammation-associated gene expression, in contrast, was more variable between models as compared to the human disease. These findings support the value of all five models as research tools, each with identifiable areas of convergence to and divergence from the human disease. Additionally, the approach used in this paper provides an objective and quantitative method for evaluation of proposed mouse models of psoriasis, which can be strategically applied in future studies to score strengths of mouse phenotypes relative to specific aspects of human psoriasis. PMID:21483750

  16. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  17. Deep Predictive Models in Interactive Music

    OpenAIRE

    Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

    2018-01-01

    Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

  18. Experimental Study of Dowel Bar Alternatives Based on Similarity Model Test

    Directory of Open Access Journals (Sweden)

    Chichun Hu

    2017-01-01

    Full Text Available In this study, a small-scaled accelerated loading test based on similarity theory and Accelerated Pavement Analyzer was developed to evaluate dowel bars with different materials and cross-sections. Jointed concrete specimen consisting of one dowel was designed as scaled model for the test, and each specimen was subjected to 864 thousand loading cycles. Deflections between jointed slabs were measured with dial indicators, and strains of the dowel bars were monitored with strain gauges. The load transfer efficiency, differential deflection, and dowel-concrete bearing stress for each case were calculated from these measurements. The test results indicated that the effect of the dowel modulus on load transfer efficiency can be characterized based on the similarity model test developed in the study. Moreover, round steel dowel was found to have similar performance to larger FRP dowel, and elliptical dowel can be preferentially considered in practice.

  19. Bianchi VI{sub 0} and III models: self-similar approach

    Energy Technology Data Exchange (ETDEWEB)

    Belinchon, Jose Antonio, E-mail: abelcal@ciccp.e [Departamento de Fisica, ETS Arquitectura, UPM, Av. Juan de Herrera 4, Madrid 28040 (Spain)

    2009-09-07

    We study several cosmological models with Bianchi VI{sub 0} and III symmetries under the self-similar approach. We find new solutions for the 'classical' perfect fluid model as well as for the vacuum model although they are really restrictive for the equation of state. We also study a perfect fluid model with time-varying constants, G and LAMBDA. As in other studied models we find that the behaviour of G and LAMBDA are related. If G behaves as a growing time function then LAMBDA is a positive decreasing time function but if G is decreasing then LAMBDA{sub 0} is negative. We end by studying a massive cosmic string model, putting special emphasis in calculating the numerical values of the equations of state. We show that there is no SS solution for a string model with time-varying constants.

  20. Unreachable Setpoints in Model Predictive Control

    DEFF Research Database (Denmark)

    Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp

    2008-01-01

    In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...

  1. Bayesian Predictive Models for Rayleigh Wind Speed

    DEFF Research Database (Denmark)

    Shahirinia, Amir; Hajizadeh, Amin; Yu, David C

    2017-01-01

    predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....

  2. Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta-models

    Science.gov (United States)

    Spiliopoulou, Athina; Nagy, Reka; Bermingham, Mairead L.; Huffman, Jennifer E.; Hayward, Caroline; Vitart, Veronique; Rudan, Igor; Campbell, Harry; Wright, Alan F.; Wilson, James F.; Pong-Wong, Ricardo; Agakov, Felix; Navarro, Pau; Haley, Chris S.

    2015-01-01

    We explore the prediction of individuals' phenotypes for complex traits using genomic data. We compare several widely used prediction models, including Ridge Regression, LASSO and Elastic Nets estimated from cohort data, and polygenic risk scores constructed using published summary statistics from genome-wide association meta-analyses (GWAMA). We evaluate the interplay between relatedness, trait architecture and optimal marker density, by predicting height, body mass index (BMI) and high-density lipoprotein level (HDL) in two data cohorts, originating from Croatia and Scotland. We empirically demonstrate that dense models are better when all genetic effects are small (height and BMI) and target individuals are related to the training samples, while sparse models predict better in unrelated individuals and when some effects have moderate size (HDL). For HDL sparse models achieved good across-cohort prediction, performing similarly to the GWAMA risk score and to models trained within the same cohort, which indicates that, for predicting traits with moderately sized effects, large sample sizes and familial structure become less important, though still potentially useful. Finally, we propose a novel ensemble of whole-genome predictors with GWAMA risk scores and demonstrate that the resulting meta-model achieves higher prediction accuracy than either model on its own. We conclude that although current genomic predictors are not accurate enough for diagnostic purposes, performance can be improved without requiring access to large-scale individual-level data. Our methodologically simple meta-model is a means of performing predictive meta-analysis for optimizing genomic predictions and can be easily extended to incorporate multiple population-level summary statistics or other domain knowledge. PMID:25918167

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

    OpenAIRE

    David Ebert

    2006-01-01

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

  4. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  5. Massive Predictive Modeling using Oracle R Enterprise

    CERN Multimedia

    CERN. Geneva

    2014-01-01

    R is fast becoming the lingua franca for analyzing data via statistics, visualization, and predictive analytics. For enterprise-scale data, R users have three main concerns: scalability, performance, and production deployment. Oracle's R-based technologies - Oracle R Distribution, Oracle R Enterprise, Oracle R Connector for Hadoop, and the R package ROracle - address these concerns. In this talk, we introduce Oracle's R technologies, highlighting how each enables R users to achieve scalability and performance while making production deployment of R results a natural outcome of the data analyst/scientist efforts. The focus then turns to Oracle R Enterprise with code examples using the transparency layer and embedded R execution, targeting massive predictive modeling. One goal behind massive predictive modeling is to build models per entity, such as customers, zip codes, simulations, in an effort to understand behavior and tailor predictions at the entity level. Predictions...

  6. Multi-model analysis in hydrological prediction

    Science.gov (United States)

    Lanthier, M.; Arsenault, R.; Brissette, F.

    2017-12-01

    Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

  7. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  17. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Modeling and Prediction Using Stochastic Differential Equations

    DEFF Research Database (Denmark)

    Juhl, Rune; Møller, Jan Kloppenborg; Jørgensen, John Bagterp

    2016-01-01

    Pharmacokinetic/pharmakodynamic (PK/PD) modeling for a single subject is most often performed using nonlinear models based on deterministic ordinary differential equations (ODEs), and the variation between subjects in a population of subjects is described using a population (mixed effects) setup...... deterministic and can predict the future perfectly. A more realistic approach would be to allow for randomness in the model due to e.g., the model be too simple or errors in input. We describe a modeling and prediction setup which better reflects reality and suggests stochastic differential equations (SDEs...

  19. Predictive Model of Systemic Toxicity (SOT)

    Science.gov (United States)

    In an effort to ensure chemical safety in light of regulatory advances away from reliance on animal testing, USEPA and L’Oréal have collaborated to develop a quantitative systemic toxicity prediction model. Prediction of human systemic toxicity has proved difficult and remains a ...

  20. Cosmological model with anisotropic dark energy and self-similarity of the second kind

    International Nuclear Information System (INIS)

    Brandt, Carlos F. Charret; Silva, Maria de Fatima A. da; Rocha, Jaime F. Villas da; Chan, Roberto

    2006-01-01

    We study the evolution of an anisotropic fluid with self-similarity of the second kind. We found a class of solution to the Einstein field equations by assuming an equation of state where the radial pressure of the fluid is proportional to its energy density (p r =ωρ) and that the fluid moves along time-like geodesics. The equation of state and the anisotropy with self-similarity of second kind imply ω = -1. The energy conditions, geometrical and physical properties of the solutions are studied. We have found that for the parameter α=-1/2 , it may represent a Big Rip cosmological model. (author)

  1. An intermittency model for predicting roughness induced transition

    Science.gov (United States)

    Ge, Xuan; Durbin, Paul

    2014-11-01

    An extended model for roughness-induced transition is proposed based on an intermittency transport equation for RANS modeling formulated in local variables. To predict roughness effects in the fully turbulent boundary layer, published boundary conditions for k and ω are used, which depend on the equivalent sand grain roughness height, and account for the effective displacement of wall distance origin. Similarly in our approach, wall distance in the transition model for smooth surfaces is modified by an effective origin, which depends on roughness. Flat plate test cases are computed to show that the proposed model is able to predict the transition onset in agreement with a data correlation of transition location versus roughness height, Reynolds number, and inlet turbulence intensity. Experimental data for a turbine cascade are compared with the predicted results to validate the applicability of the proposed model. Supported by NSF Award Number 1228195.

  2. Global vegetation change predicted by the modified Budyko model

    Energy Technology Data Exchange (ETDEWEB)

    Monserud, R.A.; Tchebakova, N.M.; Leemans, R. (US Department of Agriculture, Moscow, ID (United States). Intermountain Research Station, Forest Service)

    1993-09-01

    A modified Budyko global vegetation model is used to predict changes in global vegetation patterns resulting from climate change (CO[sub 2] doubling). Vegetation patterns are predicted using a model based on a dryness index and potential evaporation determined by solving radiation balance equations. Climate change scenarios are derived from predictions from four General Circulation Models (GCM's) of the atmosphere (GFDL, GISS, OSU, and UKMO). All four GCM scenarios show similar trends in vegetation shifts and in areas that remain stable, although the UKMO scenario predicts greater warming than the others. Climate change maps produced by all four GCM scenarios show good agreement with the current climate vegetation map for the globe as a whole, although over half of the vegetation classes show only poor to fair agreement. The most stable areas are Desert and Ice/Polar Desert. Because most of the predicted warming is concentrated in the Boreal and Temperate zones, vegetation there is predicted to undergo the greatest change. Most vegetation classes in the Subtropics and Tropics are predicted to expand. Any shift in the Tropics favouring either Forest over Savanna, or vice versa, will be determined by the magnitude of the increased precipitation accompanying global warming. Although the model predicts equilibrium conditions to which many plant species cannot adjust (through migration or microevolution) in the 50-100 y needed for CO[sub 2] doubling, it is not clear if projected global warming will result in drastic or benign vegetation change. 72 refs., 3 figs., 3 tabs.

  3. Spent fuel: prediction model development

    International Nuclear Information System (INIS)

    Almassy, M.Y.; Bosi, D.M.; Cantley, D.A.

    1979-07-01

    The need for spent fuel disposal performance modeling stems from a requirement to assess the risks involved with deep geologic disposal of spent fuel, and to support licensing and public acceptance of spent fuel repositories. Through the balanced program of analysis, diagnostic testing, and disposal demonstration tests, highlighted in this presentation, the goal of defining risks and of quantifying fuel performance during long-term disposal can be attained

  4. Navy Recruit Attrition Prediction Modeling

    Science.gov (United States)

    2014-09-01

    have high correlation with attrition, such as age, job characteristics, command climate, marital status, behavior issues prior to recruitment, and the...the additive model. glm(formula = Outcome ~ Age + Gender + Marital + AFQTCat + Pay + Ed + Dep, family = binomial, data = ltraining) Deviance ...0.1 ‘ ‘ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance : 105441 on 85221 degrees of freedom Residual deviance

  5. Predicting and Modeling RNA Architecture

    Science.gov (United States)

    Westhof, Eric; Masquida, Benoît; Jossinet, Fabrice

    2011-01-01

    SUMMARY A general approach for modeling the architecture of large and structured RNA molecules is described. The method exploits the modularity and the hierarchical folding of RNA architecture that is viewed as the assembly of preformed double-stranded helices defined by Watson-Crick base pairs and RNA modules maintained by non-Watson-Crick base pairs. Despite the extensive molecular neutrality observed in RNA structures, specificity in RNA folding is achieved through global constraints like lengths of helices, coaxiality of helical stacks, and structures adopted at the junctions of helices. The Assemble integrated suite of computer tools allows for sequence and structure analysis as well as interactive modeling by homology or ab initio assembly with possibilities for fitting within electronic density maps. The local key role of non-Watson-Crick pairs guides RNA architecture formation and offers metrics for assessing the accuracy of three-dimensional models in a more useful way than usual root mean square deviation (RMSD) values. PMID:20504963

  6. Predictive Models and Computational Toxicology (II IBAMTOX)

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  7. Finding furfural hydrogenation catalysts via predictive modelling

    NARCIS (Netherlands)

    Strassberger, Z.; Mooijman, M.; Ruijter, E.; Alberts, A.H.; Maldonado, A.G.; Orru, R.V.A.; Rothenberg, G.

    2010-01-01

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes

  8. FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL ...

    African Journals Online (AJOL)

    FINITE ELEMENT MODEL FOR PREDICTING RESIDUAL STRESSES IN ... the transverse residual stress in the x-direction (σx) had a maximum value of 375MPa ... the finite element method are in fair agreement with the experimental results.

  9. Evaluation of CASP8 model quality predictions

    KAUST Repository

    Cozzetto, Domenico; Kryshtafovych, Andriy; Tramontano, Anna

    2009-01-01

    established a prediction category to evaluate their performance in 2006. In 2008 the experiment was repeated and its results are reported here. Participants were invited to infer the correctness of the protein models submitted by the registered automatic

  10. On the scale similarity in large eddy simulation. A proposal of a new model

    International Nuclear Information System (INIS)

    Pasero, E.; Cannata, G.; Gallerano, F.

    2004-01-01

    Among the most common LES models present in literature there are the Eddy Viscosity-type models. In these models the subgrid scale (SGS) stress tensor is related to the resolved strain rate tensor through a scalar eddy viscosity coefficient. These models are affected by three fundamental drawbacks: they are purely dissipative, i.e. they cannot account for back scatter; they assume that the principal axes of the resolved strain rate tensor and SGS stress tensor are aligned; and that a local balance exists between the SGS turbulent kinetic energy production and its dissipation. Scale similarity models (SSM) were created to overcome the drawbacks of eddy viscosity-type models. The SSM models, such as that of Bardina et al. and that of Liu et al., assume that scales adjacent in wave number space present similar hydrodynamic features. This similarity makes it possible to effectively relate the unresolved scales, represented by the modified Cross tensor and the modified Reynolds tensor, to the smallest resolved scales represented by the modified Leonard tensor] or by a term obtained through multiple filtering operations at different scales. The models of Bardina et al. and Liu et al. are affected, however, by a fundamental drawback: they are not dissipative enough, i.e they are not able to ensure a sufficient energy drain from the resolved scales of motion to the unresolved ones. In this paper it is shown that such a drawback is due to the fact that such models do not take into account the smallest unresolved scales where the most dissipation of turbulent SGS energy takes place. A new scale similarity LES model that is able to grant an adequate drain of energy from the resolved scales to the unresolved ones is presented. The SGS stress tensor is aligned with the modified Leonard tensor. The coefficient of proportionality is expressed in terms of the trace of the modified Leonard tensor and in terms of the SGS kinetic energy (computed by solving its balance equation). The

  11. Conservation of connectivity of model-space effective interactions under a class of similarity transformation

    International Nuclear Information System (INIS)

    Duan Changkui; Gong Yungui; Dong Huining; Reid, Michael F.

    2004-01-01

    Effective interaction operators usually act on a restricted model space and give the same energies (for Hamiltonian) and matrix elements (for transition operators, etc.) as those of the original operators between the corresponding true eigenstates. Various types of effective operators are possible. Those well defined effective operators have been shown to be related to each other by similarity transformation. Some of the effective operators have been shown to have connected-diagram expansions. It is shown in this paper that under a class of very general similarity transformations, the connectivity is conserved. The similarity transformation between Hermitian and non-Hermitian Rayleigh-Schroedinger perturbative effective operators is one of such transformations and hence the connectivity can be deducted from each other

  12. Conservation of connectivity of model-space effective interactions under a class of similarity transformation.

    Science.gov (United States)

    Duan, Chang-Kui; Gong, Yungui; Dong, Hui-Ning; Reid, Michael F

    2004-09-15

    Effective interaction operators usually act on a restricted model space and give the same energies (for Hamiltonian) and matrix elements (for transition operators, etc.) as those of the original operators between the corresponding true eigenstates. Various types of effective operators are possible. Those well defined effective operators have been shown to be related to each other by similarity transformation. Some of the effective operators have been shown to have connected-diagram expansions. It is shown in this paper that under a class of very general similarity transformations, the connectivity is conserved. The similarity transformation between Hermitian and non-Hermitian Rayleigh-Schrodinger perturbative effective operators is one of such transformations and hence the connectivity can be deducted from each other.

  13. Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms.

    Science.gov (United States)

    Falda, Marco; Toppo, Stefano; Pescarolo, Alessandro; Lavezzo, Enrico; Di Camillo, Barbara; Facchinetti, Andrea; Cilia, Elisa; Velasco, Riccardo; Fontana, Paolo

    2012-03-28

    Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods. Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes. The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.

  14. Model-free aftershock forecasts constructed from similar sequences in the past

    Science.gov (United States)

    van der Elst, N.; Page, M. T.

    2017-12-01

    The basic premise behind aftershock forecasting is that sequences in the future will be similar to those in the past. Forecast models typically use empirically tuned parametric distributions to approximate past sequences, and project those distributions into the future to make a forecast. While parametric models do a good job of describing average outcomes, they are not explicitly designed to capture the full range of variability between sequences, and can suffer from over-tuning of the parameters. In particular, parametric forecasts may produce a high rate of "surprises" - sequences that land outside the forecast range. Here we present a non-parametric forecast method that cuts out the parametric "middleman" between training data and forecast. The method is based on finding past sequences that are similar to the target sequence, and evaluating their outcomes. We quantify similarity as the Poisson probability that the observed event count in a past sequence reflects the same underlying intensity as the observed event count in the target sequence. Event counts are defined in terms of differential magnitude relative to the mainshock. The forecast is then constructed from the distribution of past sequences outcomes, weighted by their similarity. We compare the similarity forecast with the Reasenberg and Jones (RJ95) method, for a set of 2807 global aftershock sequences of M≥6 mainshocks. We implement a sequence-specific RJ95 forecast using a global average prior and Bayesian updating, but do not propagate epistemic uncertainty. The RJ95 forecast is somewhat more precise than the similarity forecast: 90% of observed sequences fall within a factor of two of the median RJ95 forecast value, whereas the fraction is 85% for the similarity forecast. However, the surprise rate is much higher for the RJ95 forecast; 10% of observed sequences fall in the upper 2.5% of the (Poissonian) forecast range. The surprise rate is less than 3% for the similarity forecast. The similarity

  15. A general model for metabolic scaling in self-similar asymmetric networks.

    Directory of Open Access Journals (Sweden)

    Alexander Byers Brummer

    2017-03-01

    Full Text Available How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE model argues that these two principles (space-filling and energy minimization are (i general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

  16. Return Predictability, Model Uncertainty, and Robust Investment

    DEFF Research Database (Denmark)

    Lukas, Manuel

    Stock return predictability is subject to great uncertainty. In this paper we use the model confidence set approach to quantify uncertainty about expected utility from investment, accounting for potential return predictability. For monthly US data and six representative return prediction models, we...... find that confidence sets are very wide, change significantly with the predictor variables, and frequently include expected utilities for which the investor prefers not to invest. The latter motivates a robust investment strategy maximizing the minimal element of the confidence set. The robust investor...... allocates a much lower share of wealth to stocks compared to a standard investor....

  17. Model predictive Controller for Mobile Robot

    OpenAIRE

    Alireza Rezaee

    2017-01-01

    This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...

  18. Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

    Directory of Open Access Journals (Sweden)

    Jin Dai

    2014-01-01

    Full Text Available The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers.

  19. Spatial Economics Model Predicting Transport Volume

    Directory of Open Access Journals (Sweden)

    Lu Bo

    2016-10-01

    Full Text Available It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

  20. Applications of Analytical Self-Similar Solutions of Reynolds-Averaged Models for Instability-Induced Turbulent Mixing

    Science.gov (United States)

    Hartland, Tucker; Schilling, Oleg

    2017-11-01

    Analytical self-similar solutions to several families of single- and two-scale, eddy viscosity and Reynolds stress turbulence models are presented for Rayleigh-Taylor, Richtmyer-Meshkov, and Kelvin-Helmholtz instability-induced turbulent mixing. The use of algebraic relationships between model coefficients and physical observables (e.g., experimental growth rates) following from the self-similar solutions to calibrate a member of a given family of turbulence models is shown. It is demonstrated numerically that the algebraic relations accurately predict the value and variation of physical outputs of a Reynolds-averaged simulation in flow regimes that are consistent with the simplifying assumptions used to derive the solutions. The use of experimental and numerical simulation data on Reynolds stress anisotropy ratios to calibrate a Reynolds stress model is briefly illustrated. The implications of the analytical solutions for future Reynolds-averaged modeling of hydrodynamic instability-induced mixing are briefly discussed. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  1. Accuracy assessment of landslide prediction models

    International Nuclear Information System (INIS)

    Othman, A N; Mohd, W M N W; Noraini, S

    2014-01-01

    The increasing population and expansion of settlements over hilly areas has greatly increased the impact of natural disasters such as landslide. Therefore, it is important to developed models which could accurately predict landslide hazard zones. Over the years, various techniques and models have been developed to predict landslide hazard zones. The aim of this paper is to access the accuracy of landslide prediction models developed by the authors. The methodology involved the selection of study area, data acquisition, data processing and model development and also data analysis. The development of these models are based on nine different landslide inducing parameters i.e. slope, land use, lithology, soil properties, geomorphology, flow accumulation, aspect, proximity to river and proximity to road. Rank sum, rating, pairwise comparison and AHP techniques are used to determine the weights for each of the parameters used. Four (4) different models which consider different parameter combinations are developed by the authors. Results obtained are compared to landslide history and accuracies for Model 1, Model 2, Model 3 and Model 4 are 66.7, 66.7%, 60% and 22.9% respectively. From the results, rank sum, rating and pairwise comparison can be useful techniques to predict landslide hazard zones

  2. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  3. Hierarchical Model for the Similarity Measurement of a Complex Holed-Region Entity Scene

    Directory of Open Access Journals (Sweden)

    Zhanlong Chen

    2017-11-01

    Full Text Available Complex multi-holed-region entity scenes (i.e., sets of random region with holes are common in spatial database systems, spatial query languages, and the Geographic Information System (GIS. A multi-holed-region (region with an arbitrary number of holes is an abstraction of the real world that primarily represents geographic objects that have more than one interior boundary, such as areas that contain several lakes or lakes that contain islands. When the similarity of the two complex holed-region entity scenes is measured, the number of regions in the scenes and the number of holes in the regions are usually different between the two scenes, which complicates the matching relationships of holed-regions and holes. The aim of this research is to develop several holed-region similarity metrics and propose a hierarchical model to measure comprehensively the similarity between two complex holed-region entity scenes. The procedure first divides a complex entity scene into three layers: a complex scene, a micro-spatial-scene, and a simple entity (hole. The relationships between the adjacent layers are considered to be sets of relationships, and each level of similarity measurements is nested with the adjacent one. Next, entity matching is performed from top to bottom, while the similarity results are calculated from local to global. In addition, we utilize position graphs to describe the distribution of the holed-regions and subsequently describe the directions between the holes using a feature matrix. A case study that uses the Great Lakes in North America in 1986 and 2015 as experimental data illustrates the entire similarity measurement process between two complex holed-region entity scenes. The experimental results show that the hierarchical model accounts for the relationships of the different layers in the entire complex holed-region entity scene. The model can effectively calculate the similarity of complex holed-region entity scenes, even if the

  4. A self-similar model for conduction in the plasma erosion opening switch

    International Nuclear Information System (INIS)

    Mosher, D.; Grossmann, J.M.; Ottinger, P.F.; Colombant, D.G.

