Modeling of indoor radon concentration from radon exhalation rates of building materials and validation through measurements.

PubMed

Kumar, Amit; Chauhan, R P; Joshi, Manish; Sahoo, B K

2014-01-01

Building materials are the second major source of indoor radon after soil. The contribution of building materials towards indoor radon depends upon the radium content and exhalation rates and can be used as a primary index for radon levels in the dwellings. The radon flux data from the building materials was used for calculation of the indoor radon concentrations and doses by many researchers using one and two dimensional model suggested by various researchers. In addition to radium content, the radon wall flux from a surface strongly depends upon the radon diffusion length (L) and thickness of the wall (2d). In the present work the indoor radon concentrations from the measured radon exhalation rate of building materials calculated using different models available in literature and validation of models was made through measurement. The variation in the predicted radon flux from different models was compared with d/L value for wall and roofs of different dwellings. The results showed that the radon concentrations predicted by models agree with experimental value. The applicability of different model with d/L ratio was discussed. The work aims to select a more appropriate and general model among available models in literature for the prediction of indoor radon. Copyright © 2013 Elsevier Ltd. All rights reserved.

What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

PubMed Central

Arepeva, Maria; Kolbin, Alexey; Kurylev, Alexey; Balykina, Julia; Sidorenko, Sergey

2015-01-01

Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results. PMID:25972847

Shaking table test and dynamic response prediction on an earthquake-damaged RC building

NASA Astrophysics Data System (ADS)

Xianguo, Ye; Jiaru, Qian; Kangning, Li

2004-12-01

This paper presents the results from shaking table tests of a one-tenth-scale reinforced concrete (RC) building model. The test model is a protype of a building that was seriously damaged during the 1985 Mexico earthquake. The input ground excitation used during the test was from the records obtained near the site of the prototype building during the 1985 and 1995 Mexico earthquakes. The tests showed that the damage pattern of the test model agreed well with that of the prototype building. Analytical prediction of earthquake response has been conducted for the prototype building using a sophisticated 3-D frame model. The input motion used for the dynamic analysis was the shaking table test measurements with similarity transformation. The comparison of the analytical results and the shaking table test results indicates that the response of the RC building to minor and the moderate earthquakes can be predicated well. However, there is difference between the predication and the actual response to the major earthquake.

A model to predict radon exhalation from walls to indoor air based on the exhalation from building material samples.

PubMed

Sahoo, B K; Sapra, B K; Gaware, J J; Kanse, S D; Mayya, Y S

2011-06-01

In recognition of the fact that building materials are an important source of indoor radon, second only to soil, surface radon exhalation fluxes have been extensively measured from the samples of these materials. Based on this flux data, several researchers have attempted to predict the inhalation dose attributable to radon emitted from walls and ceilings made up of these materials. However, an important aspect not considered in this methodology is the enhancement of the radon flux from the wall or the ceiling constructed using the same building material. This enhancement occurs mainly because of the change in the radon diffusion process from the former to the latter configuration. To predict the true radon flux from the wall based on the flux data of building material samples, we now propose a semi-empirical model involving radon diffusion length and the physical dimensions of the samples as well as wall thickness as other input parameters. This model has been established by statistically fitting the ratio of the solution to radon diffusion equations for the cases of three-dimensional cuboidal shaped building materials (such as brick, concrete block) and one dimensional wall system to a simple mathematical function. The model predictions have been validated against the measurements made at a new construction site. This model provides an alternative tool (substitute to conventional 1-D model) to estimate radon flux from a wall without relying on ²²⁶Ra content, radon emanation factor and bulk density of the samples. Moreover, it may be very useful in the context of developing building codes for radon regulation in new buildings. Copyright © 2011 Elsevier B.V. All rights reserved.

Fast response modeling of a two building urban street canyon

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

Pardyjak, E. R.; Brown, M. J.

2002-01-01

QWIC-URB is a fast response model designed to generate high resolution, 3-dimensional wind fields around buildings. The wind fields are produced using a mass consistent diagnostic wind model based on the work of Roeckle (1990, 1998) and Kaplan & Dinar (1996). QWIC-URB has been used for producing wind fields around single buildings with various incident wind angles (Pardyjak and Brown 2001). Recently, the model has been expanded to consider two-building, 3D canyon flow. That is, two rectangular parallelepipeds of height H, crosswind width W, and length L separated by a distance S. The purpose of this work is to continuemore » to evaluate the Roeckle (1990) model and develop improvements. In this paper, the model is compared to the twin high-rise building data set of Ohba et al. (1993, hereafter OSL93). Although the model qualitatively predicts the flow field fairly well for simple canyon flow, it over predicts the strength of vortex circulation and fails to reproduce the upstream rotor.« less

Prediction of Indoor Air Exposure from Outdoor Air Quality Using an Artificial Neural Network Model for Inner City Commercial Buildings

PubMed Central

Challoner, Avril; Pilla, Francesco; Gill, Laurence

2015-01-01

NO2 and particulate matter are the air pollutants of most concern in Ireland, with possible links to the higher respiratory and cardiovascular mortality and morbidity rates found in the country compared to the rest of Europe. Currently, air quality limits in Europe only cover outdoor environments yet the quality of indoor air is an essential determinant of a person’s well-being, especially since the average person spends more than 90% of their time indoors. The modelling conducted in this research aims to provide a framework for epidemiological studies by the use of publically available data from fixed outdoor monitoring stations to predict indoor air quality more accurately. Predictions are made using two modelling techniques, the Personal-exposure Activity Location Model (PALM), to predict outdoor air quality at a particular building, and Artificial Neural Networks, to model the indoor/outdoor relationship of the building. This joint approach has been used to predict indoor air concentrations for three inner city commercial buildings in Dublin, where parallel indoor and outdoor diurnal monitoring had been carried out on site. This modelling methodology has been shown to provide reasonable predictions of average NO2 indoor air quality compared to the monitored data, but did not perform well in the prediction of indoor PM2.5 concentrations. Hence, this approach could be used to determine NO2 exposures more rigorously of those who work and/or live in the city centre, which can then be linked to potential health impacts. PMID:26633448

USE OF ROUGH SETS AND SPECTRAL DATA FOR BUILDING PREDICTIVE MODELS OF REACTION RATE CONSTANTS

EPA Science Inventory

A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase min-infrared library spectra and a rule-building software system based on the mathematical theory of rough sets. A diverse set of 41 esters ...

Dynamic modeling and experiments on the coupled vibrations of building and elevator ropes

NASA Astrophysics Data System (ADS)

Yang, Dong-Ho; Kim, Ki-Young; Kwak, Moon K.; Lee, Seungjun

2017-03-01

This study is concerned with the theoretical modelling and experimental verification of the coupled vibrations of building and elevator ropes. The elevator ropes consist of a main rope which supports the cage and the compensation rope which is connected to the compensation sheave. The elevator rope is a flexible wire with a low damping, so it is prone to vibrations. In the case of a high-rise building, the rope length also increases significantly, so that the fundamental frequency of the elevator rope approaches the fundamental frequency of the building thus increasing the possibility of resonance. In this study, the dynamic model for the analysis of coupled vibrations of building and elevator ropes was derived by using Hamilton's principle, where the cage motion was also considered. An experimental testbed was built to validate the proposed dynamic model. It was found that the experimental results are in good agreement with the theoretical predictions thus validating the proposed dynamic model. The proposed model was then used to predict the vibrations of real building and elevator ropes.

Predictive model for CO2 generation and decay in building envelopes

NASA Astrophysics Data System (ADS)

Aglan, Heshmat A.

2003-01-01

Understanding carbon dioxide generation and decay patterns in buildings with high occupancy levels is useful to identify their indoor air quality, air change rates, percent fresh air makeup, occupancy pattern, and how a variable air volume system to off-set undesirable CO2 level can be modulated. A mathematical model governing the generation and decay of CO2 in building envelopes with forced ventilation due to high occupancy is developed. The model has been verified experimentally in a newly constructed energy efficient healthy house. It was shown that the model accurately predicts the CO2 concentration at any time during the generation and decay processes.

Building a Better Applicant Pool--A Case Study of the Use of Predictive Modeling and Market Segmentation to Build and Enroll Better Pools of Students

ERIC Educational Resources Information Center

Herridge, Bart; Heil, Robert

2003-01-01

Predictive modeling has been a popular topic in higher education for the last few years. This case study shows an example of an effective use of modeling combined with market segmentation to strategically divide large, unmanageable prospect and inquiry pools and convert them into applicants, and eventually, enrolled students. (Contains 6 tables.)

A numerical simulation strategy on occupant evacuation behaviors and casualty prediction in a building during earthquakes

NASA Astrophysics Data System (ADS)

Li, Shuang; Yu, Xiaohui; Zhang, Yanjuan; Zhai, Changhai

2018-01-01

Casualty prediction in a building during earthquakes benefits to implement the economic loss estimation in the performance-based earthquake engineering methodology. Although after-earthquake observations reveal that the evacuation has effects on the quantity of occupant casualties during earthquakes, few current studies consider occupant movements in the building in casualty prediction procedures. To bridge this knowledge gap, a numerical simulation method using refined cellular automata model is presented, which can describe various occupant dynamic behaviors and building dimensions. The simulation on the occupant evacuation is verified by a recorded evacuation process from a school classroom in real-life 2013 Ya'an earthquake in China. The occupant casualties in the building under earthquakes are evaluated by coupling the building collapse process simulation by finite element method, the occupant evacuation simulation, and the casualty occurrence criteria with time and space synchronization. A case study of casualty prediction in a building during an earthquake is provided to demonstrate the effect of occupant movements on casualty prediction.