    1987-01-01

    The conduction phase of the plasma erosion opening switch (PEOS) is characterized by combining a 1-D fluid model for plasma hydrodynamics, Maxwell's equations, and a 2-D electron-orbit analysis. A self-similar approximation for the plasma and field variables permits analytic expressions for their space and time variations to be derived. It is shown that a combination of axial MHD compression and magnetic insulation of high-energy electrons emitted from the switch cathode can control the character of switch conduction. The analysis highlights the need to include additional phenomena for accurate fluid modeling of PEOS conduction

  5. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  6. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  7. Traditional and robust vector selection methods for use with similarity based models

    International Nuclear Information System (INIS)

    Hines, J. W.; Garvey, D. R.

    2006-01-01

    Vector selection, or instance selection as it is often called in the data mining literature, performs a critical task in the development of nonparametric, similarity based models. Nonparametric, similarity based modeling (SBM) is a form of 'lazy learning' which constructs a local model 'on the fly' by comparing a query vector to historical, training vectors. For large training sets the creation of local models may become cumbersome, since each training vector must be compared to the query vector. To alleviate this computational burden, varying forms of training vector sampling may be employed with the goal of selecting a subset of the training data such that the samples are representative of the underlying process. This paper describes one such SBM, namely auto-associative kernel regression (AAKR), and presents five traditional vector selection methods and one robust vector selection method that may be used to select prototype vectors from a larger data set in model training. The five traditional vector selection methods considered are min-max, vector ordering, combination min-max and vector ordering, fuzzy c-means clustering, and Adeli-Hung clustering. Each method is described in detail and compared using artificially generated data and data collected from the steam system of an operating nuclear power plant. (authors)

  8. State impulsive control strategies for a two-languages competitive model with bilingualism and interlinguistic similarity

    Science.gov (United States)

    Nie, Lin-Fei; Teng, Zhi-Dong; Nieto, Juan J.; Jung, Il Hyo

    2015-07-01

    For reasons of preserving endangered languages, we propose, in this paper, a novel two-languages competitive model with bilingualism and interlinguistic similarity, where state-dependent impulsive control strategies are introduced. The novel control model includes two control threshold values, which are different from the previous state-dependent impulsive differential equations. By using qualitative analysis method, we obtain that the control model exhibits two stable positive order-1 periodic solutions under some general conditions. Moreover, numerical simulations clearly illustrate the main theoretical results and feasibility of state-dependent impulsive control strategies. Meanwhile numerical simulations also show that state-dependent impulsive control strategy can be applied to other general two-languages competitive model and obtain the desired result. The results indicate that the fractions of two competitive languages can be kept within a reasonable level under almost any circumstances. Theoretical basis for finding a new control measure to protect the endangered language is offered.

  9. Hierarchical modeling of systems with similar components: A framework for adaptive monitoring and control

    International Nuclear Information System (INIS)

    Memarzadeh, Milad; Pozzi, Matteo; Kolter, J. Zico

    2016-01-01

    System management includes the selection of maintenance actions depending on the available observations: when a system is made up by components known to be similar, data collected on one is also relevant for the management of others. This is typically the case of wind farms, which are made up by similar turbines. Optimal management of wind farms is an important task due to high cost of turbines' operation and maintenance: in this context, we recently proposed a method for planning and learning at system-level, called PLUS, built upon the Partially Observable Markov Decision Process (POMDP) framework, which treats transition and emission probabilities as random variables, and is therefore suitable for including model uncertainty. PLUS models the components as independent or identical. In this paper, we extend that formulation, allowing for a weaker similarity among components. The proposed approach, called Multiple Uncertain POMDP (MU-POMDP), models the components as POMDPs, and assumes the corresponding parameters as dependent random variables. Through this framework, we can calibrate specific degradation and emission models for each component while, at the same time, process observations at system-level. We compare the performance of the proposed MU-POMDP with PLUS, and discuss its potential and computational complexity. - Highlights: • A computational framework is proposed for adaptive monitoring and control. • It adopts a scheme based on Markov Chain Monte Carlo for inference and learning. • Hierarchical Bayesian modeling is used to allow a system-level flow of information. • Results show potential of significant savings in management of wind farms.

  10. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-09-10

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

  11. Corporate prediction models, ratios or regression analysis?

    NARCIS (Netherlands)

    Bijnen, E.J.; Wijn, M.F.C.M.

    1994-01-01

    The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in

  12. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  13. Energy based prediction models for building acoustics

    DEFF Research Database (Denmark)

    Brunskog, Jonas

    2012-01-01

    In order to reach robust and simplified yet accurate prediction models, energy based principle are commonly used in many fields of acoustics, especially in building acoustics. This includes simple energy flow models, the framework of statistical energy analysis (SEA) as well as more elaborated...... principles as, e.g., wave intensity analysis (WIA). The European standards for building acoustic predictions, the EN 12354 series, are based on energy flow and SEA principles. In the present paper, different energy based prediction models are discussed and critically reviewed. Special attention is placed...... on underlying basic assumptions, such as diffuse fields, high modal overlap, resonant field being dominant, etc., and the consequences of these in terms of limitations in the theory and in the practical use of the models....

  14. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  15. Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor

    Directory of Open Access Journals (Sweden)

    Ye Li

    2017-01-01

    Full Text Available Recommended system is beneficial to e-commerce sites, which provides customers with product information and recommendations; the recommendation system is currently widely used in many fields. In an era of information explosion, the key challenges of the recommender system is to obtain valid information from the tremendous amount of information and produce high quality recommendations. However, when facing the large mount of information, the traditional collaborative filtering algorithm usually obtains a high degree of sparseness, which ultimately lead to low accuracy recommendations. To tackle this issue, we propose a novel algorithm named Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor, which is based on the trust model and is combined with the user similarity. The novel algorithm takes into account the degree of interest overlap between the two users and results in a superior performance to the recommendation based on Trust Model in criteria of Precision, Recall, Diversity and Coverage. Additionally, the proposed model can effectively improve the efficiency of collaborative filtering algorithm and achieve high performance.

  16. Scaling and interaction of self-similar modes in models of high Reynolds number wall turbulence.

    Science.gov (United States)

    Sharma, A S; Moarref, R; McKeon, B J

    2017-03-13

    Previous work has established the usefulness of the resolvent operator that maps the terms nonlinear in the turbulent fluctuations to the fluctuations themselves. Further work has described the self-similarity of the resolvent arising from that of the mean velocity profile. The orthogonal modes provided by the resolvent analysis describe the wall-normal coherence of the motions and inherit that self-similarity. In this contribution, we present the implications of this similarity for the nonlinear interaction between modes with different scales and wall-normal locations. By considering the nonlinear interactions between modes, it is shown that much of the turbulence scaling behaviour in the logarithmic region can be determined from a single arbitrarily chosen reference plane. Thus, the geometric scaling of the modes is impressed upon the nonlinear interaction between modes. Implications of these observations on the self-sustaining mechanisms of wall turbulence, modelling and simulation are outlined.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

  17. An Experimental Comparison of Similarity Assessment Measures for 3D Models on Constrained Surface Deformation

    Science.gov (United States)

    Quan, Lulin; Yang, Zhixin

    2010-05-01

    To address the issues in the area of design customization, this paper expressed the specification and application of the constrained surface deformation, and reported the experimental performance comparison of three prevail effective similarity assessment algorithms on constrained surface deformation domain. Constrained surface deformation becomes a promising method that supports for various downstream applications of customized design. Similarity assessment is regarded as the key technology for inspecting the success of new design via measuring the difference level between the deformed new design and the initial sample model, and indicating whether the difference level is within the limitation. According to our theoretical analysis and pre-experiments, three similarity assessment algorithms are suitable for this domain, including shape histogram based method, skeleton based method, and U system moment based method. We analyze their basic functions and implementation methodologies in detail, and do a series of experiments on various situations to test their accuracy and efficiency using precision-recall diagram. Shoe model is chosen as an industrial example for the experiments. It shows that shape histogram based method gained an optimal performance in comparison. Based on the result, we proposed a novel approach that integrating surface constrains and shape histogram description with adaptive weighting method, which emphasize the role of constrains during the assessment. The limited initial experimental result demonstrated that our algorithm outperforms other three algorithms. A clear direction for future development is also drawn at the end of the paper.

  18. Prediction Models for Dynamic Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    Aman, Saima; Frincu, Marc; Chelmis, Charalampos; Noor, Muhammad; Simmhan, Yogesh; Prasanna, Viktor K.

    2015-11-02

    As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D2R, which we address in this paper. Our first contribution is the formal definition of D2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D2R. Also, prediction models require just few days’ worth of data indicating that small amounts of

  19. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

  20. Self-similar measures in multi-sector endogenous growth models

    International Nuclear Information System (INIS)

    La Torre, Davide; Marsiglio, Simone; Mendivil, Franklin; Privileggi, Fabio

    2015-01-01

    We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Uzawa–Lucas (1965, 1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transformations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some (possibly fractal) compact set. This proves that also the steady state of endogenous growth models—i.e., the stochastic balanced growth path equilibrium—might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity).

  1. Self-similar formation of the Kolmogorov spectrum in the Leith model of turbulence

    International Nuclear Information System (INIS)

    Nazarenko, S V; Grebenev, V N

    2017-01-01

    The last stage of evolution toward the stationary Kolmogorov spectrum of hydrodynamic turbulence is studied using the Leith model [1]. This evolution is shown to manifest itself as a reflection wave in the wavenumber space propagating from the largest toward the smallest wavenumbers, and is described by a self-similar solution of a new (third) kind. This stage follows the previously studied stage of an initial explosive propagation of the spectral front from the smallest to the largest wavenumbers reaching arbitrarily large wavenumbers in a finite time, and which was described by a self-similar solution of the second kind [2–4]. Nonstationary solutions corresponding to ‘warm cascades’ characterised by a thermalised spectrum at large wavenumbers are also obtained. (paper)

  2. Wind farm production prediction - The Zephyr model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Giebel, G. [Risoe National Lab., Wind Energy Dept., Roskilde (Denmark); Madsen, H. [IMM (DTU), Kgs. Lyngby (Denmark); Nielsen, T.S. [IMM (DTU), Kgs. Lyngby (Denmark); Joergensen, J.U. [Danish Meteorologisk Inst., Copenhagen (Denmark); Lauersen, L. [Danish Meteorologisk Inst., Copenhagen (Denmark); Toefting, J. [Elsam, Fredericia (DK); Christensen, H.S. [Eltra, Fredericia (Denmark); Bjerge, C. [SEAS, Haslev (Denmark)

    2002-06-01

    This report describes a project - funded by the Danish Ministry of Energy and the Environment - which developed a next generation prediction system called Zephyr. The Zephyr system is a merging between two state-of-the-art prediction systems: Prediktor of Risoe National Laboratory and WPPT of IMM at the Danish Technical University. The numerical weather predictions were generated by DMI's HIRLAM model. Due to technical difficulties programming the system, only the computational core and a very simple version of the originally very complex system were developed. The project partners were: Risoe, DMU, DMI, Elsam, Eltra, Elkraft System, SEAS and E2. (au)

  3. Model predictive controller design of hydrocracker reactors

    OpenAIRE

    GÖKÇE, Dila

    2011-01-01

    This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...

  4. A solvable self-similar model of the sausage instability in a resistive Z pinch

    International Nuclear Information System (INIS)

    Lampe, M.

    1991-01-01

    A solvable model is developed for the linearized sausage mode within the context of resistive magnetohydrodynamics. The model is based on the assumption that the fluid motion of the plasma is self-similar, as well as several assumptions pertinent to the limit of wavelength long compared to the pinch radius. The perturbations to the magnetic field are not assumed to be self-similar, but rather are calculated. Effects arising from time dependences of the z-independent perturbed state, e.g., current rising as t α , Ohmic heating, and time variation of the pinch radius, are included in the analysis. The formalism appears to provide a good representation of ''global'' modes that involve coherent sausage distortion of the entire cross section of the pinch, but excludes modes that are localized radially, and higher radial eigenmodes. For this and other reasons, it is expected that the model underestimates the maximum instability growth rates, but is reasonable for global sausage modes. The net effect of resistivity and time variation of the unperturbed state is to decrease the growth rate if α approx-lt 1, but never by more than a factor of about 2. The effect is to increase the growth rate if α approx-gt 1

  5. A Deep Similarity Metric Learning Model for Matching Text Chunks to Spatial Entities

    Science.gov (United States)

    Ma, K.; Wu, L.; Tao, L.; Li, W.; Xie, Z.

    2017-12-01

    The matching of spatial entities with related text is a long-standing research topic that has received considerable attention over the years. This task aims at enrich the contents of spatial entity, and attach the spatial location information to the text chunk. In the data fusion field, matching spatial entities with the corresponding describing text chunks has a big range of significance. However, the most traditional matching methods often rely fully on manually designed, task-specific linguistic features. This work proposes a Deep Similarity Metric Learning Model (DSMLM) based on Siamese Neural Network to learn similarity metric directly from the textural attributes of spatial entity and text chunk. The low-dimensional feature representation of the space entity and the text chunk can be learned separately. By employing the Cosine distance to measure the matching degree between the vectors, the model can make the matching pair vectors as close as possible. Mearnwhile, it makes the mismatching as far apart as possible through supervised learning. In addition, extensive experiments and analysis on geological survey data sets show that our DSMLM model can effectively capture the matching characteristics between the text chunk and the spatial entity, and achieve state-of-the-art performance.

  6. Initial virtual flight test for a dynamically similar aircraft model with control augmentation system

    Directory of Open Access Journals (Sweden)

    Linliang Guo

    2017-04-01

    Full Text Available To satisfy the validation requirements of flight control law for advanced aircraft, a wind tunnel based virtual flight testing has been implemented in a low speed wind tunnel. A 3-degree-of-freedom gimbal, ventrally installed in the model, was used in conjunction with an actively controlled dynamically similar model of aircraft, which was equipped with the inertial measurement unit, attitude and heading reference system, embedded computer and servo-actuators. The model, which could be rotated around its center of gravity freely by the aerodynamic moments, together with the flow field, operator and real time control system made up the closed-loop testing circuit. The model is statically unstable in longitudinal direction, and it can fly stably in wind tunnel with the function of control augmentation of the flight control laws. The experimental results indicate that the model responds well to the operator’s instructions. The response of the model in the tests shows reasonable agreement with the simulation results. The difference of response of angle of attack is less than 0.5°. The effect of stability augmentation and attitude control law was validated in the test, meanwhile the feasibility of virtual flight test technique treated as preliminary evaluation tool for advanced flight vehicle configuration research was also verified.

  7. Embryo quality predictive models based on cumulus cells gene expression

    Directory of Open Access Journals (Sweden)

    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  8. Vertex labeling and routing in self-similar outerplanar unclustered graphs modeling complex networks

    International Nuclear Information System (INIS)

    Comellas, Francesc; Miralles, Alicia

    2009-01-01

    This paper introduces a labeling and optimal routing algorithm for a family of modular, self-similar, small-world graphs with clustering zero. Many properties of this family are comparable to those of networks associated with technological and biological systems with low clustering, such as the power grid, some electronic circuits and protein networks. For these systems, the existence of models with an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization) and also to understand the underlying mechanisms that have shaped their particular structure.

  9. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  10. Longitudinal modeling to predict vital capacity in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Jahandideh, Samad; Taylor, Albert A; Beaulieu, Danielle; Keymer, Mike; Meng, Lisa; Bian, Amy; Atassi, Nazem; Andrews, Jinsy; Ennist, David L

    2018-05-01

    Death in amyotrophic lateral sclerosis (ALS) patients is related to respiratory failure, which is assessed in clinical settings by measuring vital capacity. We developed ALS-VC, a modeling tool for longitudinal prediction of vital capacity in ALS patients. A gradient boosting machine (GBM) model was trained using the PRO-ACT (Pooled Resource Open-access ALS Clinical Trials) database of over 10,000 ALS patient records. We hypothesized that a reliable vital capacity predictive model could be developed using PRO-ACT. The model was used to compare FVC predictions with a 30-day run-in period to predictions made from just baseline. The internal root mean square deviations (RMSD) of the run-in and baseline models were 0.534 and 0.539, respectively, across the 7L FVC range captured in PRO-ACT. The RMSDs of the run-in and baseline models using an unrelated, contemporary external validation dataset (0.553 and 0.538, respectively) were comparable to the internal validation. The model was shown to have similar accuracy for predicting SVC (RMSD = 0.562). The most important features for both run-in and baseline models were "Baseline forced vital capacity" and "Days since baseline." We developed ALS-VC, a GBM model trained with the PRO-ACT ALS dataset that provides vital capacity predictions generalizable to external datasets. The ALS-VC model could be helpful in advising and counseling patients, and, in clinical trials, it could be used to generate virtual control arms against which observed outcomes could be compared, or used to stratify patients into slowly, average, and rapidly progressing subgroups.

  11. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  12. PREDICTIVE CAPACITY OF ARCH FAMILY MODELS

    Directory of Open Access Journals (Sweden)

    Raphael Silveira Amaro

    2016-03-01

    Full Text Available In the last decades, a remarkable number of models, variants from the Autoregressive Conditional Heteroscedastic family, have been developed and empirically tested, making extremely complex the process of choosing a particular model. This research aim to compare the predictive capacity, using the Model Confidence Set procedure, than five conditional heteroskedasticity models, considering eight different statistical probability distributions. The financial series which were used refers to the log-return series of the Bovespa index and the Dow Jones Industrial Index in the period between 27 October 2008 and 30 December 2014. The empirical evidences showed that, in general, competing models have a great homogeneity to make predictions, either for a stock market of a developed country or for a stock market of a developing country. An equivalent result can be inferred for the statistical probability distributions that were used.

  13. Alcator C-Mod predictive modeling

    International Nuclear Information System (INIS)

    Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas

    2001-01-01

    Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles

  14. Self-similarities of periodic structures for a discrete model of a two-gene system

    International Nuclear Information System (INIS)

    Souza, S.L.T. de; Lima, A.A.; Caldas, I.L.; Medrano-T, R.O.; Guimarães-Filho, Z.O.

    2012-01-01

    We report self-similar properties of periodic structures remarkably organized in the two-parameter space for a two-gene system, described by two-dimensional symmetric map. The map consists of difference equations derived from the chemical reactions for gene expression and regulation. We characterize the system by using Lyapunov exponents and isoperiodic diagrams identifying periodic windows, denominated Arnold tongues and shrimp-shaped structures. Period-adding sequences are observed for both periodic windows. We also identify Fibonacci-type series and Golden ratio for Arnold tongues, and period multiple-of-three windows for shrimps. -- Highlights: ► The existence of noticeable periodic windows has been reported recently for several nonlinear systems. ► The periodic window distributions appear highly organized in two-parameter space. ► We characterize self-similar properties of Arnold tongues and shrimps for a two-gene model. ► We determine the period of the Arnold tongues recognizing a Fibonacci-type sequence. ► We explore self-similar features of the shrimps identifying multiple period-three structures.

  15. Self-similarities of periodic structures for a discrete model of a two-gene system

    Energy Technology Data Exchange (ETDEWEB)

    Souza, S.L.T. de, E-mail: thomaz@ufsj.edu.br [Departamento de Física e Matemática, Universidade Federal de São João del-Rei, Ouro Branco, MG (Brazil); Lima, A.A. [Escola de Farmácia, Universidade Federal de Ouro Preto, Ouro Preto, MG (Brazil); Caldas, I.L. [Instituto de Física, Universidade de São Paulo, São Paulo, SP (Brazil); Medrano-T, R.O. [Departamento de Ciências Exatas e da Terra, Universidade Federal de São Paulo, Diadema, SP (Brazil); Guimarães-Filho, Z.O. [Aix-Marseille Univ., CNRS PIIM UMR6633, International Institute for Fusion Science, Marseille (France)

    2012-03-12

    We report self-similar properties of periodic structures remarkably organized in the two-parameter space for a two-gene system, described by two-dimensional symmetric map. The map consists of difference equations derived from the chemical reactions for gene expression and regulation. We characterize the system by using Lyapunov exponents and isoperiodic diagrams identifying periodic windows, denominated Arnold tongues and shrimp-shaped structures. Period-adding sequences are observed for both periodic windows. We also identify Fibonacci-type series and Golden ratio for Arnold tongues, and period multiple-of-three windows for shrimps. -- Highlights: ► The existence of noticeable periodic windows has been reported recently for several nonlinear systems. ► The periodic window distributions appear highly organized in two-parameter space. ► We characterize self-similar properties of Arnold tongues and shrimps for a two-gene model. ► We determine the period of the Arnold tongues recognizing a Fibonacci-type sequence. ► We explore self-similar features of the shrimps identifying multiple period-three structures.

  16. A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space

    Directory of Open Access Journals (Sweden)

    Jinjun Li

    2011-01-01

    Full Text Available A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation, and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.

  17. Individual differences in emotion processing: how similar are diffusion model parameters across tasks?

    Science.gov (United States)

    Mueller, Christina J; White, Corey N; Kuchinke, Lars

    2017-11-27

    The goal of this study was to replicate findings of diffusion model parameters capturing emotion effects in a lexical decision task and investigating whether these findings extend to other tasks of implicit emotion processing. Additionally, we were interested in the stability of diffusion model parameters across emotional stimuli and tasks for individual subjects. Responses to words in a lexical decision task were compared with responses to faces in a gender categorization task for stimuli of the emotion categories: happy, neutral and fear. Main effects of emotion as well as stability of emerging response style patterns as evident in diffusion model parameters across these tasks were analyzed. Based on earlier findings, drift rates were assumed to be more similar in response to stimuli of the same emotion category compared to stimuli of a different emotion category. Results showed that emotion effects of the tasks differed with a processing advantage for happy followed by neutral and fear-related words in the lexical decision task and a processing advantage for neutral followed by happy and fearful faces in the gender categorization task. Both emotion effects were captured in estimated drift rate parameters-and in case of the lexical decision task also in the non-decision time parameters. A principal component analysis showed that contrary to our hypothesis drift rates were more similar within a specific task context than within a specific emotion category. Individual response patterns of subjects across tasks were evident in significant correlations regarding diffusion model parameters including response styles, non-decision times and information accumulation.