NREL Improves Building Energy Simulation Programs Through Diagnostic Testing (Fact Sheet)

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

Not Available

2012-01-01

This technical highlight describes NREL research to develop Building Energy Simulation Test for Existing Homes (BESTEST-EX) to increase the quality and accuracy of energy analysis tools for the building retrofit market. Researchers at the National Renewable Energy Laboratory (NREL) have developed a new test procedure to increase the quality and accuracy of energy analysis tools for the building retrofit market. The Building Energy Simulation Test for Existing Homes (BESTEST-EX) is a test procedure that enables software developers to evaluate the performance of their audit tools in modeling energy use and savings in existing homes when utility bills are available formore » model calibration. Similar to NREL's previous energy analysis tests, such as HERS BESTEST and other BESTEST suites included in ANSI/ASHRAE Standard 140, BESTEST-EX compares software simulation findings to reference results generated with state-of-the-art simulation tools such as EnergyPlus, SUNREL, and DOE-2.1E. The BESTEST-EX methodology: (1) Tests software predictions of retrofit energy savings in existing homes; (2) Ensures building physics calculations and utility bill calibration procedures perform to a minimum standard; and (3) Quantifies impacts of uncertainties in input audit data and occupant behavior. BESTEST-EX includes building physics and utility bill calibration test cases. The diagram illustrates the utility bill calibration test cases. Participants are given input ranges and synthetic utility bills. Software tools use the utility bills to calibrate key model inputs and predict energy savings for the retrofit cases. Participant energy savings predictions using calibrated models are compared to NREL predictions using state-of-the-art building energy simulation programs.« less

Providing pressure inputs to multizone building models

DOE PAGES

Herring, Steven J.; Batchelor, Simon; Bieringer, Paul E.; ...

2016-02-13

A study to assess how the fidelity of wind pressure inputs and indoor model complexity affect the predicted air change rate for a study building is presented. The purpose of the work is to support the development of a combined indoor-outdoor hazard prediction tool, which links the CONTAM multizone building simulation tool with outdoor dispersion models. The study building, representing a large office block of a simple rectangular geometry under natural ventilation, was based on a real building used in the Joint Urban 2003 experiment. A total of 1600 indoor model flow simulations were made, driven by 100 meteorological conditionsmore » which provided a wide range of building surface pressures. These pressures were applied at four levels of resolution to four different building configurations with varying numbers of internal zones and indoor and outdoor flow paths. Analysis of the results suggests that surface pressures and flow paths across the envelope should be specified at a resolution consistent with the dimensions of the smallest volume of interest, to ensure that appropriate outputs are obtained.« less

Improving stability of prediction models based on correlated omics data by using network approaches.

PubMed

Tissier, Renaud; Houwing-Duistermaat, Jeanine; Rodríguez-Girondo, Mar

2018-01-01

Building prediction models based on complex omics datasets such as transcriptomics, proteomics, metabolomics remains a challenge in bioinformatics and biostatistics. Regularized regression techniques are typically used to deal with the high dimensionality of these datasets. However, due to the presence of correlation in the datasets, it is difficult to select the best model and application of these methods yields unstable results. We propose a novel strategy for model selection where the obtained models also perform well in terms of overall predictability. Several three step approaches are considered, where the steps are 1) network construction, 2) clustering to empirically derive modules or pathways, and 3) building a prediction model incorporating the information on the modules. For the first step, we use weighted correlation networks and Gaussian graphical modelling. Identification of groups of features is performed by hierarchical clustering. The grouping information is included in the prediction model by using group-based variable selection or group-specific penalization. We compare the performance of our new approaches with standard regularized regression via simulations. Based on these results we provide recommendations for selecting a strategy for building a prediction model given the specific goal of the analysis and the sizes of the datasets. Finally we illustrate the advantages of our approach by application of the methodology to two problems, namely prediction of body mass index in the DIetary, Lifestyle, and Genetic determinants of Obesity and Metabolic syndrome study (DILGOM) and prediction of response of each breast cancer cell line to treatment with specific drugs using a breast cancer cell lines pharmacogenomics dataset.

Prediction of indoor radon/thoron concentration in a model room from exhalation rates of building materials for different ventilation rates

NASA Astrophysics Data System (ADS)

Kumar, Manish; Sharma, Navjeet; Sarin, Amit

2018-05-01

Studies have confirmed that elevated levels of radon/thoron in the human-environments can substantially increase the risk of lung cancer in general population. The building materials are the second largest contributors to indoor radon/thoron after soil and bedrock beneath dwellings. In present investigation, the exhalation rates of radon/thoron from different building materials samples have been analysed using active technique. Radon/thoron concentrations in a model room have been predicted based on the exhalation rates from walls, floor and roof. The indoor concentrations show significant variations depending upon the ventilation rate and type of building materials used.

Data-driven forecasting algorithms for building energy consumption

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram

2013-04-01

This paper introduces two forecasting methods for building energy consumption data that are recorded from smart meters in high resolution. For utility companies, it is important to reliably forecast the aggregate consumption profile to determine energy supply for the next day and prevent any crisis. The proposed methods involve forecasting individual load on the basis of their measurement history and weather data without using complicated models of building system. The first method is most efficient for a very short-term prediction, such as the prediction period of one hour, and uses a simple adaptive time-series model. For a longer-term prediction, a nonparametric Gaussian process has been applied to forecast the load profiles and their uncertainty bounds to predict a day-ahead. These methods are computationally simple and adaptive and thus suitable for analyzing a large set of data whose pattern changes over the time. These forecasting methods are applied to several sets of building energy consumption data for lighting and heating-ventilation-air-conditioning (HVAC) systems collected from a campus building at Stanford University. The measurements are collected every minute, and corresponding weather data are provided hourly. The results show that the proposed algorithms can predict those energy consumption data with high accuracy.

A consistent model for tsunami actions on buildings

NASA Astrophysics Data System (ADS)

Foster, A.; Rossetto, T.; Eames, I.; Chandler, I.; Allsop, W.

2016-12-01

The Japan (2011) and Indian Ocean (2004) tsunami resulted in significant loss of life, buildings, and critical infrastructure. The tsunami forces imposed upon structures in coastal regions are initially due to wave slamming, after which the quasi-steady flow of the sea water around buildings becomes important. An essential requirement in both design and loss assessment is a consistent model that can accurately predict these forces. A model suitable for predicting forces in the in the quasi-steady range has been established as part of a systematic programme of research by the UCL EPICentre to understand the fundamental physical processes of tsunami actions on buildings, and more generally their social and economic consequences. Using the pioneering tsunami generator at HR Wallingford, this study considers the influence of unsteady flow conditions on the forces acting upon a rectangular building occupying 10-80% of a channel for 20-240 second wave periods. A mathematical model based upon basic open-channel flow principles is proposed, which provides empirical estimates for drag and hydrostatic coefficients. A simple force prediction equation, requiring only basic flow velocity and wave height inputs is then developed, providing good agreement with the experimental results. The results of this study demonstrate that the unsteady forces from the very long waves encountered during tsunami events can be predicted with a level of accuracy and simplicity suitable for design and risk assessment.

A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock

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

None

Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper willmore » describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.« less

CONFOLD2: improved contact-driven ab initio protein structure modeling.

PubMed

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

Weather Correlations to Calculate Infiltration Rates for U. S. Commercial Building Energy Models.

PubMed

Ng, Lisa C; Quiles, Nelson Ojeda; Dols, W Stuart; Emmerich, Steven J

2018-01-01

As building envelope performance improves, a greater percentage of building energy loss will occur through envelope leakage. Although the energy impacts of infiltration on building energy use can be significant, current energy simulation software have limited ability to accurately account for envelope infiltration and the impacts of improved airtightness. This paper extends previous work by the National Institute of Standards and Technology that developed a set of EnergyPlus inputs for modeling infiltration in several commercial reference buildings using Chicago weather. The current work includes cities in seven additional climate zones and uses the updated versions of the prototype commercial building types developed by the Pacific Northwest National Laboratory for the U. S. Department of Energy. Comparisons were made between the predicted infiltration rates using three representations of the commercial building types: PNNL EnergyPlus models, CONTAM models, and EnergyPlus models using the infiltration inputs developed in this paper. The newly developed infiltration inputs in EnergyPlus yielded average annual increases of 3 % and 8 % in the HVAC electrical and gas use, respectively, over the original infiltration inputs in the PNNL EnergyPlus models. When analyzing the benefits of building envelope airtightening, greater HVAC energy savings were predicted using the newly developed infiltration inputs in EnergyPlus compared with using the original infiltration inputs. These results indicate that the effects of infiltration on HVAC energy use can be significant and that infiltration can and should be better accounted for in whole-building energy models.