  18. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    2010-02-01

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  19. Modelling the predictive performance of credit scoring

    Directory of Open Access Journals (Sweden)

    Shi-Wei Shen

    2013-07-01

    Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

  20. Comparison of two ordinal prediction models

    DEFF Research Database (Denmark)

    Kattan, Michael W; Gerds, Thomas A

    2015-01-01

    system (i.e. old or new), such as the level of evidence for one or more factors included in the system or the general opinions of expert clinicians. However, given the major objective of estimating prognosis on an ordinal scale, we argue that the rival staging system candidates should be compared...... on their ability to predict outcome. We sought to outline an algorithm that would compare two rival ordinal systems on their predictive ability. RESULTS: We devised an algorithm based largely on the concordance index, which is appropriate for comparing two models in their ability to rank observations. We...... demonstrate our algorithm with a prostate cancer staging system example. CONCLUSION: We have provided an algorithm for selecting the preferred staging system based on prognostic accuracy. It appears to be useful for the purpose of selecting between two ordinal prediction models....

  1. Predictive analytics can support the ACO model.

    Science.gov (United States)

    Bradley, Paul

    2012-04-01

    Predictive analytics can be used to rapidly spot hard-to-identify opportunities to better manage care--a key tool in accountable care. When considering analytics models, healthcare providers should: Make value-based care a priority and act on information from analytics models. Create a road map that includes achievable steps, rather than major endeavors. Set long-term expectations and recognize that the effectiveness of an analytics program takes time, unlike revenue cycle initiatives that may show a quick return.

  2. Predictive performance models and multiple task performance

    Science.gov (United States)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  3. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  4. Distributed Model Predictive Control via Dual Decomposition

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle

    2014-01-01

    This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...

  5. Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model

    Directory of Open Access Journals (Sweden)

    Salha M. Alzahrani

    2015-07-01

    Full Text Available Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7% handmade paraphrases, 54,815 (55.4% artificial plagiarism cases, and 32,578 (32.9% plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.

  6. A stepwise model to predict monthly streamflow

    Science.gov (United States)

    Mahmood Al-Juboori, Anas; Guven, Aytac

    2016-12-01

    In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

  7. The synaptonemal complex of basal metazoan hydra: more similarities to vertebrate than invertebrate meiosis model organisms.

    Science.gov (United States)

    Fraune, Johanna; Wiesner, Miriam; Benavente, Ricardo

    2014-03-20

    The synaptonemal complex (SC) is an evolutionarily well-conserved structure that mediates chromosome synapsis during prophase of the first meiotic division. Although its structure is conserved, the characterized protein components in the current metazoan meiosis model systems (Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) show no sequence homology, challenging the question of a single evolutionary origin of the SC. However, our recent studies revealed the monophyletic origin of the mammalian SC protein components. Many of them being ancient in Metazoa and already present in the cnidarian Hydra. Remarkably, a comparison between different model systems disclosed a great similarity between the SC components of Hydra and mammals while the proteins of the ecdysozoan systems (D. melanogaster and C. elegans) differ significantly. In this review, we introduce the basal-branching metazoan species Hydra as a potential novel invertebrate model system for meiosis research and particularly for the investigation of SC evolution, function and assembly. Also, available methods for SC research in Hydra are summarized. Copyright © 2014. Published by Elsevier Ltd.

  8. Electrostatic ion thrusters - towards predictive modeling

    Energy Technology Data Exchange (ETDEWEB)

    Kalentev, O.; Matyash, K.; Duras, J.; Lueskow, K.F.; Schneider, R. [Ernst-Moritz-Arndt Universitaet Greifswald, D-17489 (Germany); Koch, N. [Technische Hochschule Nuernberg Georg Simon Ohm, Kesslerplatz 12, D-90489 Nuernberg (Germany); Schirra, M. [Thales Electronic Systems GmbH, Soeflinger Strasse 100, D-89077 Ulm (Germany)

    2014-02-15

    The development of electrostatic ion thrusters so far has mainly been based on empirical and qualitative know-how, and on evolutionary iteration steps. This resulted in considerable effort regarding prototype design, construction and testing and therefore in significant development and qualification costs and high time demands. For future developments it is anticipated to implement simulation tools which allow for quantitative prediction of ion thruster performance, long-term behavior and space craft interaction prior to hardware design and construction. Based on integrated numerical models combining self-consistent kinetic plasma models with plasma-wall interaction modules a new quality in the description of electrostatic thrusters can be reached. These open the perspective for predictive modeling in this field. This paper reviews the application of a set of predictive numerical modeling tools on an ion thruster model of the HEMP-T (High Efficiency Multi-stage Plasma Thruster) type patented by Thales Electron Devices GmbH. (copyright 2014 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  9. An Intelligent Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    IbrahimM. Hamed

    2012-08-01

    Full Text Available This paper presents an intelligent model for stock market signal prediction using Multi-Layer Perceptron (MLP Artificial Neural Networks (ANN. Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD is used, as a learning algorithm, because it converges fast and provides generalization in the learning mechanism. Both accuracy and efficiency of the proposed model were confirmed through the Microsoft stock, from wall-street market, and various data sets, from different sectors of the Egyptian stock market. In addition, sensitivity analysis was conducted on the various parameters of the model to ensure the coverage of the generalization issue. Finally, statistical significance was examined using ANOVA test.

  10. Predictive Models, How good are they?

    DEFF Research Database (Denmark)

    Kasch, Helge

    The WAD grading system has been used for more than 20 years by now. It has shown long-term viability, but with strengths and limitations. New bio-psychosocial assessment of the acute whiplash injured subject may provide better prediction of long-term disability and pain. Furthermore, the emerging......-up. It is important to obtain prospective identification of the relevant risk underreported disability could, if we were able to expose these hidden “risk-factors” during our consultations, provide us with better predictive models. New data from large clinical studies will present exciting new genetic risk markers...

  11. Similar Biophysical Abnormalities in Glomeruli and Podocytes from Two Distinct Models.

    Science.gov (United States)

    Embry, Addie E; Liu, Zhenan; Henderson, Joel M; Byfield, F Jefferson; Liu, Liping; Yoon, Joonho; Wu, Zhenzhen; Cruz, Katrina; Moradi, Sara; Gillombardo, C Barton; Hussain, Rihanna Z; Doelger, Richard; Stuve, Olaf; Chang, Audrey N; Janmey, Paul A; Bruggeman, Leslie A; Miller, R Tyler

    2018-03-23

    Background FSGS is a pattern of podocyte injury that leads to loss of glomerular function. Podocytes support other podocytes and glomerular capillary structure, oppose hemodynamic forces, form the slit diaphragm, and have mechanical properties that permit these functions. However, the biophysical characteristics of glomeruli and podocytes in disease remain unclear. Methods Using microindentation, atomic force microscopy, immunofluorescence microscopy, quantitative RT-PCR, and a three-dimensional collagen gel contraction assay, we studied the biophysical and structural properties of glomeruli and podocytes in chronic (Tg26 mice [HIV protein expression]) and acute (protamine administration [cytoskeletal rearrangement]) models of podocyte injury. Results Compared with wild-type glomeruli, Tg26 glomeruli became progressively more deformable with disease progression, despite increased collagen content. Tg26 podocytes had disordered cytoskeletons, markedly abnormal focal adhesions, and weaker adhesion; they failed to respond to mechanical signals and exerted minimal traction force in three-dimensional collagen gels. Protamine treatment had similar but milder effects on glomeruli and podocytes. Conclusions Reduced structural integrity of Tg26 podocytes causes increased deformability of glomerular capillaries and limits the ability of capillaries to counter hemodynamic force, possibly leading to further podocyte injury. Loss of normal podocyte mechanical integrity could injure neighboring podocytes due to the absence of normal biophysical signals required for podocyte maintenance. The severe defects in podocyte mechanical behavior in the Tg26 model may explain why Tg26 glomeruli soften progressively, despite increased collagen deposition, and may be the basis for the rapid course of glomerular diseases associated with severe podocyte injury. In milder injury (protamine), similar processes occur but over a longer time. Copyright © 2018 by the American Society of Nephrology.

  12. NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES

    Directory of Open Access Journals (Sweden)

    SILVA R. G.

    1999-01-01

    Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.

  13. A statistical model for predicting muscle performance

    Science.gov (United States)

    Byerly, Diane Leslie De Caix

    The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing

  14. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  15. Neuro-fuzzy modeling in bankruptcy prediction

    Directory of Open Access Journals (Sweden)

    Vlachos D.

    2003-01-01

    Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

  16. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity

    DEFF Research Database (Denmark)

    Olsen, Michael; Greve, Sara; Blicher, Marie

    2016-01-01

    OBJECTIVE: Carotid-femoral pulse wave velocity (cfPWV) adds significantly to traditional cardiovascular (CV) risk prediction, but is not widely available. Therefore, it would be helpful if cfPWV could be replaced by an estimated carotid-femoral pulse wave velocity (ePWV) using age and mean blood...... pressure and previously published equations. The aim of this study was to investigate whether ePWV could predict CV events independently of traditional cardiovascular risk factors and/or cfPWV. DESIGN AND METHOD: cfPWV was measured and ePWV calculated in 2366 apparently healthy subjects from four age...

  17. More Similar than Different? Exploring Cultural Models of Depression among Latino Immigrants in Florida

    Directory of Open Access Journals (Sweden)

    Dinorah (Dina Martinez Tyson

    2011-01-01

    Full Text Available The Surgeon General's report, “Culture, Race, and Ethnicity: A Supplement to Mental Health,” points to the need for subgroup specific mental health research that explores the cultural variation and heterogeneity of the Latino population. Guided by cognitive anthropological theories of culture, we utilized ethnographic interviewing techniques to explore cultural models of depression among foreign-born Mexican (n=30, Cuban (n=30, Columbian (n=30, and island-born Puerto Ricans (n=30, who represent the largest Latino groups in Florida. Results indicate that Colombian, Cuban, Mexican, and Puerto Rican immigrants showed strong intragroup consensus in their models of depression causality, symptoms, and treatment. We found more agreement than disagreement among all four groups regarding core descriptions of depression, which was largely unexpected but can potentially be explained by their common immigrant experiences. Findings expand our understanding about Latino subgroup similarities and differences in their conceptualization of depression and can be used to inform the adaptation of culturally relevant interventions in order to better serve Latino immigrant communities.

  18. Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.

    Directory of Open Access Journals (Sweden)

    Octavio Miramontes

    Full Text Available Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties--including Lévy flights--in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.

  19. Models of Affective Decision Making: How Do Feelings Predict Choice?

    Science.gov (United States)

    Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

    2016-06-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. © The Author(s) 2016.

  20. Predictive model for convective flows induced by surface reactivity contrast

    Science.gov (United States)

    Davidson, Scott M.; Lammertink, Rob G. H.; Mani, Ali

    2018-05-01

    Concentration gradients in a fluid adjacent to a reactive surface due to contrast in surface reactivity generate convective flows. These flows result from contributions by electro- and diffusio-osmotic phenomena. In this study, we have analyzed reactive patterns that release and consume protons, analogous to bimetallic catalytic conversion of peroxide. Similar systems have typically been studied using either scaling analysis to predict trends or costly numerical simulation. Here, we present a simple analytical model, bridging the gap in quantitative understanding between scaling relations and simulations, to predict the induced potentials and consequent velocities in such systems without the use of any fitting parameters. Our model is tested against direct numerical solutions to the coupled Poisson, Nernst-Planck, and Stokes equations. Predicted slip velocities from the model and simulations agree to within a factor of ≈2 over a multiple order-of-magnitude change in the input parameters. Our analysis can be used to predict enhancement of mass transport and the resulting impact on overall catalytic conversion, and is also applicable to predicting the speed of catalytic nanomotors.

  1. Predictive Models for Carcinogenicity and Mutagenicity ...

    Science.gov (United States)

    Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t

  2. A scientometric prediction of the discovery of the first potentially habitable planet with a mass similar to Earth.

    Directory of Open Access Journals (Sweden)

    Samuel Arbesman

    Full Text Available BACKGROUND: The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. METHODOLOGY/PRINCIPAL FINDINGS: Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. CONCLUSIONS/SIGNIFICANCE: Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields.

  3. A scientometric prediction of the discovery of the first potentially habitable planet with a mass similar to Earth.

    Science.gov (United States)

    Arbesman, Samuel; Laughlin, Gregory

    2010-10-04

    The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields.

  4. Validated predictive modelling of the environmental resistome.

    Science.gov (United States)

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-06-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  5. Nonlinear model predictive control theory and algorithms

    CERN Document Server

    Grüne, Lars

    2017-01-01

    This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

  6. Baryogenesis model predicting antimatter in the Universe

    International Nuclear Information System (INIS)

    Kirilova, D.

    2003-01-01

    Cosmic ray and gamma-ray data do not rule out antimatter domains in the Universe, separated at distances bigger than 10 Mpc from us. Hence, it is interesting to analyze the possible generation of vast antimatter structures during the early Universe evolution. We discuss a SUSY-condensate baryogenesis model, predicting large separated regions of matter and antimatter. The model provides generation of the small locally observed baryon asymmetry for a natural initial conditions, it predicts vast antimatter domains, separated from the matter ones by baryonically empty voids. The characteristic scale of antimatter regions and their distance from the matter ones is in accordance with observational constraints from cosmic ray, gamma-ray and cosmic microwave background anisotropy data

  7. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    OpenAIRE

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre t...

  8. Predictive Modeling in Actinide Chemistry and Catalysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Ping [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-05-16

    These are slides from a presentation on predictive modeling in actinide chemistry and catalysis. The following topics are covered in these slides: Structures, bonding, and reactivity (bonding can be quantified by optical probes and theory, and electronic structures and reaction mechanisms of actinide complexes); Magnetic resonance properties (transition metal catalysts with multi-nuclear centers, and NMR/EPR parameters); Moving to more complex systems (surface chemistry of nanomaterials, and interactions of ligands with nanoparticles); Path forward and conclusions.

  9. Tectonic predictions with mantle convection models

    Science.gov (United States)

    Coltice, Nicolas; Shephard, Grace E.

    2018-04-01

    Over the past 15 yr, numerical models of convection in Earth's mantle have made a leap forward: they can now produce self-consistent plate-like behaviour at the surface together with deep mantle circulation. These digital tools provide a new window into the intimate connections between plate tectonics and mantle dynamics, and can therefore be used for tectonic predictions, in principle. This contribution explores this assumption. First, initial conditions at 30, 20, 10 and 0 Ma are generated by driving a convective flow with imposed plate velocities at the surface. We then compute instantaneous mantle flows in response to the guessed temperature fields without imposing any boundary conditions. Plate boundaries self-consistently emerge at correct locations with respect to reconstructions, except for small plates close to subduction zones. As already observed for other types of instantaneous flow calculations, the structure of the top boundary layer and upper-mantle slab is the dominant character that leads to accurate predictions of surface velocities. Perturbations of the rheological parameters have little impact on the resulting surface velocities. We then compute fully dynamic model evolution from 30 and 10 to 0 Ma, without imposing plate boundaries or plate velocities. Contrary to instantaneous calculations, errors in kinematic predictions are substantial, although the plate layout and kinematics in several areas remain consistent with the expectations for the Earth. For these calculations, varying the rheological parameters makes a difference for plate boundary evolution. Also, identified errors in initial conditions contribute to first-order kinematic errors. This experiment shows that the tectonic predictions of dynamic models over 10 My are highly sensitive to uncertainties of rheological parameters and initial temperature field in comparison to instantaneous flow calculations. Indeed, the initial conditions and the rheological parameters can be good enough

  10. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  11. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  12. A new, accurate predictive model for incident hypertension.

    Science.gov (United States)

    Völzke, Henry; Fung, Glenn; Ittermann, Till; Yu, Shipeng; Baumeister, Sebastian E; Dörr, Marcus; Lieb, Wolfgang; Völker, Uwe; Linneberg, Allan; Jørgensen, Torben; Felix, Stephan B; Rettig, Rainer; Rao, Bharat; Kroemer, Heyo K

    2013-11-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

  13. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Predicting extinction rates in stochastic epidemic models

    International Nuclear Information System (INIS)

    Schwartz, Ira B; Billings, Lora; Dykman, Mark; Landsman, Alexandra

    2009-01-01

    We investigate the stochastic extinction processes in a class of epidemic models. Motivated by the process of natural disease extinction in epidemics, we examine the rate of extinction as a function of disease spread. We show that the effective entropic barrier for extinction in a susceptible–infected–susceptible epidemic model displays scaling with the distance to the bifurcation point, with an unusual critical exponent. We make a direct comparison between predictions and numerical simulations. We also consider the effect of non-Gaussian vaccine schedules, and show numerically how the extinction process may be enhanced when the vaccine schedules are Poisson distributed

  15. Predictive Modeling of the CDRA 4BMS

    Science.gov (United States)

    Coker, Robert F.; Knox, James C.

    2016-01-01

    As part of NASA's Advanced Exploration Systems (AES) program and the Life Support Systems Project (LSSP), fully predictive models of the Four Bed Molecular Sieve (4BMS) of the Carbon Dioxide Removal Assembly (CDRA) on the International Space Station (ISS) are being developed. This virtual laboratory will be used to help reduce mass, power, and volume requirements for future missions. In this paper we describe current and planned modeling developments in the area of carbon dioxide removal to support future crewed Mars missions as well as the resolution of anomalies observed in the ISS CDRA.

  16. Estimated carotid-femoral pulse wave velocity has similar predictive value as measured carotid-femoral pulse wave velocity

    DEFF Research Database (Denmark)

    Greve, Sara V; Blicher, Marie K; Kruger, Ruan

    2016-01-01

    BACKGROUND: Carotid-femoral pulse wave velocity (cfPWV) adds significantly to traditional cardiovascular risk prediction, but is not widely available. Therefore, it would be helpful if cfPWV could be replaced by an estimated carotid-femoral pulse wave velocity (ePWV) using age and mean blood pres...... that these traditional risk scores have underestimated the complicated impact of age and blood pressure on arterial stiffness and cardiovascular risk....

  17. An application of superpositions of two-state Markovian sources to the modelling of self-similar behaviour

    DEFF Research Database (Denmark)

    Andersen, Allan T.; Nielsen, Bo Friis

    1997-01-01

    We present a modelling framework and a fitting method for modelling second order self-similar behaviour with the Markovian arrival process (MAP). The fitting method is based on fitting to the autocorrelation function of counts a second order self-similar process. It is shown that with this fittin...

  18. Data Driven Economic Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Masoud Kheradmandi

    2018-04-01

    Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

  19. Parent-child value similarity in families with young children: The predictive power of prosocial educational goals.

    Science.gov (United States)

    Döring, Anna K; Makarova, Elena; Herzog, Walter; Bardi, Anat

    2017-11-01

    Value transmission from one generation to the next is a key issue in every society, but it is not clear which parents are the most successful in transmitting their values to their children. We propose parents' prosocial educational goals as key predictors of parent-child value similarity. Accordingly, we hypothesized that the more parents wanted their children to endorse values of self-transcendence (helping, supporting, and caring for others) and the less parents wanted their children to endorse the opposing values of self-enhancement (striving for power and achievement), the higher would be parent-child overall value similarity. Findings from two studies of families - Study 1: 261 Swiss families, children aged 7-9 years; Study 2: 157 German families, children aged 6-11 years - confirmed this hypothesis. The effect was even stronger after controlling for values that prevail in the Swiss and German society, respectively. We integrate evidence from this study of values in families with young children with existing findings from studies with adolescent and adult children, and we discuss potential pathways from parents' educational goals to parent-child value similarity. © 2017 The British Psychological Society.

  20. Plant control using embedded predictive models

    International Nuclear Information System (INIS)

    Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.

    1990-01-01

    B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available

  1. A new biodegradation prediction model specific to petroleum hydrocarbons.

    Science.gov (United States)

    Howard, Philip; Meylan, William; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Michael; Parkerton, Thomas F

    2005-08-01

    A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

  2. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  3. Modeling and Prediction of Krueger Device Noise

    Science.gov (United States)

    Guo, Yueping; Burley, Casey L.; Thomas, Russell H.

    2016-01-01

    This paper presents the development of a noise prediction model for aircraft Krueger flap devices that are considered as alternatives to leading edge slotted slats. The prediction model decomposes the total Krueger noise into four components, generated by the unsteady flows, respectively, in the cove under the pressure side surface of the Krueger, in the gap between the Krueger trailing edge and the main wing, around the brackets supporting the Krueger device, and around the cavity on the lower side of the main wing. For each noise component, the modeling follows a physics-based approach that aims at capturing the dominant noise-generating features in the flow and developing correlations between the noise and the flow parameters that control the noise generation processes. The far field noise is modeled using each of the four noise component's respective spectral functions, far field directivities, Mach number dependencies, component amplitudes, and other parametric trends. Preliminary validations are carried out by using small scale experimental data, and two applications are discussed; one for conventional aircraft and the other for advanced configurations. The former focuses on the parametric trends of Krueger noise on design parameters, while the latter reveals its importance in relation to other airframe noise components.

  4. Prediction of Chemical Function: Model Development and ...

    Science.gov (United States)

    The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

  5. Evaluating Predictive Models of Software Quality

    Science.gov (United States)

    Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

    2014-06-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  6. Predicting FLDs Using a Multiscale Modeling Scheme

    Science.gov (United States)

    Wu, Z.; Loy, C.; Wang, E.; Hegadekatte, V.

    2017-09-01

    The measurement of a single forming limit diagram (FLD) requires significant resources and is time consuming. We have developed a multiscale modeling scheme to predict FLDs using a combination of limited laboratory testing, crystal plasticity (VPSC) modeling, and dual sequential-stage finite element (ABAQUS/Explicit) modeling with the Marciniak-Kuczynski (M-K) criterion to determine the limit strain. We have established a means to work around existing limitations in ABAQUS/Explicit by using an anisotropic yield locus (e.g., BBC2008) in combination with the M-K criterion. We further apply a VPSC model to reduce the number of laboratory tests required to characterize the anisotropic yield locus. In the present work, we show that the predicted FLD is in excellent agreement with the measured FLD for AA5182 in the O temper. Instead of 13 different tests as for a traditional FLD determination within Novelis, our technique uses just four measurements: tensile properties in three orientations; plane strain tension; biaxial bulge; and the sheet crystallographic texture. The turnaround time is consequently far less than for the traditional laboratory measurement of the FLD.

  7. PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION

    Directory of Open Access Journals (Sweden)

    Narciso Ysac Avila Serrano

    2009-06-01

    Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (P≤ 0.05 among cultivars. Paceño and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients ≥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (P≤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.

  8. Evaluating predictive models of software quality

    International Nuclear Information System (INIS)

    Ciaschini, V; Canaparo, M; Ronchieri, E; Salomoni, D

    2014-01-01

    Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

  9. Gamma-Ray Pulsars Models and Predictions

    CERN Document Server

    Harding, A K

    2001-01-01

    Pulsed emission from gamma-ray pulsars originates inside the magnetosphere, from radiation by charged particles accelerated near the magnetic poles or in the outer gaps. In polar cap models, the high energy spectrum is cut off by magnetic pair production above an energy that is dependent on the local magnetic field strength. While most young pulsars with surface fields in the range B = 10^{12} - 10^{13} G are expected to have high energy cutoffs around several GeV, the gamma-ray spectra of old pulsars having lower surface fields may extend to 50 GeV. Although the gamma-ray emission of older pulsars is weaker, detecting pulsed emission at high energies from nearby sources would be an important confirmation of polar cap models. Outer gap models predict more gradual high-energy turnovers at around 10 GeV, but also predict an inverse Compton component extending to TeV energies. Detection of pulsed TeV emission, which would not survive attenuation at the polar caps, is thus an important test of outer gap models. N...

  10. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  11. Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

    Science.gov (United States)

    Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

    2015-01-01

    Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

  12. SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.

    Science.gov (United States)

    Li, Ying Hong; Xu, Jing Yu; Tao, Lin; Li, Xiao Feng; Li, Shuang; Zeng, Xian; Chen, Shang Ying; Zhang, Peng; Qin, Chu; Zhang, Cheng; Chen, Zhe; Zhu, Feng; Chen, Yu Zong

    2016-01-01

    Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

  13. Do similarities or differences between CEO leadership and organizational culture have a more positive effect on firm performance? A test of competing predictions.

    Science.gov (United States)

    Hartnell, Chad A; Kinicki, Angelo J; Lambert, Lisa Schurer; Fugate, Mel; Doyle Corner, Patricia

    2016-06-01

    This study examines the nature of the interaction between CEO leadership and organizational culture using 2 common metathemes (task and relationship) in leadership and culture research. Two perspectives, similarity and dissimilarity, offer competing predictions about the fit, or interaction, between leadership and culture and its predicted effect on firm performance. Predictions for the similarity perspective draw upon attribution theory and social identity theory of leadership, whereas predictions for the dissimilarity perspective are developed based upon insights from leadership contingency theories and the notion of substitutability. Hierarchical regression results from 114 CEOs and 324 top management team (TMT) members failed to support the similarity hypotheses but revealed broad support for the dissimilarity predictions. Findings suggest that culture can serve as a substitute for leadership when leadership behaviors are redundant with cultural values (i.e., they both share a task- or relationship-oriented focus). Findings also support leadership contingency theories indicating that CEO leadership is effective when it provides psychological and motivational resources lacking in the organization's culture. We discuss theoretical and practical implications and delineate directions for future research. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  14. An analytical model for climatic predictions

    International Nuclear Information System (INIS)

    Njau, E.C.

    1990-12-01

    A climatic model based upon analytical expressions is presented. This model is capable of making long-range predictions of heat energy variations on regional or global scales. These variations can then be transformed into corresponding variations of some other key climatic parameters since weather and climatic changes are basically driven by differential heating and cooling around the earth. On the basis of the mathematical expressions upon which the model is based, it is shown that the global heat energy structure (and hence the associated climatic system) are characterized by zonally as well as latitudinally propagating fluctuations at frequencies downward of 0.5 day -1 . We have calculated the propagation speeds for those particular frequencies that are well documented in the literature. The calculated speeds are in excellent agreement with the measured speeds. (author). 13 refs

  15. An Anisotropic Hardening Model for Springback Prediction

    Science.gov (United States)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  16. An Anisotropic Hardening Model for Springback Prediction

    International Nuclear Information System (INIS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-01-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test

  17. In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function.

    Science.gov (United States)

    Follin, Elna; Karlsson, Maria; Lundegaard, Claus; Nielsen, Morten; Wallin, Stefan; Paulsson, Kajsa; Westerdahl, Helena

    2013-04-01

    The major histocompatibility complex (MHC) genes are the most polymorphic genes found in the vertebrate genome, and they encode proteins that play an essential role in the adaptive immune response. Many songbirds (passerines) have been shown to have a large number of transcribed MHC class I genes compared to most mammals. To elucidate the reason for this large number of genes, we compared 14 MHC class I alleles (α1-α3 domains), from great reed warbler, house sparrow and tree sparrow, via phylogenetic analysis, homology modelling and in silico peptide-binding predictions to investigate their functional and genetic relationships. We found more pronounced clustering of the MHC class I allomorphs (allele specific proteins) in regards to their function (peptide-binding specificities) compared to their genetic relationships (amino acid sequences), indicating that the high number of alleles is of functional significance. The MHC class I allomorphs from house sparrow and tree sparrow, species that diverged 10 million years ago (MYA), had overlapping peptide-binding specificities, and these similarities across species were also confirmed in phylogenetic analyses based on amino acid sequences. Notably, there were also overlapping peptide-binding specificities in the allomorphs from house sparrow and great reed warbler, although these species diverged 30 MYA. This overlap was not found in a tree based on amino acid sequences. Our interpretation is that convergent evolution on the level of the protein function, possibly driven by selection from shared pathogens, has resulted in allomorphs with similar peptide-binding repertoires, although trans-species evolution in combination with gene conversion cannot be ruled out.

  18. Web tools for predictive toxicology model building.

    Science.gov (United States)

    Jeliazkova, Nina

    2012-07-01

    The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

  19. Predictions of models for environmental radiological assessment

    International Nuclear Information System (INIS)

    Peres, Sueli da Silva; Lauria, Dejanira da Costa; Mahler, Claudio Fernando

    2011-01-01

    In the field of environmental impact assessment, models are used for estimating source term, environmental dispersion and transfer of radionuclides, exposure pathway, radiation dose and the risk for human beings Although it is recognized that the specific information of local data are important to improve the quality of the dose assessment results, in fact obtaining it can be very difficult and expensive. Sources of uncertainties are numerous, among which we can cite: the subjectivity of modelers, exposure scenarios and pathways, used codes and general parameters. The various models available utilize different mathematical approaches with different complexities that can result in different predictions. Thus, for the same inputs different models can produce very different outputs. This paper presents briefly the main advances in the field of environmental radiological assessment that aim to improve the reliability of the models used in the assessment of environmental radiological impact. The intercomparison exercise of model supplied incompatible results for 137 Cs and 60 Co, enhancing the need for developing reference methodologies for environmental radiological assessment that allow to confront dose estimations in a common comparison base. The results of the intercomparison exercise are present briefly. (author)

  20. A Predictive Maintenance Model for Railway Tracks

    DEFF Research Database (Denmark)

    Li, Rui; Wen, Min; Salling, Kim Bang

    2015-01-01

    presents a mathematical model based on Mixed Integer Programming (MIP) which is designed to optimize the predictive railway tamping activities for ballasted track for the time horizon up to four years. The objective function is setup to minimize the actual costs for the tamping machine (measured by time......). Five technical and economic aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality...

  1. Effective modelling for predictive analytics in data science ...

    African Journals Online (AJOL)

    Effective modelling for predictive analytics in data science. ... the nearabsence of empirical or factual predictive analytics in the mainstream research going on ... Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning.

  2. Similar circuits but different connectivity patterns between the cerebellum, basal ganglia, and supplementary motor area in early Parkinson's disease patients and controls during predictive motor timing.

    Science.gov (United States)

    Husárová, Ivica; Mikl, Michal; Lungu, Ovidiu V; Mareček, Radek; Vaníček, Jiří; Bareš, Martin

    2013-10-01

    The cerebellum, basal ganglia (BG), and other cortical regions, such as supplementary motor area (SMA) have emerged as important structures dealing with various aspects of timing, yet the modulation of functional connectivity between them during motor timing tasks remains unexplored. We used dynamic causal modeling to investigate the differences in effective connectivity (EC) between these regions and its modulation by behavioral outcome during a motor timing prediction task in a group of 16 patients with early Parkinson's disease (PD) and 17 healthy controls. Behavioral events (hits and errors) constituted the driving input connected to the cerebellum, and the modulation in connectivity was assessed relative to the hit condition (successful interception of target). The driving input elicited response in the target area, while modulatory input changed the specific connection strength. The neuroimaging data revealed similar structure of intrinsic connectivity in both groups with unidirectional connections from cerebellum to both sides of the BG, from BG to the SMA, and then from SMA to the cerebellum. However, the type of intrinsic connection was different between two groups. In the PD group, the connection between the SMA and cerebellum was inhibitory in comparison to the HC group, where the connection was activated. Furthermore, the modulation of connectivity by the performance in the task was different between the two groups, with decreased connectivity between the cerebellum and left BG and SMA and a more pronounced symmetry of these connections in controls. In the same time, there was an increased EC between the cerebellum and both sides of BG with more pronounced asymmetry (stronger connection with left BG) in patients. In addition, in the PD group the modulatory input strengthened inhibitory connectivity between the SMA and the cerebellum, while in the HC group the excitatory connection was slightly strengthened. Our findings indicate that although early PD

  3. A fast prediction of plant behaviour in the steam generator tube rupture accident at Mihama unit 2 using a similar case

    International Nuclear Information System (INIS)

    Gofuku, Akio; Tanaka, Yutaka; Numoto, Atsushi; Yoshikawa, Hidekazu.

    1996-01-01

    It is important to predict fast and accurately future trend of behaviour of a nuclear power plant in an emergency situation. The case-based reasoning is a strong tool for this purpose because it solves a problem by effectively using past similar cases. This study investigates the applicability of the case-based reasoning as a fast prediction technique of plant behaviour. This paper discusses a prediction of initial plant behaviour in the steam generator tube rupture accident happened at the Mihama nuclear power plant unit 2 by using the behaviour data of an accident of the same type happened at Prairie Island nuclear power plant unit 1. The prediction results coincide well with the reported plant behaviour although there are several important differences in the detailed plant specifications and operator actions between the two SGTR accidents. (author)

  4. A Model for Comparative Analysis of the Similarity between Android and iOS Operating Systems

    Directory of Open Access Journals (Sweden)

    Lixandroiu R.

    2014-12-01

    Full Text Available Due to recent expansion of mobile devices, in this article we try to do an analysis of two of the most used mobile OSS. This analysis is made on the method of calculating Jaccard's similarity coefficient. To complete the analysis, we developed a hierarchy of factors in evaluating OSS. Analysis has shown that the two OSS are similar in terms of functionality, but there are a number of factors that weighted make a difference.

  5. Ethnic differences in the effects of media on body image: the effects of priming with ethnically different or similar models.

    Science.gov (United States)

    Bruns, Gina L; Carter, Michele M

    2015-04-01

    Media exposure has been positively correlated with body dissatisfaction. While body image concerns are common, being African American has been found to be a protective factor in the development of body dissatisfaction. Participants either viewed ten advertisements showing 1) ethnically-similar thin models; 2) ethnically-different thin models; 3) ethnically-similar plus-sized models; and 4) ethnically-diverse plus-sized models. Following exposure, body image was measured. African American women had less body dissatisfaction than Caucasian women. Ethnically-similar thin-model conditions did not elicit greater body dissatisfaction scores than ethnically-different thin or plus-sized models nor did the ethnicity of the model impact ratings of body dissatisfaction for women of either race. There were no differences among the African American women exposed to plus-sized versus thin models. Among Caucasian women exposure to plus-sized models resulted in greater body dissatisfaction than exposure to thin models. Results support existing literature that African American women experience less body dissatisfaction than Caucasian women even following exposure to an ethnically-similar thin model. Additionally, women exposed to plus-sized model conditions experienced greater body dissatisfaction than those shown thin models. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Combining GPS measurements and IRI model predictions

    International Nuclear Information System (INIS)

    Hernandez-Pajares, M.; Juan, J.M.; Sanz, J.; Bilitza, D.

    2002-01-01

    The free electrons distributed in the ionosphere (between one hundred and thousands of km in height) produce a frequency-dependent effect on Global Positioning System (GPS) signals: a delay in the pseudo-orange and an advance in the carrier phase. These effects are proportional to the columnar electron density between the satellite and receiver, i.e. the integrated electron density along the ray path. Global ionospheric TEC (total electron content) maps can be obtained with GPS data from a network of ground IGS (international GPS service) reference stations with an accuracy of few TEC units. The comparison with the TOPEX TEC, mainly measured over the oceans far from the IGS stations, shows a mean bias and standard deviation of about 2 and 5 TECUs respectively. The discrepancies between the STEC predictions and the observed values show an RMS typically below 5 TECUs (which also includes the alignment code noise). he existence of a growing database 2-hourly global TEC maps and with resolution of 5x2.5 degrees in longitude and latitude can be used to improve the IRI prediction capability of the TEC. When the IRI predictions and the GPS estimations are compared for a three month period around the Solar Maximum, they are in good agreement for middle latitudes. An over-determination of IRI TEC has been found at the extreme latitudes, the IRI predictions being, typically two times higher than the GPS estimations. Finally, local fits of the IRI model can be done by tuning the SSN from STEC GPS observations

  7. In Silico Modeling of Gastrointestinal Drug Absorption: Predictive Performance of Three Physiologically Based Absorption Models.

    Science.gov (United States)

    Sjögren, Erik; Thörn, Helena; Tannergren, Christer

    2016-06-06

    Gastrointestinal (GI) drug absorption is a complex process determined by formulation, physicochemical and biopharmaceutical factors, and GI physiology. Physiologically based in silico absorption models have emerged as a widely used and promising supplement to traditional in vitro assays and preclinical in vivo studies. However, there remains a lack of comparative studies between different models. The aim of this study was to explore the strengths and limitations of the in silico absorption models Simcyp 13.1, GastroPlus 8.0, and GI-Sim 4.1, with respect to their performance in predicting human intestinal drug absorption. This was achieved by adopting an a priori modeling approach and using well-defined input data for 12 drugs associated with incomplete GI absorption and related challenges in predicting the extent of absorption. This approach better mimics the real situation during formulation development where predictive in silico models would be beneficial. Plasma concentration-time profiles for 44 oral drug administrations were calculated by convolution of model-predicted absorption-time profiles and reported pharmacokinetic parameters. Model performance was evaluated by comparing the predicted plasma concentration-time profiles, Cmax, tmax, and exposure (AUC) with observations from clinical studies. The overall prediction accuracies for AUC, given as the absolute average fold error (AAFE) values, were 2.2, 1.6, and 1.3 for Simcyp, GastroPlus, and GI-Sim, respectively. The corresponding AAFE values for Cmax were 2.2, 1.6, and 1.3, respectively, and those for tmax were 1.7, 1.5, and 1.4, respectively. Simcyp was associated with underprediction of AUC and Cmax; the accuracy decreased with decreasing predicted fabs. A tendency for underprediction was also observed for GastroPlus, but there was no correlation with predicted fabs. There were no obvious trends for over- or underprediction for GI-Sim. The models performed similarly in capturing dependencies on dose and

  8. Mathematical models for indoor radon prediction

    International Nuclear Information System (INIS)

    Malanca, A.; Pessina, V.; Dallara, G.

    1995-01-01

    It is known that the indoor radon (Rn) concentration can be predicted by means of mathematical models. The simplest model relies on two variables only: the Rn source strength and the air exchange rate. In the Lawrence Berkeley Laboratory (LBL) model several environmental parameters are combined into a complex equation; besides, a correlation between the ventilation rate and the Rn entry rate from the soil is admitted. The measurements were carried out using activated carbon canisters. Seventy-five measurements of Rn concentrations were made inside two rooms placed on the second floor of a building block. One of the rooms had a single-glazed window whereas the other room had a double pane window. During three different experimental protocols, the mean Rn concentration was always higher into the room with a double-glazed window. That behavior can be accounted for by the simplest model. A further set of 450 Rn measurements was collected inside a ground-floor room with a grounding well in it. This trend maybe accounted for by the LBL model

  9. Towards predictive models for transitionally rough surfaces

    Science.gov (United States)

    Abderrahaman-Elena, Nabil; Garcia-Mayoral, Ricardo

    2017-11-01

    We analyze and model the previously presented decomposition for flow variables in DNS of turbulence over transitionally rough surfaces. The flow is decomposed into two contributions: one produced by the overlying turbulence, which has no footprint of the surface texture, and one induced by the roughness, which is essentially the time-averaged flow around the surface obstacles, but modulated in amplitude by the first component. The roughness-induced component closely resembles the laminar steady flow around the roughness elements at the same non-dimensional roughness size. For small - yet transitionally rough - textures, the roughness-free component is essentially the same as over a smooth wall. Based on these findings, we propose predictive models for the onset of the transitionally rough regime. Project supported by the Engineering and Physical Sciences Research Council (EPSRC).

  10. PROPRIEDADES TERMOFÍSICAS DE SOLUÇÕES MODELO SIMILARES A CREME DE LEITE THERMOPHYSICAL PROPERTIES OF MODEL SOLUTIONS SIMILAR TO CREAM

    Directory of Open Access Journals (Sweden)

    Silvia Cristina Sobottka Rolim de MOURA

    2001-08-01

    Full Text Available A demanda de creme de leite UHT tem aumentado significativamente. Diversas empresas diversificaram e aumentaram sua produção, visto que o consumidor, cada vez mais exigente, almeja cremes com ampla faixa de teor de gordura. O objetivo do presente trabalho foi determinar a densidade, viscosidade aparente e difusividade térmica, de soluções modelo similares a creme de leite, na faixa de temperatura de 30 a 70°C, estudando a influência do teor de gordura e da temperatura nas propriedades físicas dos produtos. O delineamento estatístico aplicado foi o planejamento 3X5, usando níveis de teor de gordura e temperatura fixos em 15%, 25% e 35%; 30°C, 40°C, 50°C, 60°C e 70°C, respectivamente (STATISTICA 6.0. Manteve-se constante a quantidade de carboidrato e de proteína, ambos em 3%. A densidade foi determinada pelo método de deslocamento de fluidos em picnômetro; a difusividade térmica com base no método de Dickerson e a viscosidade aparente foi determinada em reômetro Rheotest 2.1. Os resultados de cada propriedade foram analisados através de método de superfície de resposta. No caso destas propriedades, os dados obtidos apresentaram resultados significativos, indicando que o modelo representou de forma confiável a variação destas propriedades com a variação da gordura (% e da temperatura (°C.The requirement of UHT cream has been increased considerably. Several industries varied and increased their production, since consumers, more and more exigent, are demanding creams with a wide range of fat content. The objective of the present research was to determine the density, viscosity and thermal diffusivity of model solutions similar to cream. The range of temperature varied from 30°C to 70°C in order to study the influence of fat content and temperature in the physical properties of cream. The statistic method applied was the factorial 3X5 planning, with levels of fat content and temperature fixed in 15%, 25% and 35%; 30

  11. Resource-estimation models and predicted discovery

    International Nuclear Information System (INIS)

    Hill, G.W.

    1982-01-01

    Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)

  12. The positive group affect spiral : a dynamic model of the emergence of positive affective similarity in work groups

    NARCIS (Netherlands)

    Walter, F.; Bruch, H.

    This conceptual paper seeks to clarify the process of the emergence of positive collective affect. Specifically, it develops a dynamic model of the emergence of positive affective similarity in work groups. It is suggested that positive group affective similarity and within-group relationship

  13. SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity.

    Directory of Open Access Journals (Sweden)

    Bo Yao

    Full Text Available Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP. Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

  14. Prediction of pipeline corrosion rate based on grey Markov models

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Peng Guichu; Wang Yuemin

    2009-01-01

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

  15. The role of visual similarity and memory in body model distortions.

    Science.gov (United States)

    Saulton, Aurelie; Longo, Matthew R; Wong, Hong Yu; Bülthoff, Heinrich H; de la Rosa, Stephan

    2016-02-01

    Several studies have shown that the perception of one's own hand size is distorted in proprioceptive localization tasks. It has been suggested that those distortions mirror somatosensory anisotropies. Recent research suggests that non-corporeal items also show some spatial distortions. In order to investigate the psychological processes underlying the localization task, we investigated the influences of visual similarity and memory on distortions observed on corporeal and non-corporeal items. In experiment 1, participants indicated the location of landmarks on: their own hand, a rubber hand (rated as most similar to the real hand), and a rake (rated as least similar to the real hand). Results show no significant differences between rake and rubber hand distortions but both items were significantly less distorted than the hand. Experiments 2 and 3 explored the role of memory in spatial distance judgments of the hand, the rake and the rubber hand. Spatial representations of items measured in experiments 2 and 3 were also distorted but showed the tendency to be smaller than in localization tasks. While memory and visual similarity seem to contribute to explain qualitative similarities in distortions between the hand and non-corporeal items, those factors cannot explain the larger magnitude observed in hand distortions. Copyright © 2015. Published by Elsevier B.V.

  16. An Operational Model for the Prediction of Jet Blast

    Science.gov (United States)

    2012-01-09

    This paper presents an operational model for the prediction of jet blast. The model was : developed based upon three modules including a jet exhaust model, jet centerline decay : model and aircraft motion model. The final analysis was compared with d...

  17. Testing Process Predictions of Models of Risky Choice: A Quantitative Model Comparison Approach

    Directory of Open Access Journals (Sweden)

    Thorsten ePachur

    2013-09-01

    Full Text Available This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or nonlinear functions thereof and the separate evaluation of risky options (expectation models. Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models. We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter, Gigerenzer, & Hertwig, 2006, and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up and direction of search (i.e., gamble-wise vs. reason-wise. In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly; acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988 called similarity. In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies.

  18. Testing process predictions of models of risky choice: a quantitative model comparison approach

    Science.gov (United States)

    Pachur, Thorsten; Hertwig, Ralph; Gigerenzer, Gerd; Brandstätter, Eduard

    2013-01-01

    This article presents a quantitative model comparison contrasting the process predictions of two prominent views on risky choice. One view assumes a trade-off between probabilities and outcomes (or non-linear functions thereof) and the separate evaluation of risky options (expectation models). Another view assumes that risky choice is based on comparative evaluation, limited search, aspiration levels, and the forgoing of trade-offs (heuristic models). We derived quantitative process predictions for a generic expectation model and for a specific heuristic model, namely the priority heuristic (Brandstätter et al., 2006), and tested them in two experiments. The focus was on two key features of the cognitive process: acquisition frequencies (i.e., how frequently individual reasons are looked up) and direction of search (i.e., gamble-wise vs. reason-wise). In Experiment 1, the priority heuristic predicted direction of search better than the expectation model (although neither model predicted the acquisition process perfectly); acquisition frequencies, however, were inconsistent with both models. Additional analyses revealed that these frequencies were primarily a function of what Rubinstein (1988) called “similarity.” In Experiment 2, the quantitative model comparison approach showed that people seemed to rely more on the priority heuristic in difficult problems, but to make more trade-offs in easy problems. This finding suggests that risky choice may be based on a mental toolbox of strategies. PMID:24151472

  19. Data driven propulsion system weight prediction model

    Science.gov (United States)

    Gerth, Richard J.