A Method to Test Model Calibration Techniques

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

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

A Method to Test Model Calibration Techniques: Preprint

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

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

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Rise of Buoyant Emissions from Low-Level Sources in the Presence of Upstream and Downstream Obstacles

NASA Astrophysics Data System (ADS)

Pournazeri, Sam; Princevac, Marko; Venkatram, Akula

2012-08-01

Field and laboratory studies have been conducted to investigate the effect of surrounding buildings on the plume rise from low-level buoyant sources, such as distributed power generators. The field experiments were conducted in Palm Springs, California, USA in November 2010 and plume rise from a 9.3 m stack was measured. In addition to the field study, a laboratory study was conducted in a water channel to investigate the effects of surrounding buildings on plume rise under relatively high wind-speed conditions. Different building geometries and source conditions were tested. The experiments revealed that plume rise from low-level buoyant sources is highly affected by the complex flows induced by buildings stationed upstream and downstream of the source. The laboratory results were compared with predictions from a newly developed numerical plume-rise model. Using the flow measurements associated with each building configuration, the numerical model accurately predicted plume rise from low-level buoyant sources that are influenced by buildings. This numerical plume rise model can be used as a part of a computational fluid dynamics model.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

PubMed

Zhao, Lue Ping; Bolouri, Hamid

2016-04-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015). Copyright © 2016 Elsevier Inc. All rights reserved.

Object-Oriented Regression for Building Predictive Models with High Dimensional Omics Data from Translational Studies

PubMed Central

Zhao, Lue Ping; Bolouri, Hamid

2016-01-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and to make the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (p=0.015). PMID:26972839

Study on the Influence of Building Materials on Indoor Pollutants and Pollution Sources

NASA Astrophysics Data System (ADS)

Wang, Yao

2018-01-01

The paper summarizes the achievements and problems of indoor air quality research at home and abroad. The pollutants and pollution sources in the room are analyzed systematically. The types of building materials and pollutants are also discussed. The physical and chemical properties and health effects of main pollutants were analyzed and studied. According to the principle of mass balance, the basic mathematical model of indoor air quality is established. Considering the release rate of pollutants and indoor ventilation, a mathematical model for predicting the concentration of indoor air pollutants is derived. The model can be used to analyze and describe the variation of pollutant concentration in indoor air, and to predict and calculate the concentration of pollutants in indoor air at a certain time. The results show that the mathematical model established in this study can be used to analyze and predict the variation law of pollutant concentration in indoor air. The evaluation model can be used to evaluate the impact of indoor air quality and evaluation of current situation. Especially in the process of building and interior decoration, through pre-evaluation, it can provide reliable design parameters for selecting building materials and determining ventilation volume.

A diffusivity model for predicting VOC diffusion in porous building materials based on fractal theory.

PubMed

Liu, Yanfeng; Zhou, Xiaojun; Wang, Dengjia; Song, Cong; Liu, Jiaping

2015-12-15

Most building materials are porous media, and the internal diffusion coefficients of such materials have an important influences on the emission characteristics of volatile organic compounds (VOCs). The pore structure of porous building materials has a significant impact on the diffusion coefficient. However, the complex structural characteristics bring great difficulties to the model development. The existing prediction models of the diffusion coefficient are flawed and need to be improved. Using scanning electron microscope (SEM) observations and mercury intrusion porosimetry (MIP) tests of typical porous building materials, this study developed a new diffusivity model: the multistage series-connection fractal capillary-bundle (MSFC) model. The model considers the variable-diameter capillaries formed by macropores connected in series as the main mass transfer paths, and the diameter distribution of the capillary bundles obeys a fractal power law in the cross section. In addition, the tortuosity of the macrocapillary segments with different diameters is obtained by the fractal theory. Mesopores serve as the connections between the macrocapillary segments rather than as the main mass transfer paths. The theoretical results obtained using the MSFC model yielded a highly accurate prediction of the diffusion coefficients and were in a good agreement with the VOC concentration measurements in the environmental test chamber. Copyright © 2015 Elsevier B.V. All rights reserved.

Single-layer model to predict the source/sink behavior of diffusion-controlled building materials.

PubMed

Kumar, Deept; Little, John C

2003-09-01

Building materials may act as both sources of and sinks forvolatile organic compounds (VOCs) in indoor air. A strategy to characterize the rate of absorption and desorption of VOCs by diffusion-controlled building materials is validated. A previously developed model that predicts mass transfer between a flat slab of material and the well-mixed air within a chamber or room is extended. The generalized model allows a nonuniform initial material-phase concentration and a transient influent gas-phase concentration to be simultaneously considered. An analytical solution to the more general model is developed. Experimental data are obtained by placing samples of vinyl flooring inside a small stainless steel chamber and exposing them to absorption/desorption cycles of n-dodecane and phenol. Measured values for the material-air partition coefficient and the material-phase diffusion coefficient were obtained previously in a series of completely independent experiments. The a priori model predictions are in close agreement with the observed experimental data.

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

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

Delgoshaei, Parastoo; Austin, Mark A.; Pertzborn, Amanda J.

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-drivenmore » control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.« less

Energy and IAQ Implications of Alternative Minimum Ventilation Rates in California Retail and School Buildings

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

Dutton, Spencer M.; Fisk, William J.

For a stand-alone retail building, a primary school, and a secondary school in each of the 16 California climate zones, the EnergyPlus building energy simulation model was used to estimate how minimum mechanical ventilation rates (VRs) affect energy use and indoor air concentrations of an indoor-generated contaminant. The modeling indicates large changes in heating energy use, but only moderate changes in total building energy use, as minimum VRs in the retail building are changed. For example, predicted state-wide heating energy consumption in the retail building decreases by more than 50% and total building energy consumption decreases by approximately 10% asmore » the minimum VR decreases from the Title 24 requirement to no mechanical ventilation. The primary and secondary schools have notably higher internal heat gains than in the retail building models, resulting in significantly reduced demand for heating. The school heating energy use was correspondingly less sensitive to changes in the minimum VR. The modeling indicates that minimum VRs influence HVAC energy and total energy use in schools by only a few percent. For both the retail building and the school buildings, minimum VRs substantially affected the predicted annual-average indoor concentrations of an indoor generated contaminant, with larger effects in schools. The shape of the curves relating contaminant concentrations with VRs illustrate the importance of avoiding particularly low VRs.« less

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

A prediction model of signal degradation in LMSS for urban areas

NASA Technical Reports Server (NTRS)

Matsudo, Takashi; Minamisono, Kenichi; Karasawa, Yoshio; Shiokawa, Takayasu

1993-01-01

A prediction model of signal degradation in a Land Mobile Satellite Service (LMSS) for urban areas is proposed. This model treats shadowing effects caused by buildings statistically and can predict a Cumulative Distribution Function (CDF) of signal diffraction losses in urban areas as a function of system parameters such as frequency and elevation angle and environmental parameters such as number of building stories and so on. In order to examine the validity of the model, we compared the percentage of locations where diffraction losses were smaller than 6 dB obtained by the CDF with satellite visibility measured by a radiometer. As a result, it was found that this proposed model is useful for estimating the feasibility of providing LMSS in urban areas.

A hybrid approach to survival model building using integration of clinical and molecular information in censored data.

PubMed

Choi, Ickwon; Kattan, Michael W; Wells, Brian J; Yu, Changhong

2012-01-01

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the P ≥ N problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, L₂ penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.

Community United Methodist Church passive solar classroom addition: comparison of predicted and actual energy use

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

Miller, W.H.; Peckham, N.

1984-01-01

The Community United Methodist Church of Columbia, Missouri, has recently built a passive solar addition. This building was partially funded by the Department of Energy Passive Solar Commercial Building Demonstration Program (1) and by a grant from the Board of Global Ministries of the United Methodist Church. As part of the design phase, the PASOLE computer code was used to model the thermal characteristics of the building. The building was subsequently completed in September 1981, and one and one-half years of end use energy data has been collected as of March 1983. This paper presents (1) a description of themore » new building and the computer model used to analyze it, (2) a comparison of predicted and actual energy use, (3) a comparison between the new, solar building and conventional portions of the church complex and (4) summarizes other operational experiences.« less

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

PubMed

Liu, Zitao; Hauskrecht, Milos

2017-11-01

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

Enhanced air dispersion modelling at a typical Chinese nuclear power plant site: Coupling RIMPUFF with two advanced diagnostic wind models.

PubMed

Liu, Yun; Li, Hong; Sun, Sida; Fang, Sheng

2017-09-01

An enhanced air dispersion modelling scheme is proposed to cope with the building layout and complex terrain of a typical Chinese nuclear power plant (NPP) site. In this modelling, the California Meteorological Model (CALMET) and the Stationary Wind Fit and Turbulence (SWIFT) are coupled with the Risø Mesoscale PUFF model (RIMPUFF) for refined wind field calculation. The near-field diffusion coefficient correction scheme of the Atmospheric Relative Concentrations in the Building Wakes Computer Code (ARCON96) is adopted to characterize dispersion in building arrays. The proposed method is evaluated by a wind tunnel experiment that replicates the typical Chinese NPP site. For both wind speed/direction and air concentration, the enhanced modelling predictions agree well with the observations. The fraction of the predictions within a factor of 2 and 5 of observations exceeds 55% and 82% respectively in the building area and the complex terrain area. This demonstrates the feasibility of the new enhanced modelling for typical Chinese NPP sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations.