    1994-10-01

    The objective of the research was to develop a method to predict the weight of paper engines, i.e., engines that are in the early stages of development. The impetus for the project was the Single Stage To Orbit (SSTO) project, where engineers need to evaluate alternative engine designs. Since the SSTO is a performance driven project the performance models for alternative designs were well understood. The next tradeoff is weight. Since it is known that engine weight varies with thrust levels, a model is required that would allow discrimination between engines that produce the same thrust. Above all, the model had to be rooted in data with assumptions that could be justified based on the data. The general approach was to collect data on as many existing engines as possible and build a statistical model of the engines weight as a function of various component performance parameters. This was considered a reasonable level to begin the project because the data would be readily available, and it would be at the level of most paper engines, prior to detailed component design.

  20. Predictive modeling of emergency cesarean delivery.

    Directory of Open Access Journals (Sweden)

    Carlos Campillo-Artero

    Full Text Available To increase discriminatory accuracy (DA for emergency cesarean sections (ECSs.We prospectively collected data on and studied all 6,157 births occurring in 2014 at four public hospitals located in three different autonomous communities of Spain. To identify risk factors (RFs for ECS, we used likelihood ratios and logistic regression, fitted a classification tree (CTREE, and analyzed a random forest model (RFM. We used the areas under the receiver-operating-characteristic (ROC curves (AUCs to assess their DA.The magnitude of the LR+ for all putative individual RFs and ORs in the logistic regression models was low to moderate. Except for parity, all putative RFs were positively associated with ECS, including hospital fixed-effects and night-shift delivery. The DA of all logistic models ranged from 0.74 to 0.81. The most relevant RFs (pH, induction, and previous C-section in the CTREEs showed the highest ORs in the logistic models. The DA of the RFM and its most relevant interaction terms was even higher (AUC = 0.94; 95% CI: 0.93-0.95.Putative fetal, maternal, and contextual RFs alone fail to achieve reasonable DA for ECS. It is the combination of these RFs and the interactions between them at each hospital that make it possible to improve the DA for the type of delivery and tailor interventions through prediction to improve the appropriateness of ECS indications.

  1. Model Predictive Control based on Finite Impulse Response Models

    DEFF Research Database (Denmark)

    Prasath, Guru; Jørgensen, John Bagterp

    2008-01-01

    We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....

  2. Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

    Science.gov (United States)

    Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

    2013-10-01

    Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. Molecular Quantum Similarity Measures from Fermi hole Densities: Modeling Hammett Sigma Constants

    Czech Academy of Sciences Publication Activity Database

    Girónes, X.; Ponec, Robert

    2006-01-01

    Roč. 46, č. 3 (2006), s. 1388-1393 ISSN 1549-9596 Grant - others:SMCT(ES) SAF2000/0223/C03/01 Institutional research plan: CEZ:AV0Z40720504 Keywords : molecula quantum similarity measures * fermi hole densities * substituent effect Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.423, year: 2006

  4. Methodology for Designing Models Predicting Success of Infertility Treatment

    OpenAIRE

    Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

    2016-01-01

    Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

  5. Comparison of the Nosocomial Pneumonia Mortality Prediction (NPMP) model with standard mortality prediction tools.

    Science.gov (United States)

    Srinivasan, M; Shetty, N; Gadekari, S; Thunga, G; Rao, K; Kunhikatta, V

    2017-07-01

    Severity or mortality prediction of nosocomial pneumonia could aid in the effective triage of patients and assisting physicians. To compare various severity assessment scoring systems for predicting intensive care unit (ICU) mortality in nosocomial pneumonia patients. A prospective cohort study was conducted in a tertiary care university-affiliated hospital in Manipal, India. One hundred patients with nosocomial pneumonia, admitted in the ICUs who developed pneumonia after >48h of admission, were included. The Nosocomial Pneumonia Mortality Prediction (NPMP) model, developed in our hospital, was compared with Acute Physiology and Chronic Health Evaluation II (APACHE II), Mortality Probability Model II (MPM 72  II), Simplified Acute Physiology Score II (SAPS II), Multiple Organ Dysfunction Score (MODS), Sequential Organ Failure Assessment (SOFA), Clinical Pulmonary Infection Score (CPIS), Ventilator-Associated Pneumonia Predisposition, Insult, Response, Organ dysfunction (VAP-PIRO). Data and clinical variables were collected on the day of pneumonia diagnosis. The outcome for the study was ICU mortality. The sensitivity and specificity of the various scoring systems was analysed by plotting receiver operating characteristic (ROC) curves and computing the area under the curve for each of the mortality predicting tools. NPMP, APACHE II, SAPS II, MPM 72  II, SOFA, and VAP-PIRO were found to have similar and acceptable discrimination power as assessed by the area under the ROC curve. The AUC values for the above scores ranged from 0.735 to 0.762. CPIS and MODS showed least discrimination. NPMP is a specific tool to predict mortality in nosocomial pneumonia and is comparable to other standard scores. Copyright © 2017 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  6. Finite Unification: Theory, Models and Predictions

    CERN Document Server

    Heinemeyer, S; Zoupanos, G

    2011-01-01

    All-loop Finite Unified Theories (FUTs) are very interesting N=1 supersymmetric Grand Unified Theories (GUTs) realising an old field theory dream, and moreover have a remarkable predictive power due to the required reduction of couplings. The reduction of the dimensionless couplings in N=1 GUTs is achieved by searching for renormalization group invariant (RGI) relations among them holding beyond the unification scale. Finiteness results from the fact that there exist RGI relations among dimensional couplings that guarantee the vanishing of all beta-functions in certain N=1 GUTs even to all orders. Furthermore developments in the soft supersymmetry breaking sector of N=1 GUTs and FUTs lead to exact RGI relations, i.e. reduction of couplings, in this dimensionful sector of the theory, too. Based on the above theoretical framework phenomenologically consistent FUTs have been constructed. Here we review FUT models based on the SU(5) and SU(3)^3 gauge groups and their predictions. Of particular interest is the Hig...

  7. Nonlinear dynamical modeling and prediction of the terrestrial magnetospheric activity

    International Nuclear Information System (INIS)

    Vassiliadis, D.

    1992-01-01

    The irregular activity of the magnetosphere results from its complex internal dynamics as well as the external influence of the solar wind. The dominating self-organization of the magnetospheric plasma gives rise to repetitive, large-scale coherent behavior manifested in phenomena such as the magnetic substorm. Based on the nonlinearity of the global dynamics this dissertation examines the magnetosphere as a nonlinear dynamical system using time series analysis techniques. Initially the magnetospheric activity is modeled in terms of an autonomous system. A dimension study shows that its observed time series is self-similar, but the correlation dimension is high. The implication of a large number of degrees of freedom is confirmed by other state space techniques such as Poincare sections and search for unstable periodic orbits. At the same time a stability study of the time series in terms of Lyapunov exponents suggests that the series is not chaotic. The absence of deterministic chaos is supported by the low predictive capability of the autonomous model. Rather than chaos, it is an external input which is largely responsible for the irregularity of the magnetospheric activity. In fact, the external driving is so strong that the above state space techniques give results for magnetospheric and solar wind time series that are at least qualitatively similar. Therefore the solar wind input has to be included in a low-dimensional nonautonomous model. Indeed it is shown that such a model can reproduce the observed magnetospheric behavior up to 80-90 percent. The characteristic coefficients of the model show little variation depending on the external disturbance. The impulse response is consistent with earlier results of linear prediction filters. The model can be easily extended to contain nonlinear features of the magnetospheric activity and in particular the loading-unloading behavior of substorms

  8. Revised predictive equations for salt intrusion modelling in estuaries

    NARCIS (Netherlands)

    Gisen, J.I.A.; Savenije, H.H.G.; Nijzink, R.C.

    2015-01-01

    For one-dimensional salt intrusion models to be predictive, we need predictive equations to link model parameters to observable hydraulic and geometric variables. The one-dimensional model of Savenije (1993b) made use of predictive equations for the Van der Burgh coefficient $K$ and the dispersion

  9. Neutrino nucleosynthesis in supernovae: Shell model predictions

    International Nuclear Information System (INIS)

    Haxton, W.C.

    1989-01-01

    Almost all of the 3 · 10 53 ergs liberated in a core collapse supernova is radiated as neutrinos by the cooling neutron star. I will argue that these neutrinos interact with nuclei in the ejected shells of the supernovae to produce new elements. It appears that this nucleosynthesis mechanism is responsible for the galactic abundances of 7 Li, 11 B, 19 F, 138 La, and 180 Ta, and contributes significantly to the abundances of about 15 other light nuclei. I discuss shell model predictions for the charged and neutral current allowed and first-forbidden responses of the parent nuclei, as well as the spallation processes that produce the new elements. 18 refs., 1 fig., 1 tab

  10. Hierarchical Model Predictive Control for Resource Distribution

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob

    2010-01-01

    units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...

  11. Distributed model predictive control made easy

    CERN Document Server

    Negenborn, Rudy

    2014-01-01

    The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.   This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...

  12. Model predictive control of a wind turbine modelled in Simpack

    International Nuclear Information System (INIS)

    Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G

    2014-01-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine

  13. Model predictive control of a wind turbine modelled in Simpack

    Science.gov (United States)

    Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.

    2014-06-01

    Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to

  14. Research on Kalman Filtering Algorithm for Deformation Information Series of Similar Single-Difference Model

    Institute of Scientific and Technical Information of China (English)

    L(U) Wei-cai; XU Shao-quan

    2004-01-01

    Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.

  15. Boson mapping of the shell model algebra obtained from a seniority-dictated similarity transformation

    International Nuclear Information System (INIS)

    Geyer, H.B.

    1986-01-01

    The qualitative ideas put forward by Geyer and Lee are given quantitative content by constructing a similarity transformation which reexpresses the Dyson boson images of the single-j shell fermion operators in terms of seniority bosons. It is shown that the results of Otsuka, Arima, and Iachello, or generalizations thereof which include g bosons or even bosons with J>4, can be obtained in an economic and transparent way without resorting to any comparison of matrix elements

  16. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  17. Regional differences in prediction models of lung function in Germany

    Directory of Open Access Journals (Sweden)

    Schäper Christoph

    2010-04-01

    Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.

  18. Predicting future glacial lakes in Austria using different modelling approaches

    Science.gov (United States)

    Otto, Jan-Christoph; Helfricht, Kay; Prasicek, Günther; Buckel, Johannes; Keuschnig, Markus

    2017-04-01

    Glacier retreat is one of the most apparent consequences of temperature rise in the 20th and 21th centuries in the European Alps. In Austria, more than 240 new lakes have formed in glacier forefields since the Little Ice Age. A similar signal is reported from many mountain areas worldwide. Glacial lakes can constitute important environmental and socio-economic impacts on high mountain systems including water resource management, sediment delivery, natural hazards, energy production and tourism. Their development significantly modifies the landscape configuration and visual appearance of high mountain areas. Knowledge on the location, number and extent of these future lakes can be used to assess potential impacts on high mountain geo-ecosystems and upland-lowland interactions. Information on new lakes is critical to appraise emerging threads and potentials for society. The recent development of regional ice thickness models and their combination with high resolution glacier surface data allows predicting the topography below current glaciers by subtracting ice thickness from glacier surface. Analyzing these modelled glacier bed surfaces reveals overdeepenings that represent potential locations for future lakes. In order to predict the location of future glacial lakes below recent glaciers in the Austrian Alps we apply different ice thickness models using high resolution terrain data and glacier outlines. The results are compared and validated with ice thickness data from geophysical surveys. Additionally, we run the models on three different glacier extents provided by the Austrian Glacier Inventories from 1969, 1998 and 2006. Results of this historical glacier extent modelling are compared to existing glacier lakes and discussed focusing on geomorphological impacts on lake evolution. We discuss model performance and observed differences in the results in order to assess the approach for a realistic prediction of future lake locations. The presentation delivers

  19. From epidemics to information propagation : Striking differences in structurally similar adaptive network models

    NARCIS (Netherlands)

    Trajanovski, S.; Guo, D.; Van Mieghem, P.F.A.

    2015-01-01

    The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways:

  20. Predictive integrated modelling for ITER scenarios

    International Nuclear Information System (INIS)

    Artaud, J.F.; Imbeaux, F.; Aniel, T.; Basiuk, V.; Eriksson, L.G.; Giruzzi, G.; Hoang, G.T.; Huysmans, G.; Joffrin, E.; Peysson, Y.; Schneider, M.; Thomas, P.

    2005-01-01

    The uncertainty on the prediction of ITER scenarios is evaluated. 2 transport models which have been extensively validated against the multi-machine database are used for the computation of the transport coefficients. The first model is GLF23, the second called Kiauto is a model in which the profile of dilution coefficient is a gyro Bohm-like analytical function, renormalized in order to get profiles consistent with a given global energy confinement scaling. The package of codes CRONOS is used, it gives access to the dynamics of the discharge and allows the study of interplay between heat transport, current diffusion and sources. The main motivation of this work is to study the influence of parameters such plasma current, heat, density, impurities and toroidal moment transport. We can draw the following conclusions: 1) the target Q = 10 can be obtained in ITER hybrid scenario at I p = 13 MA, using either the DS03 two terms scaling or the GLF23 model based on the same pedestal; 2) I p = 11.3 MA, Q = 10 can be reached only assuming a very peaked pressure profile and a low pedestal; 3) at fixed Greenwald fraction, Q increases with density peaking; 4) achieving a stationary q-profile with q > 1 requires a large non-inductive current fraction (80%) that could be provided by 20 to 40 MW of LHCD; and 5) owing to the high temperature the q-profile penetration is delayed and q = 1 is reached about 600 s in ITER hybrid scenario at I p = 13 MA, in the absence of active q-profile control. (A.C.)

  1. Analyzing and leveraging self-similarity for variable resolution atmospheric models

    Science.gov (United States)

    O'Brien, Travis; Collins, William

    2015-04-01

    Variable resolution modeling techniques are rapidly becoming a popular strategy for achieving high resolution in a global atmospheric models without the computational cost of global high resolution. However, recent studies have demonstrated a variety of resolution-dependent, and seemingly artificial, features. We argue that the scaling properties of the atmosphere are key to understanding how the statistics of an atmospheric model should change with resolution. We provide two such examples. In the first example we show that the scaling properties of the cloud number distribution define how the ratio of resolved to unresolved clouds should increase with resolution. We show that the loss of resolved clouds, in the high resolution region of variable resolution simulations, with the Community Atmosphere Model version 4 (CAM4) is an artifact of the model's treatment of condensed water (this artifact is significantly reduced in CAM5). In the second example we show that the scaling properties of the horizontal velocity field, combined with the incompressibility assumption, necessarily result in an intensification of vertical mass flux as resolution increases. We show that such an increase is present in a wide variety of models, including CAM and the regional climate models of the ENSEMBLES intercomparision. We present theoretical arguments linking this increase to the intensification of precipitation with increasing resolution.

  2. Similarities between the Hubbard and Periodic Anderson Models at Finite Temperatures

    International Nuclear Information System (INIS)

    Held, K.; Huscroft, C.; Scalettar, R. T.; McMahan, A. K.

    2000-01-01

    The single band Hubbard and the two band periodic Anderson Hamiltonians have traditionally been applied to rather different physical problems--the Mott transition and itinerant magnetism, and Kondo singlet formation and scattering off localized magnetic states, respectively. In this paper, we compare the magnetic and charge correlations, and spectral functions, of the two systems. We show quantitatively that they exhibit remarkably similar behavior, including a nearly identical topology of the finite temperature phase diagrams at half filling. We address potential implications of this for theories of the rare earth ''volume collapse'' transition. (c) 2000 The American Physical Society

  3. Models for predicting objective function weights in prostate cancer IMRT

    International Nuclear Information System (INIS)

    Boutilier, Justin J.; Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.

    2015-01-01

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  4. Models for predicting objective function weights in prostate cancer IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8 (Canada); Craig, Tim [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Sharpe, Michael B. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9 (Canada); Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada); Chan, Timothy C. Y. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada)

    2015-04-15

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  5. Building predictive models of soil particle-size distribution

    Directory of Open Access Journals (Sweden)

    Alessandro Samuel-Rosa

    2013-04-01

    Full Text Available Is it possible to build predictive models (PMs of soil particle-size distribution (psd in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index. The PMs explained more than half of the data variance. This performance is similar to (or even better than that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd of soils in regions of complex geology.

  6. An analytic solution of a model of language competition with bilingualism and interlinguistic similarity

    Science.gov (United States)

    Otero-Espinar, M. V.; Seoane, L. F.; Nieto, J. J.; Mira, J.

    2013-12-01

    An in-depth analytic study of a model of language dynamics is presented: a model which tackles the problem of the coexistence of two languages within a closed community of speakers taking into account bilingualism and incorporating a parameter to measure the distance between languages. After previous numerical simulations, the model yielded that coexistence might lead to survival of both languages within monolingual speakers along with a bilingual community or to extinction of the weakest tongue depending on different parameters. In this paper, such study is closed with thorough analytical calculations to settle the results in a robust way and previous results are refined with some modifications. From the present analysis it is possible to almost completely assay the number and nature of the equilibrium points of the model, which depend on its parameters, as well as to build a phase space based on them. Also, we obtain conclusions on the way the languages evolve with time. Our rigorous considerations also suggest ways to further improve the model and facilitate the comparison of its consequences with those from other approaches or with real data.

  7. Modeling the angular motion dynamics of spacecraft with a magnetic attitude control system based on experimental studies and dynamic similarity

    Science.gov (United States)

    Kulkov, V. M.; Medvedskii, A. L.; Terentyev, V. V.; Firsyuk, S. O.; Shemyakov, A. O.

    2017-12-01

    The problem of spacecraft attitude control using electromagnetic systems interacting with the Earth's magnetic field is considered. A set of dimensionless parameters has been formed to investigate the spacecraft orientation regimes based on dynamically similar models. The results of experimental studies of small spacecraft with a magnetic attitude control system can be extrapolated to the in-orbit spacecraft motion control regimes by using the methods of the dimensional and similarity theory.

  8. A Comparative Study of Spectral Auroral Intensity Predictions From Multiple Electron Transport Models

    Science.gov (United States)

    Grubbs, Guy; Michell, Robert; Samara, Marilia; Hampton, Donald; Hecht, James; Solomon, Stanley; Jahn, Jorg-Micha

    2018-01-01

    It is important to routinely examine and update models used to predict auroral emissions resulting from precipitating electrons in Earth's magnetotail. These models are commonly used to invert spectral auroral ground-based images to infer characteristics about incident electron populations when in situ measurements are unavailable. In this work, we examine and compare auroral emission intensities predicted by three commonly used electron transport models using varying electron population characteristics. We then compare model predictions to same-volume in situ electron measurements and ground-based imaging to qualitatively examine modeling prediction error. Initial comparisons showed differences in predictions by the GLobal airglOW (GLOW) model and the other transport models examined. Chemical reaction rates and radiative rates in GLOW were updated using recent publications, and predictions showed better agreement with the other models and the same-volume data, stressing that these rates are important to consider when modeling auroral processes. Predictions by each model exhibit similar behavior for varying atmospheric constants, energies, and energy fluxes. Same-volume electron data and images are highly correlated with predictions by each model, showing that these models can be used to accurately derive electron characteristics and ionospheric parameters based solely on multispectral optical imaging data.

  9. Integrating geophysics and hydrology for reducing the uncertainty of groundwater model predictions and improved prediction performance

    DEFF Research Database (Denmark)

    Christensen, Nikolaj Kruse; Christensen, Steen; Ferre, Ty

    the integration of geophysical data in the construction of a groundwater model increases the prediction performance. We suggest that modelers should perform a hydrogeophysical “test-bench” analysis of the likely value of geophysics data for improving groundwater model prediction performance before actually...... and the resulting predictions can be compared with predictions from the ‘true’ model. By performing this analysis we expect to give the modeler insight into how the uncertainty of model-based prediction can be reduced.......A major purpose of groundwater modeling is to help decision-makers in efforts to manage the natural environment. Increasingly, it is recognized that both the predictions of interest and their associated uncertainties should be quantified to support robust decision making. In particular, decision...

  10. What Models of Verbal Working Memory Can Learn from Phonological Theory: Decomposing the Phonological Similarity Effect

    Science.gov (United States)

    Schweppe, Judith; Grice, Martine; Rummer, Ralf

    2011-01-01

    Despite developments in phonology over the last few decades, models of verbal working memory make reference to phoneme-sized phonological units, rather than to the features of which they are composed. This study investigates the influence on short-term retention of such features by comparing the serial recall of lists of syllables with varying…

  11. Merging tree algorithm of growing voids in self-similar and CDM models

    NARCIS (Netherlands)

    Russell, Esra

    2013-01-01

    Observational studies show that voids are prominent features of the large-scale structure of the present-day Universe. Even though their emerging from the primordial density perturbations and evolutionary patterns differ from dark matter haloes, N-body simulations and theoretical models have shown

  12. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  13. Similar uptake profiles of microcystin-LR and -RR in an in vitro human intestinal model

    International Nuclear Information System (INIS)

    Zeller, P.; Clement, M.; Fessard, V.

    2011-01-01

    Highlights: → First description of in vitro cellular uptake of MCs into intestinal cells. → OATP 3A1 and OATP 4A1 are expressed in Caco-2 cell membranes. → MC-LR and MC-RR show similar uptake in Caco-2 cells. → MCs are probably excreted from Caco-2 cells by an active mechanism. -- Abstract: Microcystins (MCs) are cyclic hepatotoxins produced by various species of cyanobacteria. Their structure includes two variable amino acids (AA) leading to more than 80 MC variants. In this study, we focused on the most common variant, microcystin-LR (MC-LR), and microcystin-RR (MC-RR), a variant differing by only one AA. Despite their structural similarity, MC-LR elicits higher liver toxicity than MC-RR partly due to a discrepancy in their uptake by hepatic organic anion transporters (OATP 1B1 and 1B3). However, even though ingestion is the major pathway of human exposure to MCs, intestinal absorption of MCs has been poorly addressed. Consequently, we investigated the cellular uptake of the two MC variants in the human intestinal cell line Caco-2 by immunolocalization using an anti-MC antibody. Caco-2 cells were treated for 30 min to 24 h with several concentrations (1-50 μM) of both variants. We first confirmed the localization of OATP 3A1 and 4A1 at the cell membrane of Caco-2 cells. Our study also revealed a rapid uptake of both variants in less than 1 h. The uptake profiles of the two variants did not differ in our immunostaining study neither with respect to concentration nor the time of exposure. Furthermore, we have demonstrated for the first time the nuclear localization of MC-RR and confirmed that of MC-LR. Finally, our results suggest a facilitated uptake and an active excretion of MC-LR and MC-RR in Caco-2 cells. Further investigation on the role of OATP 3A1 and 4A1 in MC uptake should be useful to clarify the mechanism of intestinal absorption of MCs and contribute in risk assessment of cyanotoxin exposure.

  14. Foundation Settlement Prediction Based on a Novel NGM Model

    Directory of Open Access Journals (Sweden)

    Peng-Yu Chen

    2014-01-01

    Full Text Available Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM (1,1,k,c model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM (1,1,k,c model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM (1,1,k,c model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

  15. Nonconvex model predictive control for commercial refrigeration

    Science.gov (United States)

    Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John

    2013-08-01

    We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.

  16. Kinematic Hardening: Characterization, Modeling and Impact on Springback Prediction

    International Nuclear Information System (INIS)

    Alves, J. L.; Bouvier, S.; Jomaa, M.; Billardon, R.; Oliveira, M. C.; Menezes, L. F.

    2007-01-01

    The constitutive modeling of the materials' mechanical behavior, usually carried out using a phenomenological constitutive model, i.e., a yield criterion associated to the isotropic and kinematic hardening laws, is of paramount importance in the FEM simulation of the sheet metal forming processes, as well as in the springback prediction. Among others, the kinematic behavior of the yield surface plays an essential role, since it is indispensable to describe the Bauschinger effect, i.e., the materials' answer to the multiple tension-compression cycles to which material points are submitted during the forming process. Several laws are usually used to model and describe the kinematic hardening, namely: a) the Prager's law, which describes a linear evolution of the kinematic hardening with the plastic strain rate tensor b) the Frederick-Armstrong non-linear kinematic hardening, basically a non-linear law with saturation; and c) a more advanced physically-based law, similar to the previous one but sensitive to the strain path changes. In the present paper a mixed kinematic hardening law (linear + non-linear behavior) is proposed and its implementation into a static fully-implicit FE code is described. The material parameters identification for sheet metals using different strategies, and the classical Bauschinger loading tests (i.e. in-plane forward and reverse monotonic loading), are addressed, and their impact on springback prediction evaluated. Some numerical results concerning the springback prediction of the Numisheet'05 Benchmark no. 3 are briefly presented to emphasize the importance of a correct modeling and identification of the kinematic hardening behavior

  17. Predictive Modelling of Heavy Metals in Urban Lakes

    OpenAIRE

    Lindström, Martin

    2000-01-01

    Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes. Sediment and water data for this study were collected from ten small lakes in the ...

  18. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.; Mai, Paul Martin; Thingbaijam, Kiran Kumar; Razafindrakoto, H. N. T.; Genton, Marc G.

    2014-01-01

    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  19. Analysing earthquake slip models with the spatial prediction comparison test

    KAUST Repository

    Zhang, L.

    2014-11-10

    Earthquake rupture models inferred from inversions of geophysical and/or geodetic data exhibit remarkable variability due to uncertainties in modelling assumptions, the use of different inversion algorithms, or variations in data selection and data processing. A robust statistical comparison of different rupture models obtained for a single earthquake is needed to quantify the intra-event variability, both for benchmark exercises and for real earthquakes. The same approach may be useful to characterize (dis-)similarities in events that are typically grouped into a common class of events (e.g. moderate-size crustal strike-slip earthquakes or tsunamigenic large subduction earthquakes). For this purpose, we examine the performance of the spatial prediction comparison test (SPCT), a statistical test developed to compare spatial (random) fields by means of a chosen loss function that describes an error relation between a 2-D field (‘model’) and a reference model. We implement and calibrate the SPCT approach for a suite of synthetic 2-D slip distributions, generated as spatial random fields with various characteristics, and then apply the method to results of a benchmark inversion exercise with known solution. We find the SPCT to be sensitive to different spatial correlations lengths, and different heterogeneity levels of the slip distributions. The SPCT approach proves to be a simple and effective tool for ranking the slip models with respect to a reference model.

  20. Comorbidity predicts poor prognosis in nasopharyngeal carcinoma: Development and validation of a predictive score model

    International Nuclear Information System (INIS)

    Guo, Rui; Chen, Xiao-Zhong; Chen, Lei; Jiang, Feng; Tang, Ling-Long; Mao, Yan-Ping; Zhou, Guan-Qun; Li, Wen-Fei; Liu, Li-Zhi; Tian, Li; Lin, Ai-Hua; Ma, Jun

    2015-01-01

    Background and purpose: The impact of comorbidity on prognosis in nasopharyngeal carcinoma (NPC) is poorly characterized. Material and methods: Using the Adult Comorbidity Evaluation-27 (ACE-27) system, we assessed the prognostic value of comorbidity and developed, validated and confirmed a predictive score model in a training set (n = 658), internal validation set (n = 658) and independent set (n = 652) using area under the receiver operating curve analysis. Results: Comorbidity was present in 40.4% of 1968 patients (mild, 30.1%; moderate, 9.1%; severe, 1.2%). Compared to an ACE-27 score ⩽1, patients with an ACE-27 score >1 in the training set had shorter overall survival (OS) and disease-free survival (DFS) (both P < 0.001), similar results were obtained in the other sets (P < 0.05). In multivariate analysis, ACE-27 score was a significant independent prognostic factor for OS and DFS. The combined risk score model including ACE-27 had superior prognostic value to TNM stage alone in the internal validation set (0.70 vs. 0.66; P = 0.02), independent set (0.73 vs. 0.67; P = 0.002) and all patients (0.71 vs. 0.67; P < 0.001). Conclusions: Comorbidity significantly affects prognosis, especially in stages II and III, and should be incorporated into the TNM staging system for NPC. Assessment of comorbidity may improve outcome prediction and help tailor individualized treatment

  1. Phoneme Similarity and Confusability

    Science.gov (United States)

    Bailey, T.M.; Hahn, U.

    2005-01-01

    Similarity between component speech sounds influences language processing in numerous ways. Explanation and detailed prediction of linguistic performance consequently requires an understanding of these basic similarities. The research reported in this paper contrasts two broad classes of approach to the issue of phoneme similarity-theoretically…

  2. A predictive coding account of bistable perception - a model-based fMRI study.

    Directory of Open Access Journals (Sweden)

    Veith Weilnhammer

    2017-05-01

    Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together

  3. A predictive coding account of bistable perception - a model-based fMRI study.

    Science.gov (United States)

    Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

    2017-05-01

    In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

  4. Seasonal predictability of Kiremt rainfall in coupled general circulation models

    Science.gov (United States)

    Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen

    2017-11-01

    The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.

  5. MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Andrey Borisovich Nikolaev

    2017-09-01

    Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.

  6. More similar than you think: Frog metamorphosis as a model of human perinatal endocrinology.

    Science.gov (United States)

    Buchholz, Daniel R

    2015-12-15

    Hormonal control of development during the human perinatal period is critically important and complex with multiple hormones regulating fetal growth, brain development, and organ maturation in preparation for birth. Genetic and environmental perturbations of such hormonal control may cause irreversible morphological and physiological impairments and may also predispose individuals to diseases of adulthood, including diabetes and cardiovascular disease. Endocrine and molecular mechanisms that regulate perinatal development and that underlie the connections between early life events and adult diseases are not well elucidated. Such mechanisms are difficult to study in uterus-enclosed mammalian embryos because of confounding maternal effects. To elucidate mechanisms of developmental endocrinology in the perinatal period, Xenopus laevis the African clawed frog is a valuable vertebrate model. Frogs and humans have identical hormones which peak at birth and metamorphosis, have conserved hormone receptors and mechanisms of gene regulation, and have comparable roles for hormones in many target organs. Study of molecular and endocrine mechanisms of hormone-dependent development in frogs is advantageous because an extended free-living larval period followed by metamorphosis (1) is independent of maternal endocrine influence, (2) exhibits dramatic yet conserved developmental effects induced by thyroid and glucocorticoid hormones, and (3) begins at a developmental stage with naturally undetectable hormone levels, thereby facilitating endocrine manipulation and interpretation of results. This review highlights the utility of frog metamorphosis to elucidate molecular and endocrine actions, hormone interactions, and endocrine disruption, especially with respect to thyroid hormone. Knowledge from the frog model is expected to provide fundamental insights to aid medical understanding of endocrine disease, stress, and endocrine disruption affecting the perinatal period in humans

  7. MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

    Science.gov (United States)

    Son, S. W.; Lim, Y.; Kim, D.

    2017-12-01

    The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

  8. Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

    NARCIS (Netherlands)

    Schueller, D.; Beecks, C.; Hassani, M.; Hinnell, J.; Brenger, B.; Seidl, T.; Mittelberg, I.

    2017-01-01

    The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative

  9. SIMILARITIES BETWEEN THE KNOWLEDGE CREATION AND CONVERSION MODEL AND THE COMPETING VALUES FRAMEWORK: AN INTEGRATIVE APPROACH

    Directory of Open Access Journals (Sweden)

    PAULO COSTA

    2016-12-01

    Full Text Available ABSTRACT Contemporaneously, and with the successive paradigmatic revolutions inherent to management since the XVII century, we are witnessing a new era marked by the structural rupture in the way organizations are perceived. Market globalization, cemented by quick technological evolutions, associated with economic, cultural, political and social transformations characterize a reality where uncertainty is the only certainty for organizations and managers. Knowledge management has been interpreted by managers and academics as a viable alternative in a logic of creation and conversation of sustainable competitive advantages. However, there are several barriers to the implementation and development of knowledge management programs in organizations, with organizational culture being one of the most preponderant. In this sense, and in this article, we will analyze and compare The Knowledge Creation and Conversion Model proposed by Nonaka and Takeuchi (1995 and Quinn and Rohrbaugh's Competing Values Framework (1983, since both have convergent conceptual lines that can assist managers in different sectors to guide their organization in a perspective of productivity, quality and market competitiveness.

  10. Re-parametrization of a swine model to predict growth performance of broilers

    OpenAIRE

    Dukhta, G.; van Milgen, Jacob; Kövér, G.; Halas, V.

    2017-01-01

    The aim of the study was to investigate whether a pig growth model is suitable to be modified and adapted for broilers. As monogastric animals, pigs and poultry share many similarities in their digestion and metabolism, many structures (body protein and lipid stores) and the nutrient flows of the underlying metabolic pathways are similar among species. For that purpose, the InraPorc model was used as a basis to predict growth performance and body composition at slaughter in broilers. First...

  11. Predictive market segmentation model: An application of logistic regression model and CHAID procedure

    Directory of Open Access Journals (Sweden)

    Soldić-Aleksić Jasna

    2009-01-01

    Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

  12. Butterfly, Recurrence, and Predictability in Lorenz Models

    Science.gov (United States)

    Shen, B. W.

    2017-12-01

    Over the span of 50 years, the original three-dimensional Lorenz model (3DLM; Lorenz,1963) and its high-dimensional versions (e.g., Shen 2014a and references therein) have been used for improving our understanding of the predictability of weather and climate with a focus on chaotic responses. Although the Lorenz studies focus on nonlinear processes and chaotic dynamics, people often apply a "linear" conceptual model to understand the nonlinear processes in the 3DLM. In this talk, we present examples to illustrate the common misunderstandings regarding butterfly effect and discuss the importance of solutions' recurrence and boundedness in the 3DLM and high-dimensional LMs. The first example is discussed with the following folklore that has been widely used as an analogy of the butterfly effect: "For want of a nail, the shoe was lost.For want of a shoe, the horse was lost.For want of a horse, the rider was lost.For want of a rider, the battle was lost.For want of a battle, the kingdom was lost.And all for the want of a horseshoe nail."However, in 2008, Prof. Lorenz stated that he did not feel that this verse described true chaos but that it better illustrated the simpler phenomenon of instability; and that the verse implicitly suggests that subsequent small events will not reverse the outcome (Lorenz, 2008). Lorenz's comments suggest that the verse neither describes negative (nonlinear) feedback nor indicates recurrence, the latter of which is required for the appearance of a butterfly pattern. The second example is to illustrate that the divergence of two nearby trajectories should be bounded and recurrent, as shown in Figure 1. Furthermore, we will discuss how high-dimensional LMs were derived to illustrate (1) negative nonlinear feedback that stabilizes the system within the five- and seven-dimensional LMs (5D and 7D LMs; Shen 2014a; 2015a; 2016); (2) positive nonlinear feedback that destabilizes the system within the 6D and 8D LMs (Shen 2015b; 2017); and (3

  13. Auditing predictive models : a case study in crop growth

    NARCIS (Netherlands)

    Metselaar, K.

    1999-01-01

    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  14. Models for predicting compressive strength and water absorption of ...

    African Journals Online (AJOL)

    This work presents a mathematical model for predicting the compressive strength and water absorption of laterite-quarry dust cement block using augmented Scheffe's simplex lattice design. The statistical models developed can predict the mix proportion that will yield the desired property. The models were tested for lack of ...

  15. Two Echelon Supply Chain Integrated Inventory Model for Similar Products: A Case Study

    Science.gov (United States)

    Parjane, Manoj Baburao; Dabade, Balaji Marutirao; Gulve, Milind Bhaskar

    2017-06-01

    The purpose of this paper is to develop a mathematical model towards minimization of total cost across echelons in a multi-product supply chain environment. The scenario under consideration is a two-echelon supply chain system with one manufacturer, one retailer and M products. The retailer faces independent Poisson demand for each product. The retailer and the manufacturer are closely coupled in the sense that the information about any depletion in the inventory of a product at a retailer's end is immediately available to the manufacturer. Further, stock-out is backordered at the retailer's end. Thus the costs incurred at the retailer's end are the holding costs and the backorder costs. The manufacturer has only one processor which is time shared among the M products. Production changeover from one product to another entails a fixed setup cost and a fixed set up time. Each unit of a product has a production time. Considering the cost components, and assuming transportation time and cost to be negligible, the objective of the study is to minimize the expected total cost considering both the manufacturer and retailer. In the process two aspects are to be defined. Firstly, every time a product is taken up for production, how much of it (production batch size, q) should be produced. Considering a large value of q favors the manufacturer while a small value of q suits the retailers. Secondly, for a given batch size q, at what level of retailer's inventory (production queuing point), the batch size S of a product be taken up for production by the manufacturer. A higher value of S incurs more holding cost whereas a lower value of S increases the chance of backorder. A tradeoff between the holding and backorder cost must be taken into consideration while choosing an optimal value of S. It may be noted that due to multiple products and single processor, a product `taken' up for production may not get the processor immediately, and may have to wait in a queue. The `S

  16. Statistical and Machine Learning Models to Predict Programming Performance

    OpenAIRE

    Bergin, Susan

    2006-01-01

    This thesis details a longitudinal study on factors that influence introductory programming success and on the development of machine learning models to predict incoming student performance. Although numerous studies have developed models to predict programming success, the models struggled to achieve high accuracy in predicting the likely performance of incoming students. Our approach overcomes this by providing a machine learning technique, using a set of three significant...

  17. Probabilistic Modeling and Visualization for Bankruptcy Prediction

    DEFF Research Database (Denmark)

    Antunes, Francisco; Ribeiro, Bernardete; Pereira, Francisco Camara

    2017-01-01

    In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful...... studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian Processes (GP) in the context of bankruptcy prediction, comparing it against the Support Vector Machines (SVM) and the Logistic Regression (LR......). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical...

  18. A touch-probe path generation method through similarity analysis between the feature vectors in new and old models

    Energy Technology Data Exchange (ETDEWEB)

    Jeon, Hye Sung; Lee, Jin Won; Yang, Jeong Sam [Dept. of Industrial Engineering, Ajou University, Suwon (Korea, Republic of)

    2016-10-15

    The On-machine measurement (OMM), which measures a work piece during or after the machining process in the machining center, has the advantage of measuring the work piece directly within the work space without moving it. However, the path generation procedure used to determine the measuring sequence and variables for the complex features of a target work piece has the limitation of requiring time-consuming tasks to generate the measuring points and mostly relies on the proficiency of the on-site engineer. In this study, we propose a touch-probe path generation method using similarity analysis between the feature vectors of three-dimensional (3-D) shapes for the OMM. For the similarity analysis between a new 3-D model and existing 3-D models, we extracted the feature vectors from models that can describe the characteristics of a geometric shape model; then, we applied those feature vectors to a geometric histogram that displays a probability distribution obtained by the similarity analysis algorithm. In addition, we developed a computer-aided inspection planning system that corrects non-applied measuring points that are caused by minute geometry differences between the two models and generates the final touch-probe path.

  19. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    Science.gov (United States)

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  20. Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

    Directory of Open Access Journals (Sweden)

    Elizabeth A. Becker

    2016-02-01

    Full Text Available Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”, but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE, observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

  1. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...... of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset...

  2. Explicit Modeling of Ancestry Improves Polygenic Risk Scores and BLUP Prediction.

    Science.gov (United States)

    Chen, Chia-Yen; Han, Jiali; Hunter, David J; Kraft, Peter; Price, Alkes L

    2015-09-01

    Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color (HC), tanning ability (TA), and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRSs) and best linear unbiased prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R(2) for HC increased by 66% (0.0456-0.0755; P ancestry, which prevents ancestry effects from entering into each SNP effect and being overweighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be underweighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction. © 2015 WILEY PERIODICALS, INC.

  3. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

    Science.gov (United States)

    Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

    2018-03-14

    It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

  4. Assessing the similarity of mental models of operating room team members and implications for patient safety: a prospective, replicated study.

    Science.gov (United States)

    Nakarada-Kordic, Ivana; Weller, Jennifer M; Webster, Craig S; Cumin, David; Frampton, Christopher; Boyd, Matt; Merry, Alan F

    2016-08-31

    Patient safety depends on effective teamwork. The similarity of team members' mental models - or their shared understanding-regarding clinical tasks is likely to influence the effectiveness of teamwork. Mental models have not been measured in the complex, high-acuity environment of the operating room (OR), where professionals of different backgrounds must work together to achieve the best surgical outcome for each patient. Therefore, we aimed to explore the similarity of mental models of task sequence and of responsibility for task within multidisciplinary OR teams. We developed a computer-based card sorting tool (Momento) to capture the information on mental models in 20 six-person surgical teams, each comprised of three subteams (anaesthesia, surgery, and nursing) for two simulated laparotomies. Team members sorted 20 cards depicting key tasks according to when in the procedure each task should be performed, and which subteam was primarily responsible for each task. Within each OR team and subteam, we conducted pairwise comparisons of scores to arrive at mean similarity scores for each task. Mean similarity score for task sequence was 87 % (range 57-97 %). Mean score for responsibility for task was 70 % (range = 38-100 %), but for half of the tasks was only 51 % (range = 38-69 %). Participants believed their own subteam was primarily responsible for approximately half the tasks in each procedure. We found differences in the mental models of some OR team members about responsibility for and order of certain tasks in an emergency laparotomy. Momento is a tool that could help elucidate and better align the mental models of OR team members about surgical procedures and thereby improve teamwork and outcomes for patients.

  5. From Predictive Models to Instructional Policies

    Science.gov (United States)

    Rollinson, Joseph; Brunskill, Emma

    2015-01-01

    At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way…

  6. Nudging and predictability in regional climate modelling: investigation in a nested quasi-geostrophic model

    Science.gov (United States)

    Omrani, Hiba; Drobinski, Philippe; Dubos, Thomas

    2010-05-01

    In this work, we consider the effect of indiscriminate and spectral nudging on the large and small scales of an idealized model simulation. The model is a two layer quasi-geostrophic model on the beta-plane driven at its boundaries by the « global » version with periodic boundary condition. This setup mimics the configuration used for regional climate modelling. The effect of large-scale nudging is studied by using the "perfect model" approach. Two sets of experiments are performed: (1) the effect of nudging is investigated with a « global » high resolution two layer quasi-geostrophic model driven by a low resolution two layer quasi-geostrophic model. (2) similar simulations are conducted with the two layer quasi-geostrophic Limited Area Model (LAM) where the size of the LAM domain comes into play in addition to the first set of simulations. The study shows that the indiscriminate nudging time that minimizes the error at both the large and small scales is reached for a nudging time close to the predictability time, for spectral nudging, the optimum nudging time should tend to zero since the best large scale dynamics is supposed to be given by the driving large-scale fields are generally given at much lower frequency than the model time step(e,g, 6-hourly analysis) with a basic interpolation between the fields, the optimum nudging time differs from zero, however remaining smaller than the predictability time.

  7. The Complexity of Developmental Predictions from Dual Process Models

    Science.gov (United States)

    Stanovich, Keith E.; West, Richard F.; Toplak, Maggie E.

    2011-01-01

    Drawing developmental predictions from dual-process theories is more complex than is commonly realized. Overly simplified predictions drawn from such models may lead to premature rejection of the dual process approach as one of many tools for understanding cognitive development. Misleading predictions can be avoided by paying attention to several…

  8. Sweat loss prediction using a multi-model approach.

    Science.gov (United States)

    Xu, Xiaojiang; Santee, William R

    2011-07-01

    A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

  9. [Establishment of the mathematic model of total quantum statistical moment standard similarity for application to medical theoretical research].