PubMed

Beekhuizen, Johan; Heuvelink, Gerard B M; Huss, Anke; Bürgi, Alfred; Kromhout, Hans; Vermeulen, Roel

2014-11-01

With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified. With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen's Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites. The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance. We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output. Copyright © 2014 Elsevier Inc. All rights reserved.

The Use of Modelling for Theory Building in Qualitative Analysis

ERIC Educational Resources Information Center

Briggs, Ann R. J.

2007-01-01

The purpose of this article is to exemplify and enhance the place of modelling as a qualitative process in educational research. Modelling is widely used in quantitative research as a tool for analysis, theory building and prediction. Statistical data lend themselves to graphical representation of values, interrelationships and operational…

Improving the accuracy of energy baseline models for commercial buildings with occupancy data

DOE PAGES

Liang, Xin; Hong, Tianzhen; Shen, Geoffrey Qiping

2016-07-07

More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essentialmore » for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. To conclude, the results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.« less

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Modeling the risk of water pollution by pesticides from imbalanced data.

PubMed

Trajanov, Aneta; Kuzmanovski, Vladimir; Real, Benoit; Perreau, Jonathan Marks; Džeroski, Sašo; Debeljak, Marko

2018-04-30

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation. In addition, the data describing water pollution with pesticides are, in most cases, very imbalanced. This is due to strict regulations for pesticide applications, which lead to only a few pollution events. In this study, we propose the use of data mining to build models for assessing the risk of water pollution by pesticides in field-drained outflow water. Unlike the mechanistic models, the models generated by data mining are based on easily obtainable empirical data, while the parameterization of the models is not influenced by the subjectivity of ecological modelers. We used empirical data from field trials at the La Jaillière experimental site in France and applied the random forests algorithm to build predictive models that predict "risky" and "not-risky" pesticide application events. To address the problems of the imbalanced classes in the data, cost-sensitive learning and different measures of predictive performance were used. Despite the high imbalance between risky and not-risky application events, we managed to build predictive models that make reliable predictions. The proposed modeling approach can be easily applied to other ecological modeling problems where we encounter empirical data with highly imbalanced classes.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

Building a generalized distributed system model

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi; Foudriat, E. C.

1991-01-01

A modeling tool for both analysis and design of distributed systems is discussed. Since many research institutions have access to networks of workstations, the researchers decided to build a tool running on top of the workstations to function as a prototype as well as a distributed simulator for a computing system. The effects of system modeling on performance prediction in distributed systems and the effect of static locking and deadlocks on the performance predictions of distributed transactions are also discussed. While the probability of deadlock is considerably small, its effects on performance could be significant.

Pretest predictions for the response of a 1:8-scale steel LWR containment building model to static overpressurization

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

Clauss, D.B.

The analyses used to predict the behavior of a 1:8-scale model of a steel LWR containment building to static overpressurization are described and results are presented. Finite strain, large displacement, and nonlinear material properties were accounted for using finite element methods. Three-dimensional models were needed to analyze the penetrations, which included operable equipment hatches, personnel lock representations, and a constrained pipe. It was concluded that the scale model would fail due to leakage caused by large deformations of the equipment hatch sleeves. 13 refs., 34 figs., 1 tab.

Model estimation and measurement of ammonia emission from naturally ventilated dairy cattle buildings with slatted floor designs.

PubMed

Wang, Chaoyuan; Li, Baoming; Zhang, Guoqiang; Rom, Hans Benny; Strøom, Jan S

2006-09-01

Laboratory experiments were carried out in a wind tunnel with a model of a slurry pit to investigate the characteristics of ammonia emission from dairy cattle buildings with slatted floor designs. Ammonia emission at different temperatures and air velocities over the floor surface above the slurry pit was measured with uniform feces spreading and urine sprinkling on the surface daily. The data were used to improve a model for estimation of ammonia emission from dairy cattle buildings. Estimates from the updated emission model were compared with measured data from five naturally ventilated dairy cattle buildings. The overall measured ammonia emission rates were in the range of 11-88 g per cow per day at air temperatures of 2.3-22.4 degrees C. Ammonia emission rates estimated by the model were in the range of 19-107 g per cow per day for the surveyed buildings. The average ammonia emission estimated by the model was 11% higher than the mean measured value. The results show that predicted emission patterns generally agree with the measured one, but the prediction has less variation. The model performance may be improved if the influence of animal activity and management strategy on ammonia emission could be estimated and more reliable data of air velocities of the buildings could be obtained.

Predictive Models and Computational Embryology

EPA Science Inventory

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...

The propagation of sound in narrow street canyons

NASA Astrophysics Data System (ADS)

Iu, K. K.; Li, K. M.

2002-08-01

This paper addresses an important problem of predicting sound propagation in narrow street canyons with width less than 10 m, which are commonly found in a built-up urban district. Major noise sources are, for example, air conditioners installed on building facades and powered mechanical equipment for repair and construction work. Interference effects due to multiple reflections from building facades and ground surfaces are important contributions in these complex environments. Although the studies of sound transmission in urban areas can be traced back to as early as the 1960s, the resulting mathematical and numerical models are still unable to predict sound fields accurately in city streets. This is understandable because sound propagation in city streets involves many intriguing phenomena such as reflections and scattering at the building facades, diffusion effects due to recessions and protrusions of building surfaces, geometric spreading, and atmospheric absorption. This paper describes the development of a numerical model for the prediction of sound fields in city streets. To simplify the problem, a typical city street is represented by two parallel reflecting walls and a flat impedance ground. The numerical model is based on a simple ray theory that takes account of multiple reflections from the building facades. The sound fields due to the point source and its images are summed coherently such that mutual interference effects between contributing rays can be included in the analysis. Indoor experiments are conducted in an anechoic chamber. Experimental data are compared with theoretical predictions to establish the validity and usefulness of this simple model. Outdoor experimental measurements have also been conducted to further validate the model. copyright 2002 Acoustical Society of America.

Intelligent seismic risk mitigation system on structure building

NASA Astrophysics Data System (ADS)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

Building spatially-explicit model predictions for ecological condition of streams in the Pacific Northwest: An assessment of landscape variables, models, endpoints and prediction scale

EPA Science Inventory

While large-scale, randomized surveys estimate the percentage of a region’s streams in poor ecological condition, identifying particular stream reaches or watersheds in poor condition is an equally important goal for monitoring and management. We built predictive models of strea...

Analysis Methods for Post Occupancy Evaluation of Energy-Use in High Performance Buildings Using Short-Term Monitoring

NASA Astrophysics Data System (ADS)

Singh, Vipul

2011-12-01

The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of 'green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the general realm of post-occupancy evaluation (POE). POE involves evaluating building performance in terms of energy-use, indoor environmental quality, acoustics and water-use; the first aspect i.e. energy-use is addressed in this thesis. Normally, a full year or more of energy-use and weather data is required to determine the actual post-occupancy energy-use of buildings. In many cases, either measured building performance data is not available or the time and cost implications may not make it feasible to invest in monitoring the building for a whole year. Knowledge about the minimum amount of measured data needed to accurately capture the behavior of the building over the entire year can be immensely beneficial. This research identifies simple modeling techniques to determine best time of the year to begin in-situ monitoring of building energy-use, and the least amount of data required for generating acceptable long-term predictions. Four analysis procedures are studied. The short-term monitoring for long-term prediction (SMLP) approach and dry-bulb temperature analysis (DBTA) approach allow determining the best time and duration of the year for in-situ monitoring to be performed based only on the ambient temperature data of the location. Multivariate change-point (MCP) modeling uses simulated/monitored data to determine best monitoring period of the year. This is also used to validate the SMLP and DBTA approaches. The hybrid inverse modeling method-1 predicts energy-use by combining a short dataset of monitored internal loads with a year of utility-bills, and hybrid inverse method-2 predicts long term building performance using utility-bills only. The results obtained show that often less than three to four months of monitored data is adequate for estimating the annual building energy use, provided that the monitoring is initiated at the right time, and the seasonal as well as daily variations are adequately captured by the short dataset. The predictive accuracy of the short data-sets is found to be strongly influenced by the closeness of the dataset's mean temperature to the annual average temperature. The analysis methods studied would be very useful for energy professionals involved in POE.

Building-Scale Atmospheric Modeling for Understanding and Anticipating Environmental Risks to Urban Populations

NASA Astrophysics Data System (ADS)

Warner, T. T.; Swerdlin, S. P.; Chen, F.; Hayden, M.

2009-05-01

The innovative use of Computational Fluid-Dynamics (CFD) models to define the building- and street-scale atmospheric environment in urban areas can benefit society in a number of ways. Design criteria used by architectural climatologists, who help plan the livable cities of the future, require information about air movement within street canyons for different seasons and weather regimes. Understanding indoor urban air- quality problems and their mitigation, especially for older buildings, requires data on air movement and associated dynamic pressures near buildings. Learning how heat waves and anthropogenic forcing in cities collectively affect the health of vulnerable residents is a problem in building thermodynamics, human behavior, and neighborhood-scale and street-canyon-scale atmospheric sciences. And, predicting the movement of plumes of hazardous material released in urban industrial or transportation accidents requires detailed information about vertical and horizontal air motions in the street canyons. These challenges are closer to being addressed because of advances in CFD modeling, the coupling of CFD models with models of indoor air motion and air quality, and the coupling of CFD models with mesoscale weather-prediction models. This paper will review some of the new knowledge and technologies that are being developed to meet these atmospheric-environment needs of our growing urban populations.