    Science.gov (United States)

    He, Fu-yuan; Deng, Kai-wen; Huang, Sheng; Liu, Wen-long; Shi, Ji-lian

    2013-09-01

    The paper aims to elucidate and establish a new mathematic model: the total quantum statistical moment standard similarity (TQSMSS) on the base of the original total quantum statistical moment model and to illustrate the application of the model to medical theoretical research. The model was established combined with the statistical moment principle and the normal distribution probability density function properties, then validated and illustrated by the pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical method for them, and by analysis of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving the Buyanghanwu-decoction extract. The established model consists of four mainly parameters: (1) total quantum statistical moment similarity as ST, an overlapped area by two normal distribution probability density curves in conversion of the two TQSM parameters; (2) total variability as DT, a confidence limit of standard normal accumulation probability which is equal to the absolute difference value between the two normal accumulation probabilities within integration of their curve nodical; (3) total variable probability as 1-Ss, standard normal distribution probability within interval of D(T); (4) total variable probability (1-beta)alpha and (5) stable confident probability beta(1-alpha): the correct probability to make positive and negative conclusions under confident coefficient alpha. With the model, we had analyzed the TQSMS similarities of pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical methods for them were at range of 0.3852-0.9875 that illuminated different pharmacokinetic behaviors of each other; and the TQSMS similarities (ST) of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving Buyanghuanwu-decoction-extract were at range of 0.6842-0.999 2 that showed different constituents

  10. Comparisons of Faulting-Based Pavement Performance Prediction Models

    Directory of Open Access Journals (Sweden)

    Weina Wang

    2017-01-01

    Full Text Available Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR model, artificial neural network (ANN model, and Markov Chain (MC model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

  11. Comparing predictive models of glioblastoma multiforme built using multi-institutional and local data sources.

    Science.gov (United States)

    Singleton, Kyle W; Hsu, William; Bui, Alex A T

    2012-01-01

    The growing amount of electronic data collected from patient care and clinical trials is motivating the creation of national repositories where multiple institutions share data about their patient cohorts. Such efforts aim to provide sufficient sample sizes for data mining and predictive modeling, ultimately improving treatment recommendations and patient outcome prediction. While these repositories offer the potential to improve our understanding of a disease, potential issues need to be addressed to ensure that multi-site data and resultant predictive models are useful to non-contributing institutions. In this paper we examine the challenges of utilizing National Cancer Institute datasets for modeling glioblastoma multiforme. We created several types of prognostic models and compared their results against models generated using data solely from our institution. While overall model performance between the data sources was similar, different variables were selected during model generation, suggesting that mapping data resources between models is not a straightforward issue.

  12. Different relationships between temporal phylogenetic turnover and phylogenetic similarity and in two forests were detected by a new null model.

    Science.gov (United States)

    Huang, Jian-Xiong; Zhang, Jian; Shen, Yong; Lian, Ju-yu; Cao, Hong-lin; Ye, Wan-hui; Wu, Lin-fang; Bin, Yue

    2014-01-01

    Ecologists have been monitoring community dynamics with the purpose of understanding the rates and causes of community change. However, there is a lack of monitoring of community dynamics from the perspective of phylogeny. We attempted to understand temporal phylogenetic turnover in a 50 ha tropical forest (Barro Colorado Island, BCI) and a 20 ha subtropical forest (Dinghushan in southern China, DHS). To obtain temporal phylogenetic turnover under random conditions, two null models were used. The first shuffled names of species that are widely used in community phylogenetic analyses. The second simulated demographic processes with careful consideration on the variation in dispersal ability among species and the variations in mortality both among species and among size classes. With the two models, we tested the relationships between temporal phylogenetic turnover and phylogenetic similarity at different spatial scales in the two forests. Results were more consistent with previous findings using the second null model suggesting that the second null model is more appropriate for our purposes. With the second null model, a significantly positive relationship was detected between phylogenetic turnover and phylogenetic similarity in BCI at a 10 m×10 m scale, potentially indicating phylogenetic density dependence. This relationship in DHS was significantly negative at three of five spatial scales. This could indicate abiotic filtering processes for community assembly. Using variation partitioning, we found phylogenetic similarity contributed to variation in temporal phylogenetic turnover in the DHS plot but not in BCI plot. The mechanisms for community assembly in BCI and DHS vary from phylogenetic perspective. Only the second null model detected this difference indicating the importance of choosing a proper null model.

  13. Modeling of Complex Life Cycle Prediction Based on Cell Division

    Directory of Open Access Journals (Sweden)

    Fucheng Zhang

    2017-01-01

    Full Text Available Effective fault diagnosis and reasonable life expectancy are of great significance and practical engineering value for the safety, reliability, and maintenance cost of equipment and working environment. At present, the life prediction methods of the equipment are equipment life prediction based on condition monitoring, combined forecasting model, and driven data. Most of them need to be based on a large amount of data to achieve the problem. For this issue, we propose learning from the mechanism of cell division in the organism. We have established a moderate complexity of life prediction model across studying the complex multifactor correlation life model. In this paper, we model the life prediction of cell division. Experiments show that our model can effectively simulate the state of cell division. Through the model of reference, we will use it for the equipment of the complex life prediction.

  14. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  15. Predictive modeling and reducing cyclic variability in autoignition engines

    Science.gov (United States)

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

    2016-08-30

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

  16. Dynamic Simulation of Human Gait Model With Predictive Capability.

    Science.gov (United States)

    Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

    2018-03-01

    In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

  17. Comparative Evaluation of Some Crop Yield Prediction Models ...

    African Journals Online (AJOL)

    A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...

  18. Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

    Science.gov (United States)

    Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

    2013-01-01

    Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

  19. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  20. Ocean wave prediction using numerical and neural network models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...

  1. Bayesian Poisson hierarchical models for crash data analysis: Investigating the impact of model choice on site-specific predictions.

    Science.gov (United States)

    Khazraee, S Hadi; Johnson, Valen; Lord, Dominique

    2018-08-01

    The Poisson-gamma (PG) and Poisson-lognormal (PLN) regression models are among the most popular means for motor vehicle crash data analysis. Both models belong to the Poisson-hierarchical family of models. While numerous studies have compared the overall performance of alternative Bayesian Poisson-hierarchical models, little research has addressed the impact of model choice on the expected crash frequency prediction at individual sites. This paper sought to examine whether there are any trends among candidate models predictions e.g., that an alternative model's prediction for sites with certain conditions tends to be higher (or lower) than that from another model. In addition to the PG and PLN models, this research formulated a new member of the Poisson-hierarchical family of models: the Poisson-inverse gamma (PIGam). Three field datasets (from Texas, Michigan and Indiana) covering a wide range of over-dispersion characteristics were selected for analysis. This study demonstrated that the model choice can be critical when the calibrated models are used for prediction at new sites, especially when the data are highly over-dispersed. For all three datasets, the PIGam model would predict higher expected crash frequencies than would the PLN and PG models, in order, indicating a clear link between the models predictions and the shape of their mixing distributions (i.e., gamma, lognormal, and inverse gamma, respectively). The thicker tail of the PIGam and PLN models (in order) may provide an advantage when the data are highly over-dispersed. The analysis results also illustrated a major deficiency of the Deviance Information Criterion (DIC) in comparing the goodness-of-fit of hierarchical models; models with drastically different set of coefficients (and thus predictions for new sites) may yield similar DIC values, because the DIC only accounts for the parameters in the lowest (observation) level of the hierarchy and ignores the higher levels (regression coefficients

  2. A mathematical model for predicting earthquake occurrence ...

    African Journals Online (AJOL)

    We consider the continental crust under damage. We use the observed results of microseism in many seismic stations of the world which was established to study the time series of the activities of the continental crust with a view to predicting possible time of occurrence of earthquake. We consider microseism time series ...

  3. Model for predicting the injury severity score.

    Science.gov (United States)

    Hagiwara, Shuichi; Oshima, Kiyohiro; Murata, Masato; Kaneko, Minoru; Aoki, Makoto; Kanbe, Masahiko; Nakamura, Takuro; Ohyama, Yoshio; Tamura, Jun'ichi

    2015-07-01

    To determine the formula that predicts the injury severity score from parameters that are obtained in the emergency department at arrival. We reviewed the medical records of trauma patients who were transferred to the emergency department of Gunma University Hospital between January 2010 and December 2010. The injury severity score, age, mean blood pressure, heart rate, Glasgow coma scale, hemoglobin, hematocrit, red blood cell count, platelet count, fibrinogen, international normalized ratio of prothrombin time, activated partial thromboplastin time, and fibrin degradation products, were examined in those patients on arrival. To determine the formula that predicts the injury severity score, multiple linear regression analysis was carried out. The injury severity score was set as the dependent variable, and the other parameters were set as candidate objective variables. IBM spss Statistics 20 was used for the statistical analysis. Statistical significance was set at P  Watson ratio was 2.200. A formula for predicting the injury severity score in trauma patients was developed with ordinary parameters such as fibrin degradation products and mean blood pressure. This formula is useful because we can predict the injury severity score easily in the emergency department.

  4. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  5. Personality Similarity and Work-Related Outcomes among African-American Nursing Personnel: A Test of the Supplementary Model of Person-Environment Congruence.

    Science.gov (United States)

    Day, David V.; Bedeian, Arthur G.

    1995-01-01

    Data from 206 nursing service employees (171 African American) and a 5-factor taxonomy of personality were used to test effects of personality similarity on job satisfaction, performance, and tenure. Tenure was significantly predicted by satisfaction and similarity in conscientiousness. No association was found between personality similarity and…

  6. Accuracy of some simple models for predicting particulate interception and retention in agricultural systems

    International Nuclear Information System (INIS)

    Pinder, J.E. III; McLeod, K.W.; Adriano, D.C.

    1989-01-01

    The accuracy of three radionuclide transfer models for predicting the interception and retention of airborne particles by agricultural crops was tested using Pu-bearing aerosols released to the atmosphere from nuclear fuel facilities on the U.S. Department of Energy's Savannah River Plant, near Aiken, SC. The models evaluated were: (1) NRC, the model defined in U.S. Nuclear Regulatory Guide 1.109; (2) FOOD, a model similar to the NRC model that also predicts concentrations in grains; and (3) AGNS, a model developed from the NRC model for the southeastern United States. Plutonium concentrations in vegetation and grain were predicted from measured deposition rates and compared to concentrations observed in the field. Crops included wheat, soybeans, corn and cabbage. Although predictions of the three models differed by less than a factor of 4, they showed different abilities to predict concentrations observed in the field. The NRC and FOOD models consistently underpredicted the observed Pu concentrations for vegetation. The AGNS model was a more accurate predictor of Pu concentrations for vegetation. Both the FOOD and AGNS models accurately predicted the Pu concentrations for grains

  7. Statistical model based gender prediction for targeted NGS clinical panels

    Directory of Open Access Journals (Sweden)

    Palani Kannan Kandavel

    2017-12-01

    The reference test dataset are being used to test the model. The sensitivity on predicting the gender has been increased from the current “genotype composition in ChrX” based approach. In addition, the prediction score given by the model can be used to evaluate the quality of clinical dataset. The higher prediction score towards its respective gender indicates the higher quality of sequenced data.

  8. A predictive pilot model for STOL aircraft landing

    Science.gov (United States)

    Kleinman, D. L.; Killingsworth, W. R.

    1974-01-01

    An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.

  9. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  10. Model-based uncertainty in species range prediction

    DEFF Research Database (Denmark)

    Pearson, R. G.; Thuiller, Wilfried; Bastos Araujo, Miguel

    2006-01-01

    Aim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions...

  11. Wind turbine control and model predictive control for uncertain systems

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz

    as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...

  12. Testing and analysis of internal hardwood log defect prediction models

    Science.gov (United States)

    R. Edward Thomas

    2011-01-01

    The severity and location of internal defects determine the quality and value of lumber sawn from hardwood logs. Models have been developed to predict the size and position of internal defects based on external defect indicator measurements. These models were shown to predict approximately 80% of all internal knots based on external knot indicators. However, the size...

  13. Comparison of Simple Versus Performance-Based Fall Prediction Models

    Directory of Open Access Journals (Sweden)

    Shekhar K. Gadkaree BS

    2015-05-01

    Full Text Available Objective: To compare the predictive ability of standard falls prediction models based on physical performance assessments with more parsimonious prediction models based on self-reported data. Design: We developed a series of fall prediction models progressing in complexity and compared area under the receiver operating characteristic curve (AUC across models. Setting: National Health and Aging Trends Study (NHATS, which surveyed a nationally representative sample of Medicare enrollees (age ≥65 at baseline (Round 1: 2011-2012 and 1-year follow-up (Round 2: 2012-2013. Participants: In all, 6,056 community-dwelling individuals participated in Rounds 1 and 2 of NHATS. Measurements: Primary outcomes were 1-year incidence of “ any fall ” and “ recurrent falls .” Prediction models were compared and validated in development and validation sets, respectively. Results: A prediction model that included demographic information, self-reported problems with balance and coordination, and previous fall history was the most parsimonious model that optimized AUC for both any fall (AUC = 0.69, 95% confidence interval [CI] = [0.67, 0.71] and recurrent falls (AUC = 0.77, 95% CI = [0.74, 0.79] in the development set. Physical performance testing provided a marginal additional predictive value. Conclusion: A simple clinical prediction model that does not include physical performance testing could facilitate routine, widespread falls risk screening in the ambulatory care setting.

  14. Models for predicting fuel consumption in sagebrush-dominated ecosystems

    Science.gov (United States)

    Clinton S. Wright

    2013-01-01

    Fuel consumption predictions are necessary to accurately estimate or model fire effects, including pollutant emissions during wildland fires. Fuel and environmental measurements on a series of operational prescribed fires were used to develop empirical models for predicting fuel consumption in big sagebrush (Artemisia tridentate Nutt.) ecosystems....

  15. Refining the Committee Approach and Uncertainty Prediction in Hydrological Modelling

    NARCIS (Netherlands)

    Kayastha, N.

    2014-01-01

    Due to the complexity of hydrological systems a single model may be unable to capture the full range of a catchment response and accurately predict the streamflows. The multi modelling approach opens up possibilities for handling such difficulties and allows improve the predictive capability of

  16. A new, accurate predictive model for incident hypertension

    DEFF Research Database (Denmark)

    Völzke, Henry; Fung, Glenn; Ittermann, Till

    2013-01-01

    Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures.......Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures....

  17. Prediction models for successful external cephalic version: a systematic review

    NARCIS (Netherlands)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M.; Molkenboer, Jan F. M.; van der Post, Joris A. M.; Mol, Ben W.; Kok, Marjolein

    2015-01-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015.

  18. Hidden Markov Model for quantitative prediction of snowfall

    Indian Academy of Sciences (India)

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

  19. Mathematical model for dissolved oxygen prediction in Cirata ...

    African Journals Online (AJOL)

    This paper presents the implementation and performance of mathematical model to predict theconcentration of dissolved oxygen in Cirata Reservoir, West Java by using Artificial Neural Network (ANN). The simulation program was created using Visual Studio 2012 C# software with ANN model implemented in it. Prediction ...

  20. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  1. Genomic prediction based on data from three layer lines using non-linear regression models.

    Science.gov (United States)

    Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

    2014-11-06

    Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

  2. PEEX Modelling Platform for Seamless Environmental Prediction

    Science.gov (United States)

    Baklanov, Alexander; Mahura, Alexander; Arnold, Stephen; Makkonen, Risto; Petäjä, Tuukka; Kerminen, Veli-Matti; Lappalainen, Hanna K.; Ezau, Igor; Nuterman, Roman; Zhang, Wen; Penenko, Alexey; Gordov, Evgeny; Zilitinkevich, Sergej; Kulmala, Markku

    2017-04-01

    The Pan-Eurasian EXperiment (PEEX) is a multidisciplinary, multi-scale research programme stared in 2012 and aimed at resolving the major uncertainties in Earth System Science and global sustainability issues concerning the Arctic and boreal Northern Eurasian regions and in China. Such challenges include climate change, air quality, biodiversity loss, chemicalization, food supply, and the use of natural resources by mining, industry, energy production and transport. The research infrastructure introduces the current state of the art modeling platform and observation systems in the Pan-Eurasian region and presents the future baselines for the coherent and coordinated research infrastructures in the PEEX domain. The PEEX modeling Platform is characterized by a complex seamless integrated Earth System Modeling (ESM) approach, in combination with specific models of different processes and elements of the system, acting on different temporal and spatial scales. The ensemble approach is taken to the integration of modeling results from different models, participants and countries. PEEX utilizes the full potential of a hierarchy of models: scenario analysis, inverse modeling, and modeling based on measurement needs and processes. The models are validated and constrained by available in-situ and remote sensing data of various spatial and temporal scales using data assimilation and top-down modeling. The analyses of the anticipated large volumes of data produced by available models and sensors will be supported by a dedicated virtual research environment developed for these purposes.

  3. Impact of modellers' decisions on hydrological a priori predictions

    Science.gov (United States)

    Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

    2014-06-01

    In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

  4. Adding propensity scores to pure prediction models fails to improve predictive performance

    Directory of Open Access Journals (Sweden)

    Amy S. Nowacki

    2013-08-01

    Full Text Available Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1 concordance indices; (2 Brier scores; and (3 calibration curves.Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.

  5. Function Modelling Of The Market And Assessing The Degree Of Similarity Between Real Properties - Dependent Or Independent Procedures In The Process Of Office Property Valuation

    Directory of Open Access Journals (Sweden)

    Barańska Anna

    2015-09-01

    Full Text Available Referring to the developed and presented in previous publications (e.g. Barańska 2011 two-stage algorithm for real estate valuation, this article addresses the problem of the relationship between the two stages of the algorithm. An essential part of the first stage is the multi-dimensional function modelling of the real estate market. As a result of selecting the model best fitted to the market data, in which the dependent variable is always the price of a real property, a set of market attributes is obtained, which in this model are considered to be price-determining. In the second stage, from the collection of real estate which served as a database in the process of estimating model parameters, the selected objects are those which are most similar to the one subject to valuation and form the basis for predicting the final value of the property being valued. Assessing the degree of similarity between real properties can be carried out based on the full spectrum of real estate attributes that potentially affect their value and which it is possible to gather information about, or only on the basis of those attributes which were considered to be price-determining in function modelling. It can also be performed by various methods. This article has examined the effect of various approaches on the final value of the property obtained using the two-stage prediction. In order fulfill the study aim precisely as possible, the results of each calculation step of the algorithm have been investigated in detail. Each of them points to the independence of the two procedures.

  6. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Directory of Open Access Journals (Sweden)

    Jiří Štefanica

    2014-02-01

    Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

  7. Complex versus simple models: ion-channel cardiac toxicity prediction.

    Science.gov (United States)

    Mistry, Hitesh B

    2018-01-01

    There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model B net was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the B net model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  8. Complex versus simple models: ion-channel cardiac toxicity prediction

    Directory of Open Access Journals (Sweden)

    Hitesh B. Mistry

    2018-02-01

    Full Text Available There is growing interest in applying detailed mathematical models of the heart for ion-channel related cardiac toxicity prediction. However, a debate as to whether such complex models are required exists. Here an assessment in the predictive performance between two established large-scale biophysical cardiac models and a simple linear model Bnet was conducted. Three ion-channel data-sets were extracted from literature. Each compound was designated a cardiac risk category using two different classification schemes based on information within CredibleMeds. The predictive performance of each model within each data-set for each classification scheme was assessed via a leave-one-out cross validation. Overall the Bnet model performed equally as well as the leading cardiac models in two of the data-sets and outperformed both cardiac models on the latest. These results highlight the importance of benchmarking complex versus simple models but also encourage the development of simple models.

  9. Fold-recognition and comparative modeling of human α2,3-sialyltransferases reveal their sequence and structural similarities to CstII from Campylobacter jejuni

    Directory of Open Access Journals (Sweden)

    Balaji Petety V

    2006-04-01

    Full Text Available Abstract Background The 3-D structure of none of the eukaryotic sialyltransferases (SiaTs has been determined so far. Sequence alignment algorithms such as BLAST and PSI-BLAST could not detect a homolog of these enzymes from the protein databank. SiaTs, thus, belong to the hard/medium target category in the CASP experiments. The objective of the current work is to model the 3-D structures of human SiaTs which transfer the sialic acid in α2,3-linkage viz., ST3Gal I, II, III, IV, V, and VI, using fold-recognition and comparative modeling methods. The pair-wise sequence similarity among these six enzymes ranges from 41 to 63%. Results Unlike the sequence similarity servers, fold-recognition servers identified CstII, a α2,3/8 dual-activity SiaT from Campylobacter jejuni as the homolog of all the six ST3Gals; the level of sequence similarity between CstII and ST3Gals is only 15–20% and the similarity is restricted to well-characterized motif regions of ST3Gals. Deriving template-target sequence alignments for the entire ST3Gal sequence was not straightforward: the fold-recognition servers could not find a template for the region preceding the L-motif and that between the L- and S-motifs. Multiple structural templates were identified to model these regions and template identification-modeling-evaluation had to be performed iteratively to choose the most appropriate templates. The modeled structures have acceptable stereochemical properties and are also able to provide qualitative rationalizations for some of the site-directed mutagenesis results reported in literature. Apart from the predicted models, an unexpected but valuable finding from this study is the sequential and structural relatedness of family GT42 and family GT29 SiaTs. Conclusion The modeled 3-D structures can be used for docking and other modeling studies and for the rational identification of residues to be mutated to impart desired properties such as altered stability, substrate

  10. [Application of ARIMA model on prediction of malaria incidence].

    Science.gov (United States)

    Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

    2016-01-29

    To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

  11. The perfectionism model of binge eating: testing unique contributions, mediating mechanisms, and cross-cultural similarities using a daily diary methodology.

    Science.gov (United States)

    Sherry, Simon B; Sabourin, Brigitte C; Hall, Peter A; Hewitt, Paul L; Flett, Gordon L; Gralnick, Tara M

    2014-12-01

    The perfectionism model of binge eating (PMOBE) is an integrative model explaining the link between perfectionism and binge eating. This model proposes socially prescribed perfectionism confers risk for binge eating by generating exposure to 4 putative binge triggers: interpersonal discrepancies, low interpersonal esteem, depressive affect, and dietary restraint. The present study addresses important gaps in knowledge by testing if these 4 binge triggers uniquely predict changes in binge eating on a daily basis and if daily variations in each binge trigger mediate the link between socially prescribed perfectionism and daily binge eating. Analyses also tested if proposed mediational models generalized across Asian and European Canadians. The PMOBE was tested in 566 undergraduate women using a 7-day daily diary methodology. Depressive affect predicted binge eating, whereas anxious affect did not. Each binge trigger uniquely contributed to binge eating on a daily basis. All binge triggers except for dietary restraint mediated the relationship between socially prescribed perfectionism and change in daily binge eating. Results suggested cross-cultural similarities, with the PMOBE applying to both Asian and European Canadian women. The present study advances understanding of the personality traits and the contextual conditions accompanying binge eating and provides an important step toward improving treatments for people suffering from eating binges and associated negative consequences.

  12. Mobility Modelling through Trajectory Decomposition and Prediction

    OpenAIRE

    Faghihi, Farbod

    2017-01-01

    The ubiquity of mobile devices with positioning sensors make it possible to derive user's location at any time. However, constantly sensing the position in order to track the user's movement is not feasible, either due to the unavailability of sensors, or computational and storage burdens. In this thesis, we present and evaluate a novel approach for efficiently tracking user's movement trajectories using decomposition and prediction of trajectories. We facilitate tracking by taking advantage ...

  13. Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

    Science.gov (United States)

    2013-01-01

    Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

  14. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  15. Predicting birth weight with conditionally linear transformation models.