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

PubMed

Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information.

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response: Preprint

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

Jin, Xin; Baker, Kyri A.; Christensen, Dane T.

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

User-Preference-Driven Model Predictive Control of Residential Building Loads and Battery Storage for Demand Response

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

Jin, Xin; Baker, Kyri A; Isley, Steven C

This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility andmore » reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.« less

A regression-based approach to estimating retrofit savings using the Building Performance Database

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

Walter, Travis; Sohn, Michael D.

Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofitsmore » empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.« less

Solar energy in buildings solved by building information modeling

NASA Astrophysics Data System (ADS)

Chudikova, B.; Faltejsek, M.

2018-03-01

Building lead us to use renewable energy sources for all types of buildings. The use of solar energy is the alternatives that can be applied in a good ratio of space, price, and resultant benefits. Building Information Modelling is a modern and effective way of dealing with buildings with regard to all aspects of the life cycle. The basis is careful planning and simulation in the pre-investment phase, where it is possible to determine the effective result and influence the lifetime of the building and the cost of its operation. By simulating, analysing and insert a building model into its future environment where climate conditions and surrounding buildings play a role, it is possible to predict the usability of the solar energy and establish an ideal model. Solar systems also very affect the internal layout of buildings. Pre-investment phase analysis, with a view to future aspects, will ensure that the resulting building will be both low-energy and environmentally friendly.

Generating Modeling Data From Repeat-Dose Toxicity Reports

PubMed Central

López-Massaguer, Oriol; Pinto-Gil, Kevin; Sanz, Ferran; Amberg, Alexander; Anger, Lennart T; Stolte, Manuela; Ravagli, Carlo

2018-01-01

Abstract Over the past decades, pharmaceutical companies have conducted a large number of high-quality in vivo repeat-dose toxicity (RDT) studies for regulatory purposes. As part of the eTOX project, a high number of these studies have been compiled and integrated into a database. This valuable resource can be queried directly, but it can be further exploited to build predictive models. As the studies were originally conducted to investigate the properties of individual compounds, the experimental conditions across the studies are highly heterogeneous. Consequently, the original data required normalization/standardization, filtering, categorization and integration to make possible any data analysis (such as building predictive models). Additionally, the primary objectives of the RDT studies were to identify toxicological findings, most of which do not directly translate to in vivo endpoints. This article describes a method to extract datasets containing comparable toxicological properties for a series of compounds amenable for building predictive models. The proposed strategy starts with the normalization of the terms used within the original reports. Then, comparable datasets are extracted from the database by applying filters based on the experimental conditions. Finally, carefully selected profiles of toxicological findings are mapped to endpoints of interest, generating QSAR-like tables. In this work, we describe in detail the strategy and tools used for carrying out these transformations and illustrate its application in a data sample extracted from the eTOX database. The suitability of the resulting tables for developing hazard-predicting models was investigated by building proof-of-concept models for in vivo liver endpoints. PMID:29155963

Predictive Models and Computational Toxicology (II IBAMTOX)

EPA Science Inventory

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...

Aerodynamic Parameters of a UK City Derived from Morphological Data

NASA Astrophysics Data System (ADS)

Millward-Hopkins, J. T.; Tomlin, A. S.; Ma, L.; Ingham, D. B.; Pourkashanian, M.

2013-03-01

Detailed three-dimensional building data and a morphometric model are used to estimate the aerodynamic roughness length z 0 and displacement height d over a major UK city (Leeds). Firstly, using an adaptive grid, the city is divided into neighbourhood regions that are each of a relatively consistent geometry throughout. Secondly, for each neighbourhood, a number of geometric parameters are calculated. Finally, these are used as input into a morphometric model that considers the influence of height variability to predict aerodynamic roughness length and displacement height. Predictions are compared with estimations made using standard tables of aerodynamic parameters. The comparison suggests that the accuracy of plan-area-density based tables is likely to be limited, and that height-based tables of aerodynamic parameters may be more accurate for UK cities. The displacement heights in the standard tables are shown to be lower than the current predictions. The importance of geometric details in determining z 0 and d is then explored. Height variability is observed to greatly increase the predicted values. However, building footprint shape only has a significant influence upon the predictions when height variability is not considered. Finally, we develop simple relations to quantify the influence of height variation upon predicted z 0 and d via the standard deviation of building heights. The difference in these predictions compared to the more complex approach highlights the importance of considering the specific shape of the building-height distributions. Collectively, these results suggest that to accurately predict aerodynamic parameters of real urban areas, height variability must be considered in detail, but it may be acceptable to make simple assumptions about building layout and footprint shape.

Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings' floor structural frames

NASA Astrophysics Data System (ADS)

Juszczyk, Michał

2018-04-01

This paper reports some results of the studies on the use of artificial intelligence tools for the purposes of cost estimation based on building information models. A problem of the cost estimates based on the building information models on a macro level supported by the ensembles of artificial neural networks is concisely discussed. In the course of the research a regression model has been built for the purposes of cost estimation of buildings' floor structural frames, as higher level elements. Building information models are supposed to serve as a repository of data used for the purposes of cost estimation. The core of the model is the ensemble of neural networks. The developed model allows the prediction of cost estimates with satisfactory accuracy.

Identification Approach to Alleviate Effects of Unmeasured Heat Gains for MIMO Building Thermal Systems

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

Cai, Jie; Kim, Donghun; Braun, James E.

It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances,more » and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.« less

Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

PubMed

DiMaio, Frank

2017-01-01

Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

Long-period building response to earthquakes in the San Francisco Bay Area

USGS Publications Warehouse

Olsen, A.H.; Aagaard, Brad T.; Heaton, T.H.

2008-01-01

This article reports a study of modeled, long-period building responses to ground-motion simulations of earthquakes in the San Francisco Bay Area. The earthquakes include the 1989 magnitude 6.9 Loma Prieta earthquake, a magnitude 7.8 simulation of the 1906 San Francisco earthquake, and two hypothetical magnitude 7.8 northern San Andreas fault earthquakes with hypocenters north and south of San Francisco. We use the simulated ground motions to excite nonlinear models of 20-story, steel, welded moment-resisting frame (MRF) buildings. We consider MRF buildings designed with two different strengths and modeled with either ductile or brittle welds. Using peak interstory drift ratio (IDR) as a performance measure, the stiffer, higher strength building models outperform the equivalent more flexible, lower strength designs. The hypothetical magnitude 7.8 earthquake with hypocenter north of San Francisco produces the most severe ground motions. In this simulation, the responses of the more flexible, lower strength building model with brittle welds exceed an IDR of 2.5% (that is, threaten life safety) on 54% of the urban area, compared to 4.6% of the urban area for the stiffer, higher strength building with ductile welds. We also use the simulated ground motions to predict the maximum isolator displacement of base-isolated buildings with linear, single-degree-of-freedom (SDOF) models. For two existing 3-sec isolator systems near San Francisco, the design maximum displacement is 0.5 m, and our simulations predict isolator displacements for this type of system in excess of 0.5 m in many urban areas. This article demonstrates that a large, 1906-like earthquake could cause significant damage to long-period buildings in the San Francisco Bay Area.

Effects of urban microcellular environments on ray-tracing-based coverage predictions.

PubMed

Liu, Zhongyu; Guo, Lixin; Guan, Xiaowei; Sun, Jiejing

2016-09-01

The ray-tracing (RT) algorithm, which is based on geometrical optics and the uniform theory of diffraction, has become a typical deterministic approach of studying wave-propagation characteristics. Under urban microcellular environments, the RT method highly depends on detailed environmental information. The aim of this paper is to provide help in selecting the appropriate level of accuracy required in building databases to achieve good tradeoffs between database costs and prediction accuracy. After familiarization with the operating procedures of the RT-based prediction model, this study focuses on the effect of errors in environmental information on prediction results. The environmental information consists of two parts, namely, geometric and electrical parameters. The geometric information can be obtained from a digital map of a city. To study the effects of inaccuracies in geometry information (building layout) on RT-based coverage prediction, two different artificial erroneous maps are generated based on the original digital map, and systematic analysis is performed by comparing the predictions with the erroneous maps and measurements or the predictions with the original digital map. To make the conclusion more persuasive, the influence of random errors on RMS delay spread results is investigated. Furthermore, given the electrical parameters' effect on the accuracy of the predicted results of the RT model, the dielectric constant and conductivity of building materials are set with different values. The path loss and RMS delay spread under the same circumstances are simulated by the RT prediction model.

Estimation of Model's Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Zeng, X.

2015-12-01

A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.

Toward a virtual building laboratory

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

Klems, J.H.; Finlayson, E.U.; Olsen, T.H.

1999-03-01

In order to achieve in a timely manner the large energy and dollar savings technically possible through improvements in building energy efficiency, it will be necessary to solve the problem of design failure risk. The most economical method of doing this would be to learn to calculate building performance with sufficient detail, accuracy and reliability to avoid design failure. Existing building simulation models (BSM) are a large step in this direction, but are still not capable of this level of modeling. Developments in computational fluid dynamics (CFD) techniques now allow one to construct a road map from present BSM's tomore » a complete building physical model. The most useful first step is a building interior model (BIM) that would allow prediction of local conditions affecting occupant health and comfort. To provide reliable prediction a BIM must incorporate the correct physical boundary conditions on a building interior. Doing so raises a number of specific technical problems and research questions. The solution of these within a context useful for building research and design is not likely to result from other research on CFD, which is directed toward the solution of different types of problems. A six-step plan for incorporating the correct boundary conditions within the context of the model problem of a large atrium has been outlined. A promising strategy for constructing a BIM is the overset grid technique for representing a building space in a CFD calculation. This technique promises to adapt well to building design and allows a step-by-step approach. A state-of-the-art CFD computer code using this technique has been adapted to the problem and can form the departure point for this research.« less

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Development and Testing of Building Energy Model Using Non-Linear Auto Regression Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Arida, Maya Ahmad

In 1972 sustainable development concept existed and during The years it became one of the most important solution to save natural resources and energy, but now with rising energy costs and increasing awareness of the effect of global warming, the development of building energy saving methods and models become apparently more necessary for sustainable future. According to U.S. Energy Information Administration EIA (EIA), today buildings in the U.S. consume 72 percent of electricity produced, and use 55 percent of U.S. natural gas. Buildings account for about 40 percent of the energy consumed in the United States, more than industry and transportation. Of this energy, heating and cooling systems use about 55 percent. If energy-use trends continue, buildings will become the largest consumer of global energy by 2025. This thesis proposes procedures and analysis techniques for building energy system and optimization methods using time series auto regression artificial neural networks. The model predicts whole building energy consumptions as a function of four input variables, dry bulb and wet bulb outdoor air temperatures, hour of day and type of day. The proposed model and the optimization process are tested using data collected from an existing building located in Greensboro, NC. The testing results show that the model can capture very well the system performance, and The optimization method was also developed to automate the process of finding the best model structure that can produce the best accurate prediction against the actual data. The results show that the developed model can provide results sufficiently accurate for its use in various energy efficiency and saving estimation applications.

Indoor Radon Concentration Related to Different Radon Areas and Indoor Radon Prediction

NASA Astrophysics Data System (ADS)

Juhásová Šenitková, Ingrid; Šál, Jiří

2017-12-01

Indoor radon has been observed in the buildings at areas with different radon risk potential. Preventive measures are based on control of main potential radon sources (soil gas, building material and supplied water) to avoid building of new houses above recommended indoor radon level 200 Bq/m3. Radon risk (index) estimation of individual building site bedrock in case of new house siting and building protection according technical building code are obligatory. Remedial actions in buildings built at high radon risk areas were carried out principally by unforced ventilation and anti-radon insulation. Significant differences were found in the level of radon concentration between rooms where radon reduction techniques were designed and those where it was not designed. The mathematical model based on radon exhalation from soil has been developed to describe the physical processes determining indoor radon concentration. The model is focused on combined radon diffusion through the slab and advection through the gap from sub-slab soil. In this model, radon emanated from building materials is considered not having a significant contribution to indoor radon concentration. Dimensional analysis and Gauss-Newton nonlinear least squares parametric regression were used to simplify the problem, identify essential input variables and find parameter values. The presented verification case study is introduced for real buildings with respect to various underground construction types. Presented paper gives picture of possible mathematical approach to indoor radon concentration prediction.

Challenges in microbial ecology: building predictive understanding of community function and dynamics

PubMed Central

Widder, Stefanie; Allen, Rosalind J; Pfeiffer, Thomas; Curtis, Thomas P; Wiuf, Carsten; Sloan, William T; Cordero, Otto X; Brown, Sam P; Momeni, Babak; Shou, Wenying; Kettle, Helen; Flint, Harry J; Haas, Andreas F; Laroche, Béatrice; Kreft, Jan-Ulrich; Rainey, Paul B; Freilich, Shiri; Schuster, Stefan; Milferstedt, Kim; van der Meer, Jan R; Groβkopf, Tobias; Huisman, Jef; Free, Andrew; Picioreanu, Cristian; Quince, Christopher; Klapper, Isaac; Labarthe, Simon; Smets, Barth F; Wang, Harris; Soyer, Orkun S

2016-01-01

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved. PMID:27022995

Simultaneous Co-Clustering and Classification in Customers Insight

NASA Astrophysics Data System (ADS)

Anggistia, M.; Saefuddin, A.; Sartono, B.

2017-04-01

Building predictive model based on the heterogeneous dataset may yield many problems, such as less precise in parameter and prediction accuracy. Such problem can be solved by segmenting the data into relatively homogeneous groups and then build a predictive model for each cluster. The advantage of using this strategy usually gives result in simpler models, more interpretable, and more actionable without any loss in accuracy and reliability. This work concerns on marketing data set which recorded a customer behaviour across products. There are some variables describing customer and product as attributes. The basic idea of this approach is to combine co-clustering and classification simultaneously. The objective of this research is to analyse the customer across product characteristics, so the marketing strategy implemented precisely.

Establishment of Low Energy Building materials and Equipment Database Based on Property Information

NASA Astrophysics Data System (ADS)

Kim, Yumin; Shin, Hyery; eon Lee, Seung

2018-03-01

The purpose of this study is to provide reliable service of materials information portal through the establishment of public big data by collecting and integrating scattered low energy building materials and equipment data. There were few cases of low energy building materials database in Korea have provided material properties as factors influencing material pricing. The framework of the database was defined referred with Korea On-line E-procurement system. More than 45,000 data were gathered by the specification of entities and with the gathered data, price prediction models for chillers were suggested. To improve the usability of the prediction model, detailed properties should be analysed for each item.

Developing Risk Prediction Models for Postoperative Pancreatic Fistula: a Systematic Review of Methodology and Reporting Quality.

PubMed

Wen, Zhang; Guo, Ya; Xu, Banghao; Xiao, Kaiyin; Peng, Tao; Peng, Minhao

2016-04-01

Postoperative pancreatic fistula is still a major complication after pancreatic surgery, despite improvements of surgical technique and perioperative management. We sought to systematically review and critically access the conduct and reporting of methods used to develop risk prediction models for predicting postoperative pancreatic fistula. We conducted a systematic search of PubMed and EMBASE databases to identify articles published before January 1, 2015, which described the development of models to predict the risk of postoperative pancreatic fistula. We extracted information of developing a prediction model including study design, sample size and number of events, definition of postoperative pancreatic fistula, risk predictor selection, missing data, model-building strategies, and model performance. Seven studies of developing seven risk prediction models were included. In three studies (42 %), the number of events per variable was less than 10. The number of candidate risk predictors ranged from 9 to 32. Five studies (71 %) reported using univariate screening, which was not recommended in building a multivariate model, to reduce the number of risk predictors. Six risk prediction models (86 %) were developed by categorizing all continuous risk predictors. The treatment and handling of missing data were not mentioned in all studies. We found use of inappropriate methods that could endanger the development of model, including univariate pre-screening of variables, categorization of continuous risk predictors, and model validation. The use of inappropriate methods affects the reliability and the accuracy of the probability estimates of predicting postoperative pancreatic fistula.

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE PAGES

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

2017-11-26

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Gradient boosting machine for modeling the energy consumption of commercial buildings

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

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

DOE PAGES

Nguyen, Marcus; Brettin, Thomas; Long, S. Wesley; ...

2018-01-11

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies aremore » >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.« less

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

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

Nguyen, Marcus; Brettin, Thomas; Long, S. Wesley

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies aremore » >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.« less

Fundamental mass transfer modeling of emission of volatile organic compounds from building materials

NASA Astrophysics Data System (ADS)

Bodalal, Awad Saad

In this study, a mass transfer theory based model is presented for characterizing the VOC emissions from building materials. A 3-D diffusion model is developed to describe the emissions of volatile organic compounds (VOCs) from individual sources. Then the formulation is extended to include the emissions from composite sources (system comprising an assemblage of individual sources). The key parameters for the model (The diffusion coefficient of the VOC in the source material D, and the equilibrium partition coefficient k e) were determined independently (model parameters are determined without the use of chamber emission data). This procedure eliminated to a large extent the need for emission testing using environmental chambers, which is costly, time consuming, and may be subject to confounding sink effects. An experimental method is developed and implemented to measure directly the internal diffusion (D) and partition coefficients ( ke). The use of the method is illustrated for three types of VOC's: (i) Aliphatic Hydrocarbons, (ii) Aromatic Hydrocarbons and ( iii) Aldehydes, through typical dry building materials (carpet, plywood, particleboard, vinyl floor tile, gypsum board, sub-floor tile and OSB). Then correlations for predicting D and ke based solely on commonly available properties such as molecular weight and vapour pressure were proposed for each product and type of VOC. These correlations can be used to estimate the D and ke when direct measurement data are not available, and thus facilitate the prediction of VOC emissions from the building materials using mass transfer theory. The VOC emissions from a sub-floor material (made of the recycled automobile tires), and a particleboard are measured and predicted. Finally, a mathematical model to predict the diffusion coefficient through complex sources (floor adhesive) as a function of time was developed. Then this model (for diffusion coefficient in complex sources) was used to predict the emission rate from material system (namely, substrate//glue//vinyl tile).