    Science.gov (United States)

    Möst, Lisa; Schmid, Matthias; Faschingbauer, Florian; Hothorn, Torsten

    2016-12-01

    Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs. © The Author(s) 2014.

  16. Prediction of hourly solar radiation with multi-model framework

    International Nuclear Information System (INIS)

    Wu, Ji; Chan, Chee Keong

    2013-01-01

    Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

  17. Federated learning of predictive models from federated Electronic Health Records.

    Science.gov (United States)

    Brisimi, Theodora S; Chen, Ruidi; Mela, Theofanie; Olshevsky, Alex; Paschalidis, Ioannis Ch; Shi, Wei

    2018-04-01

    In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and owned by different entities. Past research has been focused on centralized algorithms, which assume the existence of a central data repository (database) which stores and can process the data from all participants. Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data. Given scores of data widely spread across hospitals/individuals, a decentralized computationally scalable methodology is very much in need. We aim at solving a binary supervised classification problem to predict hospitalizations for cardiac events using a distributed algorithm. We seek to develop a general decentralized optimization framework enabling multiple data holders to collaborate and converge to a common predictive model, without explicitly exchanging raw data. We focus on the soft-margin l 1 -regularized sparse Support Vector Machine (sSVM) classifier. We develop an iterative cluster Primal Dual Splitting (cPDS) algorithm for solving the large-scale sSVM problem in a decentralized fashion. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the data holders to collaborate, while keeping every participant's data private. We test cPDS on the problem of predicting hospitalizations due to heart diseases within a calendar year based on information in the patients Electronic Health Records prior to that year. cPDS converges faster than centralized methods at the cost of some communication between agents. It also converges faster and with less communication overhead compared to an alternative distributed

  18. Model predictive control of a crude oil distillation column

    Directory of Open Access Journals (Sweden)

    Morten Hovd

    1999-04-01

    Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.

  19. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    T. Wu; E. Lester; M. Cloke [University of Nottingham, Nottingham (United Kingdom). Nottingham Energy and Fuel Centre

    2005-07-01

    Poor burnout in a coal-fired power plant has marked penalties in the form of reduced energy efficiency and elevated waste material that can not be utilized. The prediction of coal combustion behaviour in a furnace is of great significance in providing valuable information not only for process optimization but also for coal buyers in the international market. Coal combustion models have been developed that can make predictions about burnout behaviour and burnout potential. Most of these kinetic models require standard parameters such as volatile content, particle size and assumed char porosity in order to make a burnout prediction. This paper presents a new model called the Char Burnout Model (ChB) that also uses detailed information about char morphology in its prediction. The model can use data input from one of two sources. Both sources are derived from image analysis techniques. The first from individual analysis and characterization of real char types using an automated program. The second from predicted char types based on data collected during the automated image analysis of coal particles. Modelling results were compared with a different carbon burnout kinetic model and burnout data from re-firing the chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen across several residence times. An improved agreement between ChB model and DTF experimental data proved that the inclusion of char morphology in combustion models can improve model predictions. 27 refs., 4 figs., 4 tabs.

  20. Adaptive Neuro-Fuzzy Inference System Models for Force Prediction of a Mechatronic Flexible Structure

    DEFF Research Database (Denmark)

    Achiche, S.; Shlechtingen, M.; Raison, M.

    2016-01-01

    This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped...... obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error. The frequency domain is considered to analyze the similarity of the frequencies in the predicted...... of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that ANFIS models can be used to set up reliable force predictors for dynamical loaded flexible structures, when a certain degree of inaccuracy is accepted. Furthermore, the comparison...

  1. Questioning the Faith - Models and Prediction in Stream Restoration (Invited)

    Science.gov (United States)

    Wilcock, P.

    2013-12-01

    River management and restoration demand prediction at and beyond our present ability. Management questions, framed appropriately, can motivate fundamental advances in science, although the connection between research and application is not always easy, useful, or robust. Why is that? This presentation considers the connection between models and management, a connection that requires critical and creative thought on both sides. Essential challenges for managers include clearly defining project objectives and accommodating uncertainty in any model prediction. Essential challenges for the research community include matching the appropriate model to project duration, space, funding, information, and social constraints and clearly presenting answers that are actually useful to managers. Better models do not lead to better management decisions or better designs if the predictions are not relevant to and accepted by managers. In fact, any prediction may be irrelevant if the need for prediction is not recognized. The predictive target must be developed in an active dialog between managers and modelers. This relationship, like any other, can take time to develop. For example, large segments of stream restoration practice have remained resistant to models and prediction because the foundational tenet - that channels built to a certain template will be able to transport the supplied sediment with the available flow - has no essential physical connection between cause and effect. Stream restoration practice can be steered in a predictive direction in which project objectives are defined as predictable attributes and testable hypotheses. If stream restoration design is defined in terms of the desired performance of the channel (static or dynamic, sediment surplus or deficit), then channel properties that provide these attributes can be predicted and a basis exists for testing approximations, models, and predictions.

  2. Predicting Magazine Audiences with a Loglinear Model.

    Science.gov (United States)

    1987-07-01

    TITLE (InciudeSecuirty Clauificalson, Predicting !iagaz:ine Atidiences with a Loglinvar \\lode] * 12. PERSONAL AUTHOR(S) * Peter J.1 .:)anahel 1 3&. TYPE...important use of e.d. estimates is in media selection ( Aaker 1975; Lee 1962, 1963; Little and Lodish 1969). All advertising campaigns have a budget. It...BBD we obtain the modified BBD (MBBD). Let X be the number of exposures a person has to k insertions in a single magazine. The mass function of the

  3. General relativistic self-similar waves that induce an anomalous acceleration into the standard model of cosmology

    CERN Document Server

    Smoller, Joel

    2012-01-01

    We prove that the Einstein equations in Standard Schwarzschild Coordinates close to form a system of three ordinary differential equations for a family of spherically symmetric, self-similar expansion waves, and the critical ($k=0$) Friedmann universe associated with the pure radiation phase of the Standard Model of Cosmology (FRW), is embedded as a single point in this family. Removing a scaling law and imposing regularity at the center, we prove that the family reduces to an implicitly defined one parameter family of distinct spacetimes determined by the value of a new {\\it acceleration parameter} $a$, such that $a=1$ corresponds to FRW. We prove that all self-similar spacetimes in the family are distinct from the non-critical $k\

  4. Visual Similarity of Words Alone Can Modulate Hemispheric Lateralization in Visual Word Recognition: Evidence From Modeling Chinese Character Recognition.

    Science.gov (United States)

    Hsiao, Janet H; Cheung, Kit

    2016-03-01

    In Chinese orthography, the most common character structure consists of a semantic radical on the left and a phonetic radical on the right (SP characters); the minority, opposite arrangement also exists (PS characters). Recent studies showed that SP character processing is more left hemisphere (LH) lateralized than PS character processing. Nevertheless, it remains unclear whether this is due to phonetic radical position or character type frequency. Through computational modeling with artificial lexicons, in which we implement a theory of hemispheric asymmetry in perception but do not assume phonological processing being LH lateralized, we show that the difference in character type frequency alone is sufficient to exhibit the effect that the dominant type has a stronger LH lateralization than the minority type. This effect is due to higher visual similarity among characters in the dominant type than the minority type, demonstrating the modulation of visual similarity of words on hemispheric lateralization. Copyright © 2015 Cognitive Science Society, Inc.

  5. Aquatic pathways model to predict the fate of phenolic compounds

    Energy Technology Data Exchange (ETDEWEB)

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.J.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. To better assess possible impacts, we developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The computer programs use compartmental analysis to simulate aquatic ecosystems. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The APM will consider any aquatic pathway for which the user has transport data. Additionally, APM will estimate transport rates from physical and chemical properties of chemicals between several key compartments. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. The properties of heavier molecular weight phenolics (indanols, naphthols) are not well enough understood at this time to make similar judgements. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation (using APM) of a spill of solvent refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor.

  6. CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

    Science.gov (United States)

    Bhhatarai, Barun; Teetz, Wolfram; Liu, Tao; Öberg, Tomas; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor V; Kovarich, Simona; Papa, Ester; Gramatica, Paola

    2011-03-14

    Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.

    Science.gov (United States)

    Cichosz, Simon Lebech; Johansen, Mette Dencker; Hejlesen, Ole

    2015-10-14

    Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies. © 2015 Diabetes Technology Society.

  8. Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

  9. Predicting Error Bars for QSAR Models

    International Nuclear Information System (INIS)

    Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Mueller, Klaus-Robert

    2007-01-01

    Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D 7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches

  10. Prediction models for successful external cephalic version: a systematic review.

    Science.gov (United States)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

    2015-12-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

  11. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  12. AN EFFICIENT PATIENT INFLOW PREDICTION MODEL FOR HOSPITAL RESOURCE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Kottalanka Srikanth

    2017-07-01

    Full Text Available There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

  13. Prediction Model for Gastric Cancer Incidence in Korean Population.

    Directory of Open Access Journals (Sweden)

    Bang Wool Eom

    Full Text Available Predicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.Based on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.During a median of 11.4 years of follow-up, 19,465 (1.4% and 5,579 (0.7% newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women.In this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.

  14. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  15. Predictive modeling of pedestal structure in KSTAR using EPED model

    Energy Technology Data Exchange (ETDEWEB)

    Han, Hyunsun; Kim, J. Y. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Kwon, Ohjin [Department of Physics, Daegu University, Gyeongbuk 712-714 (Korea, Republic of)

    2013-10-15

    A predictive calculation is given for the structure of edge pedestal in the H-mode plasma of the KSTAR (Korea Superconducting Tokamak Advanced Research) device using the EPED model. Particularly, the dependence of pedestal width and height on various plasma parameters is studied in detail. The two codes, ELITE and HELENA, are utilized for the stability analysis of the peeling-ballooning and kinetic ballooning modes, respectively. Summarizing the main results, the pedestal slope and height have a strong dependence on plasma current, rapidly increasing with it, while the pedestal width is almost independent of it. The plasma density or collisionality gives initially a mild stabilization, increasing the pedestal slope and height, but above some threshold value its effect turns to a destabilization, reducing the pedestal width and height. Among several plasma shape parameters, the triangularity gives the most dominant effect, rapidly increasing the pedestal width and height, while the effect of elongation and squareness appears to be relatively weak. Implication of these edge results, particularly in relation to the global plasma performance, is discussed.

  16. Mathematical model predicts the elastic behavior of composite materials

    Directory of Open Access Journals (Sweden)

    Zoroastro de Miranda Boari

    2005-03-01

    Full Text Available Several studies have found that the non-uniform distribution of reinforcing elements in a composite material can markedly influence its characteristics of elastic and plastic deformation and that a composite's overall response is influenced by the physical and geometrical properties of its reinforcing phases. The finite element method, Eshelby's method and dislocation mechanisms are usually employed in formulating a composite's constitutive response. This paper discusses a composite material containing SiC particles in an aluminum matrix. The purpose of this study was to find the correlation between a composite material's particle distribution and its resistance, and to come up with a mathematical model to predict the material's elastic behavior. The proposed formulation was applied to establish the thermal stress field in the aluminum-SiC composite resulting from its fabrication process, whereby the mixture is prepared at 600 °C and the composite material is used at room temperature. The analytical results, which are presented as stress probabilities, were obtained from the mathematical model proposed herein. These results were compared with the numerical ones obtained by the FEM method. A comparison of the results of the two methods, analytical and numerical, reveals very similar average thermal stress values. It is also shown that Maxwell-Boltzmann's distribution law can be applied to identify the correlation between the material's particle distribution and its resistance, using Eshelby's thermal stresses.

  17. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    Science.gov (United States)

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  18. Model predictions for auxiliary heating in spheromaks

    International Nuclear Information System (INIS)

    Fauler, T.K.; Khua, D.D.

    1997-01-01

    Calculations are presented of the plasma temperature waited for under auxiliary heating in spheromaks. A model, ensuring good agreement of earlier experiments with joule heating results, is used. The model includes heat losses due to magnetic fluctuations and shows that the plasma temperatures of the kilo-electron-volt order may be achieved in a small device with the radius of 0.3 m only

  19. Validating predictions from climate envelope models.

    Directory of Open Access Journals (Sweden)

    James I Watling

    Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on

  20. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  1. Evaluation of wave runup predictions from numerical and parametric models

    Science.gov (United States)

    Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.

    2014-01-01

    Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

  2. A neighborhood statistics model for predicting stream pathogen indicator levels.

    Science.gov (United States)

    Pandey, Pramod K; Pasternack, Gregory B; Majumder, Mahbubul; Soupir, Michelle L; Kaiser, Mark S

    2015-03-01

    Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli, a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale.

  3. Prediction skill of rainstorm events over India in the TIGGE weather prediction models

    Science.gov (United States)

    Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

    2017-12-01

    Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

  4. A global high-resolution model experiment on the predictability of the atmosphere

    Science.gov (United States)

    Judt, F.

    2016-12-01

    Forecasting high-impact weather phenomena is one of the most important aspects of numerical weather prediction (NWP). Over the last couple of years, a tremendous increase in computing power has facilitated the advent of global convection-resolving NWP models, which allow for the seamless prediction of weather from local to planetary scales. Unfortunately, the predictability of specific meteorological phenomena in these models is not very well known. This raises questions about which forecast problems are potentially tractable, and what is the value of global convection-resolving model predictions for the end user. To address this issue, we use the Yellowstone supercomputer to conduct a global high-resolution predictability experiment with the recently developed Model for Prediction Across Scales (MPAS). The computing power of Yellowstone enables the model to run at a globally uniform resolution of 4 km with 55 vertical levels (>2 billion grid cells). These simulations, which require 3 million core-hours for the entire experiment, allow for the explicit treatment of organized deep moist convection (i.e., thunderstorm systems). Resolving organized deep moist convection alleviates grave limitations of previous predictability studies, which either used high-resolution limited-area models or global simulations with coarser grids and cumulus parameterization. By computing the error growth characteristics in a set of "identical twin" model runs, the experiment will clarify the intrinsic predictability limits of atmospheric phenomena on a wide range of scales, from severe thunderstorms to global-scale wind patterns that affect the distribution of tropical rainfall. Although a major task by itself, this study is intended to be exploratory work for a future predictability experiment going beyond of what has so far been feasible. We hope to use CISL's new Cheyenne supercomputer to conduct a similar predictability experiments on a global mesh with 1-2 km resolution. This

  5. Mathematical evaluation of similarity factor using various weighing approaches on aceclofenac marketed formulations by model-independent method.

    Science.gov (United States)

    Soni, T G; Desai, J U; Nagda, C D; Gandhi, T R; Chotai, N P

    2008-01-01

    The US Food and Drug Administration's (FDA's) guidance for industry on dissolution testing of immediate-release solid oral dosage forms describes that drug dissolution may be the rate limiting step for drug absorption in the case of low solubility/high permeability drugs (BCS class II drugs). US FDA Guidance describes the model-independent mathematical approach proposed by Moore and Flanner for calculating a similarity factor (f2) of dissolution across a suitable time interval. In the present study, the similarity factor was calculated on dissolution data of two marketed aceclofenac tablets (a BCS class II drug) using various weighing approaches proposed by Gohel et al. The proposed approaches were compared with a conventional approach (W = 1). On the basis of consideration of variability, preference is given in the order of approach 3 > approach 2 > approach 1 as approach 3 considers batch-to-batch as well as within-samples variability and shows best similarity profile. Approach 2 considers batch-to batch variability with higher specificity than approach 1.

  6. Preclinical models used for immunogenicity prediction of therapeutic proteins.

    Science.gov (United States)

    Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

    2013-07-01

    All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

  7. Development of Interpretable Predictive Models for BPH and Prostate Cancer.

    Science.gov (United States)

    Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

    2015-01-01

    Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

  8. Predicting Footbridge Response using Stochastic Load Models

    DEFF Research Database (Denmark)

    Pedersen, Lars; Frier, Christian

    2013-01-01

    Walking parameters such as step frequency, pedestrian mass, dynamic load factor, etc. are basically stochastic, although it is quite common to adapt deterministic models for these parameters. The present paper considers a stochastic approach to modeling the action of pedestrians, but when doing so...... decisions need to be made in terms of statistical distributions of walking parameters and in terms of the parameters describing the statistical distributions. The paper explores how sensitive computations of bridge response are to some of the decisions to be made in this respect. This is useful...

  9. A COMPARISON BETWEEN THREE PREDICTIVE MODELS OF COMPUTATIONAL INTELLIGENCE

    Directory of Open Access Journals (Sweden)

    DUMITRU CIOBANU

    2013-12-01

    Full Text Available Time series prediction is an open problem and many researchers are trying to find new predictive methods and improvements for the existing ones. Lately methods based on neural networks are used extensively for time series prediction. Also, support vector machines have solved some of the problems faced by neural networks and they began to be widely used for time series prediction. The main drawback of those two methods is that they are global models and in the case of a chaotic time series it is unlikely to find such model. In this paper it is presented a comparison between three predictive from computational intelligence field one based on neural networks one based on support vector machine and another based on chaos theory. We show that the model based on chaos theory is an alternative to the other two methods.

  10. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  11. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Directory of Open Access Journals (Sweden)

    Saerom Park

    Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  12. Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

    Science.gov (United States)

    Park, Saerom; Lee, Jaewook; Son, Youngdoo

    2016-01-01

    Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.

  13. A burnout prediction model based around char morphology

    Energy Technology Data Exchange (ETDEWEB)

    Tao Wu; Edward Lester; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical, Environmental and Mining Engineering

    2006-05-15

    Several combustion models have been developed that can make predictions about coal burnout and burnout potential. Most of these kinetic models require standard parameters such as volatile content and particle size to make a burnout prediction. This article presents a new model called the char burnout (ChB) model, which also uses detailed information about char morphology in its prediction. The input data to the model is based on information derived from two different image analysis techniques. One technique generates characterization data from real char samples, and the other predicts char types based on characterization data from image analysis of coal particles. The pyrolyzed chars in this study were created in a drop tube furnace operating at 1300{sup o}C, 200 ms, and 1% oxygen. Modeling results were compared with a different carbon burnout kinetic model as well as the actual burnout data from refiring the same chars in a drop tube furnace operating at 1300{sup o}C, 5% oxygen, and residence times of 200, 400, and 600 ms. A good agreement between ChB model and experimental data indicates that the inclusion of char morphology in combustion models could well improve model predictions. 38 refs., 5 figs., 6 tabs.

  14. Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

    Directory of Open Access Journals (Sweden)

    Volker J. Schmid

    2007-10-01

    Full Text Available The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

  15. Modeling for prediction of restrained shrinkage effect in concrete repair

    International Nuclear Information System (INIS)

    Yuan Yingshu; Li Guo; Cai Yue

    2003-01-01

    A general model of autogenous shrinkage caused by chemical reaction (chemical shrinkage) is developed by means of Arrhenius' law and a degree of chemical reaction. Models of tensile creep and relaxation modulus are built based on a viscoelastic, three-element model. Tests of free shrinkage and tensile creep were carried out to determine some coefficients in the models. Two-dimensional FEM analysis based on the models and other constitutions can predict the development of tensile strength and cracking. Three groups of patch-repaired beams were designed for analysis and testing. The prediction from the analysis shows agreement with the test results. The cracking mechanism after repair is discussed

  16. Evaluation of two models for predicting elemental accumulation by arthropods

    International Nuclear Information System (INIS)

    Webster, J.R.; Crossley, D.A. Jr.

    1978-01-01

    Two different models have been proposed for predicting elemental accumulation by arthropods. Parameters of both models can be quantified from radioisotope elimination experiments. Our analysis of the 2 models shows that both predict identical elemental accumulation for a whole organism, though differing in the accumulation in body and gut. We quantified both models with experimental data from 134 Cs and 85 Sr elimination by crickets. Computer simulations of radioisotope accumulation were then compared with actual accumulation experiments. Neither model showed exact fit to the experimental data, though both showed the general pattern of elemental accumulation

  17. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment

  18. Modeling long period swell in Southern California: Practical boundary conditions from buoy observations and global wave model predictions

    Science.gov (United States)

    Crosby, S. C.; O'Reilly, W. C.; Guza, R. T.

    2016-02-01

    Accurate, unbiased, high-resolution (in space and time) nearshore wave predictions are needed to drive models of beach erosion, coastal flooding, and alongshore transport of sediment, biota and pollutants. On highly sheltered shorelines, wave predictions are sensitive to the directions of onshore propagating waves, and nearshore model prediction error is often dominated by uncertainty in offshore boundary conditions. Offshore islands and shoals, and coastline curvature, create complex sheltering patterns over the 250km span of southern California (SC) shoreline. Here, regional wave model skill in SC was compared for different offshore boundary conditions created using offshore buoy observations and global wave model hindcasts (National Oceanographic and Atmospheric Administration Wave Watch 3, WW3). Spectral ray-tracing methods were used to transform incident offshore swell (0.04-0.09Hz) energy at high directional resolution (1-deg). Model skill is assessed for predictions (wave height, direction, and alongshore radiation stress) at 16 nearshore buoy sites between 2000 and 2009. Model skill using buoy-derived boundary conditions is higher than with WW3-derived boundary conditions. Buoy-driven nearshore model results are similar with various assumptions about the true offshore directional distribution (maximum entropy, Bayesian direct, and 2nd derivative smoothness). Two methods combining offshore buoy observations with WW3 predictions in the offshore boundary condition did not improve nearshore skill above buoy-only methods. A case example at Oceanside harbor shows strong sensitivity of alongshore sediment transport predictions to different offshore boundary conditions. Despite this uncertainty in alongshore transport magnitude, alongshore gradients in transport (e.g. the location of model accretion and erosion zones) are determined by the local bathymetry, and are similar for all predictions.

  19. Geospatial application of the Water Erosion Prediction Project (WEPP) Model

    Science.gov (United States)

    D. C. Flanagan; J. R. Frankenberger; T. A. Cochrane; C. S. Renschler; W. J. Elliot

    2011-01-01

    The Water Erosion Prediction Project (WEPP) model is a process-based technology for prediction of soil erosion by water at hillslope profile, field, and small watershed scales. In particular, WEPP utilizes observed or generated daily climate inputs to drive the surface hydrology processes (infiltration, runoff, ET) component, which subsequently impacts the rest of the...

  20. Techniques for discrimination-free predictive models (Chapter 12)

    NARCIS (Netherlands)

    Kamiran, F.; Calders, T.G.K.; Pechenizkiy, M.; Custers, B.H.M.; Calders, T.G.K.; Schermer, B.W.; Zarsky, T.Z.

    2013-01-01

    In this chapter, we give an overview of the techniques developed ourselves for constructing discrimination-free classifiers. In discrimination-free classification the goal is to learn a predictive model that classifies future data objects as accurately as possible, yet the predicted labels should be