Building vulnerability and human loss assessment in different earthquake intensity and time: a case study of the University of the Philippines, Los Baños (UPLB) Campus

NASA Astrophysics Data System (ADS)

Rusydy, I.; Faustino-Eslava, D. V.; Muksin, U.; Gallardo-Zafra, R.; Aguirre, J. J. C.; Bantayan, N. C.; Alam, L.; Dakey, S.

2017-02-01

Study on seismic hazard, building vulnerability and human loss assessment become substantial for building education institutions since the building are used by a lot of students, lecturers, researchers, and guests. The University of the Philippines, Los Banos (UPLB) located in an earthquake prone area. The earthquake could cause structural damage and injury of the UPLB community. We have conducted earthquake assessment in different magnitude and time to predict the posibility of ground shaking, building vulnerability and estimated the number of casualty of the UPLB community. The data preparation in this study includes the earthquake scenario modeling using Intensity Prediction Equations (IPEs) for shallow crustal shaking attenuation to produce intensity map of bedrock and surface. Earthquake model was generated from the segment IV and the segment X of the Valley Fault System (VFS). Building vulnerability of different type of building was calculated using fragility curve of the Philippines building. The population data for each building in various occupancy time, damage ratio, and injury ratio data were used to compute the number of casualties. The result reveals that earthquake model from the segment IV and the segment X of the VFS could generate earthquake intensity between 7.6 - 8.1 MMI in the UPLB campus. The 7.7 Mw earthquake (scenario I) from the segment IV could cause 32% - 51% damage of building and 6.5 Mw earthquake (scenario II) occurring in the segment X could cause 18% - 39% structural damage of UPLB buildings. If the earthquake occurs at 2 PM (day-time), it could injure 10.2% - 18.8% for the scenario I and could injure 7.2% - 15.6% of UPLB population in scenario II. The 5 Pm event, predicted will injure 5.1%-9.4% in the scenario I, and 3.6%-7.8% in scenario II. A nighttime event (2 Am) cause injury to students and guests who stay in dormitories. The earthquake is predicted to injure 13 - 66 students and guests in the scenario I and 9 - 47 people in the scenario II. To reduce the number of injuries, the authority of UPLB have to carry out the buildings restoration, set up earthquake early warning system in all dormitories, and evacuation sign board.

Predicted carbonation of existing concrete building based on the Indonesian tropical micro-climate

NASA Astrophysics Data System (ADS)

Hilmy, M.; Prabowo, H.

2018-03-01

This paper is aimed to predict the carbonation progress based on the previous mathematical model. It shortly explains the nature of carbonation including the processes and effects. Environmental humidity and temperature of the existing concrete building are measured and compared to data from local Meteorological, Climatological, and Geophysical Agency. The data gained are expressed in the form of annual hygrothermal values which will use as the input parameter in carbonation model. The physical properties of the observed building such as its location, dimensions, and structural material used are quantified. These data then utilized as an important input parameter for carbonation coefficients. The relationships between relative humidity and the rate of carbonation established. The results can provide a basis for repair and maintenance of existing concrete buildings and the sake of service life analysis of them.

Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

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

Gregor P. Henze; Moncef Krarti

2005-09-30

Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulationmore » research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very simple short-term prediction models to realize almost all of the theoretical potential of this control strategy. Further work evaluated the impact of modeling accuracy on the model-based closed-loop predictive optimal controller to minimize utility cost. The following guidelines have been derived: For an internal heat gain dominated commercial building, reasonable geometry simplifications are acceptable without a loss of cost savings potential. In fact, zoning simplification may improve optimizer performance and save computation time. The mass of the internal structure did not show a strong effect on the optimization. Building construction characteristics were found to impact building passive thermal storage capacity. It is thus advisable to make sure the construction material is well modeled. Zone temperature setpoint profiles and TES performance are strongly affected by mismatches in internal heat gains, especially when they are underestimated. Since they are a key factor in determining the building cooling load, efforts should be made to keep the internal gain mismatch as small as possible. Efficiencies of the building energy systems affect both zone temperature setpoints and active TES operation because of the coupling of the base chiller for building precooling and the icemaking TES chiller. Relative efficiencies of the base and TES chillers will determine the balance of operation of the two chillers. The impact of mismatch in this category may be significant. Next, a parametric analysis was conducted to assess the effects of building mass, utility rate, building location and season, thermal comfort, central plant capacities, and an economizer on the cost saving performance of optimal control for active and passive building thermal storage inventory. The key findings are: (1) Heavy-mass buildings, strong-incentive time-of-use electrical utility rates, and large on-peak cooling loads will likely lead to attractive savings resulting from optimal combined thermal storage control. (2) By using economizer to take advantage of the cool fresh air during the night, the building electrical cost can be reduced by using less mechanical cooling. (3) Larger base chiller and active thermal storage capacities have the potential of shifting more cooling loads to off-peak hours and thus higher savings can be achieved. (4) Optimal combined thermal storage control with a thermal comfort penalty included in the objective function can improve the thermal comfort levels of building occupants when compared to the non-optimized base case. Lab testing conducted in the Larson HVAC Laboratory during Phase 2 showed that the EnergyPlus-based simulation was a surprisingly accurate prediction of the experiment. Therefore, actual savings of building energy costs can be expected by applying optimal controls from simulation results.« less

Consensus models to predict endocrine disruption for all ...

EPA Pesticide Factsheets

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment. It is well known that some environmental chemicals mimic natural hormones and thus have the potential to be endocrine disruptors. Most of these environmental chemicals have never been tested for their ability to disrupt the endocrine system, in particular, their ability to interact with the estrogen receptor. EPA needs tools to prioritize thousands of chemicals, for instance in the Endocrine Disruptor Screening Program (EDSP). Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) was intended to be a demonstration of the use of predictive computational models on HTS data including ToxCast and Tox21 assays to prioritize a large chemical universe of 32464 unique structures for one specific molecular target – the estrogen receptor. CERAPP combined multiple computational models for prediction of estrogen receptor activity, and used the predicted results to build a unique consensus model. Models were developed in collaboration between 17 groups in the U.S. and Europe and applied to predict the common set of chemicals. Structure-based techniques such as docking and several QSAR modeling approaches were employed, mostly using a common training set of 1677 compounds provided by U.S. EPA, to build a total of 42 classification models and 8 regression models for binding, agonist and antagonist activity. All predictions were evaluated on ToxCast data and on an exte

A modeling investigation of the impact of street and building configurations on personal air pollutant exposure in isolated deep urban canyons.

PubMed

Ng, Wai-Yin; Chau, Chi-Kwan

2014-01-15

This study evaluated the effectiveness of different configurations for two building design elements, namely building permeability and setback, proposed for mitigating air pollutant exposure problems in isolated deep canyons by using an indirect exposure approach. The indirect approach predicted the exposures of three different population subgroups (i.e. pedestrians, shop vendors and residents) by multiplying the pollutant concentrations with the duration of exposure within a specific micro-environment. In this study, the pollutant concentrations for different configurations were predicted using a computational fluid dynamics model. The model was constructed based on the Reynolds-Averaged Navier-Stokes (RANS) equations with the standard k-ε turbulence model. Fifty-one canyon configurations with aspect ratios of 2, 4, 6 and different building permeability values (ratio of building spacing to the building façade length) or different types of building setback (recess of a high building from the road) were examined. The findings indicated that personal exposures of shop vendors were extremely high if they were present inside a canyon without any setback or separation between buildings and when the prevailing wind was perpendicular to the canyon axis. Building separation and building setbacks were effective in reducing personal air exposures in canyons with perpendicular wind, although their effectiveness varied with different configurations. Increasing the permeability value from 0 to 10% significantly lowered the personal exposures on the different population subgroups. Likewise, the personal exposures could also be reduced by the introduction of building setbacks despite their effects being strongly influenced by the aspect ratio of a canyon. Equivalent findings were observed if the reduction in the total development floor area (the total floor area permitted to be developed within a particular site area) was also considered. These findings were employed to formulate a hierarchy decision making model to guide the planning of deep canyons in high density urban cities. © 2013 Elsevier B.V. All rights reserved.

Urban UV environment in a sub-tropical megacity - A measurement and modelling study

NASA Astrophysics Data System (ADS)

Wai, Ka-Ming; Yu, Peter K. N.; Chan, Pok-Man

The variations of solar total UV (UVA + UVB) exposure rates in a megacity featured with high-rise buildings during summer months were measured and relevant model predictions were evaluated. The maximum pedestrian-level total solar UV exposure rate was less than the un-obstructed exposure rate at any time, attributing to the prevailing reduction in the diffuse solar radiation due to the obstruction effects of distant buildings. Comparing with the measurements, our coupled model well captured the spatial and temporal variations of the reduction of UV exposure rates. By measurements, large reduction in the solar total UV exposure rate down to 12% of un-obstructed exposure rate due to the building obstruction effects was found, agreeing with our previous simulation results and results from an Australian megacity. On the other hand, building reflection from reflective curtain walls could reach 23% of the un-obstructed solar total UV exposure rate at the ground level. This implied improper building design creating additional harmful effects of solar UV radiation on the environment. The coupled model was also applied to predict the urban UV exposure rates during a tropical-cyclone induced aerosol episode. A well-evaluated urban solar UV model is an important tool for sustainable urban design.

Robustness of neuroprosthetic decoding algorithms.

PubMed

Serruya, Mijail; Hatsopoulos, Nicholas; Fellows, Matthew; Paninski, Liam; Donoghue, John

2003-03-01

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

Mass and stiffness estimation using mobile devices for structural health monitoring

NASA Astrophysics Data System (ADS)

Le, Viet; Yu, Tzuyang

2015-04-01

In the structural health monitoring (SHM) of civil infrastructure, dynamic methods using mass, damping, and stiffness for characterizing structural health have been a traditional and widely used approach. Changes in these system parameters over time indicate the progress of structural degradation or deterioration. In these methods, capability of predicting system parameters is essential to their success. In this paper, research work on the development of a dynamic SHM method based on perturbation analysis is reported. The concept is to use externally applied mass to perturb an unknown system and measure the natural frequency of the system. Derived theoretical expressions for mass and stiffness prediction are experimentally verified by a building model. Dynamic responses of the building model perturbed by various masses in free vibration were experimentally measured by a mobile device (cell phone) to extract the natural frequency of the building model. Single-degreeof- freedom (SDOF) modeling approach was adopted for the sake of using a cell phone. From the experimental result, it is shown that the percentage error of predicted mass increases when the mass ratio increases, while the percentage error of predicted stiffness decreases when the mass ratio increases. This work also demonstrated the potential use of mobile devices in the health monitoring of civil infrastructure.

The use of the logistic model in space motion sickness prediction

NASA Technical Reports Server (NTRS)

Lin, Karl K.; Reschke, Millard F.

1987-01-01

The one-equation and the two-equation logistic models were used to predict subjects' susceptibility to motion sickness in KC-135 parabolic flights using data from other ground-based motion sickness tests. The results show that the logistic models correctly predicted substantially more cases (an average of 13 percent) in the data subset used for model building. Overall, the logistic models ranged from 53 to 65 percent predictions of the three endpoint parameters, whereas the Bayes linear discriminant procedure ranged from 48 to 65 percent correct for the cross validation sample.

Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

NASA Astrophysics Data System (ADS)

Wang, Xin; Li, Yan; Chen, Tongjun; Yan, Qiuyan; Ma, Li

2017-04-01

The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes. At first, we build an ELM prediction model using the PCA attributes of a synthetic seismic section. The results suggest that the ELM model can produce a reliable and accurate prediction of the TDC thickness for synthetic data, preferring Sigmoid activation function and 20 hidden nodes. Then, we analyze the applicability of the ELM model on the thickness prediction of the TDC with real application data. Through the cross validation of near-well traces, the results suggest that the ELM model can produce a reliable and accurate prediction of the TDC. After that, we use 250 near-well traces from 10 wells to build an ELM predicting model and use the model to forecast the TDC thickness of the No. 15 coal in the study area using the PCA attributes as the inputs. Comparing the predicted results, it is noted that the trained ELM model with two selected PCA attributes yields better predication results than those from the other combinations of the attributes. Finally, the trained ELM model with real seismic data have a different number of hidden nodes (10) than the trained ELM model with synthetic seismic data. In summary, it is feasible to use an ELM model to predict the TDC thickness using the calculated PCA attributes as the inputs. However, the input attributes, the activation function and the number of hidden nodes in the ELM model should be selected and tested carefully based on individual application.

Evaluation of an ensemble of genetic models for prediction of a quantitative trait.

PubMed

Milton, Jacqueline N; Steinberg, Martin H; Sebastiani, Paola

2014-01-01

Many genetic markers have been shown to be associated with common quantitative traits in genome-wide association studies. Typically these associated genetic markers have small to modest effect sizes and individually they explain only a small amount of the variability of the phenotype. In order to build a genetic prediction model without fitting a multiple linear regression model with possibly hundreds of genetic markers as predictors, researchers often summarize the joint effect of risk alleles into a genetic score that is used as a covariate in the genetic prediction model. However, the prediction accuracy can be highly variable and selecting the optimal number of markers to be included in the genetic score is challenging. In this manuscript we present a strategy to build an ensemble of genetic prediction models from data and we show that the ensemble-based method makes the challenge of choosing the number of genetic markers more amenable. Using simulated data with varying heritability and number of genetic markers, we compare the predictive accuracy and inclusion of true positive and false positive markers of a single genetic prediction model and our proposed ensemble method. The results show that the ensemble of genetic models tends to include a larger number of genetic variants than a single genetic model and it is more likely to include all of the true genetic markers. This increased sensitivity is obtained at the price of a lower specificity that appears to minimally affect the predictive accuracy of the ensemble.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

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

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

A Predictive Model of Inquiry to Enrollment

ERIC Educational Resources Information Center

Goenner, Cullen F.; Pauls, Kenton

2006-01-01

The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an…

Modeling the capacity of riverscapes to support beaver dams

NASA Astrophysics Data System (ADS)

Macfarlane, William W.; Wheaton, Joseph M.; Bouwes, Nicolaas; Jensen, Martha L.; Gilbert, Jordan T.; Hough-Snee, Nate; Shivik, John A.

2017-01-01

The construction of beaver dams facilitates a suite of hydrologic, hydraulic, geomorphic, and ecological feedbacks that increase stream complexity and channel-floodplain connectivity that benefit aquatic and terrestrial biota. Depending on where beaver build dams within a drainage network, they impact lateral and longitudinal connectivity by introducing roughness elements that fundamentally change the timing, delivery, and storage of water, sediment, nutrients, and organic matter. While the local effects of beaver dams on streams are well understood, broader coverage network models that predict where beaver dams can be built and highlight their impacts on connectivity across diverse drainage networks are lacking. Here we present a capacity model to assess the limits of riverscapes to support dam-building activities by beaver across physiographically diverse landscapes. We estimated dam capacity with freely and nationally-available inputs to evaluate seven lines of evidence: (1) reliable water source, (2) riparian vegetation conducive to foraging and dam building, (3) vegetation within 100 m of edge of stream to support expansion of dam complexes and maintain large colonies, (4) likelihood that channel-spanning dams could be built during low flows, (5) the likelihood that a beaver dam is likely to withstand typical floods, (6) a suitable stream gradient that is neither too low to limit dam density nor too high to preclude the building or persistence of dams, and (7) a suitable river that is not too large to restrict dam building or persistence. Fuzzy inference systems were used to combine these controlling factors in a framework that explicitly also accounts for model uncertainty. The model was run for 40,561 km of streams in Utah, USA, and portions of surrounding states, predicting an overall network capacity of 356,294 dams at an average capacity of 8.8 dams/km. We validated model performance using 2852 observed dams across 1947 km of streams. The model showed excellent agreement with observed dam densities where beaver dams were present. Model performance was spatially coherent and logical, with electivity indices that effectively segregated capacity categories. That is, beaver dams were not found where the model predicted no dams could be supported, beaver avoided segments that were predicted to support rare or occasional densities, and beaver preferentially occupied and built dams in areas predicted to have pervasive dam densities. The resulting spatially explicit reach-scale (250 m long reaches) data identifies where dam-building activity is sustainable, and at what densities dams can occur across a landscape. As such, model outputs can be used to determine where channel-floodplain and wetland connectivity are likely to persist or expand by promoting increases in beaver dam densities.

Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging.

PubMed

Caporaso, Nicola; Whitworth, Martin B; Grebby, Stephen; Fisk, Ian D

2018-06-01

Hyperspectral imaging (1000-2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a "push-broom" system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320-350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry.

Round-robin analysis of the behavior of a 1:6-scale reinforced concrete containment model pressurized to failure: Posttest evaluations

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

Clauss, D.B.

A 1:6-scale model of a reinforced concrete containment building was pressurized incrementally to failure at a remote site at Sandia National Laboratories. The response of the model was recorded with more than 1000 channels of data (primarily strain and displacement measurements) at 37 discrete pressure levels. The primary objective of this test was to generate data that could be used to validate methods for predicting the performance of containment buildings subject to loads beyond their design basis. Extensive analyses were conducted before the test to predict the behavior of the model. Ten organizations in Europe and the US conducted independentmore » analyses of the model and contributed to a report on the pretest predictions. Predictions included structural response at certain predetermined locations in the model as well as capacity and failure mode. This report discusses comparisons between the pretest predictions and the experimental results. Posttest evaluations that were conducted to provide additional insight into the model behavior are also described. The significance of the analysis and testing of the 1:6-scale model to performance evaluations of actual containments subject to beyond design basis loads is also discussed. 70 refs., 428 figs., 24 tabs.« less

Demand response-enabled model predictive HVAC load control in buildings using real-time electricity pricing

NASA Astrophysics Data System (ADS)

Avci, Mesut

A practical cost and energy efficient model predictive control (MPC) strategy is proposed for HVAC load control under dynamic real-time electricity pricing. The MPC strategy is built based on a proposed model that jointly minimizes the total energy consumption and hence, cost of electricity for the user, and the deviation of the inside temperature from the consumer's preference. An algorithm that assigns temperature set-points (reference temperatures) to price ranges based on the consumer's discomfort tolerance index is developed. A practical parameter prediction model is also designed for mapping between the HVAC load and the inside temperature. The prediction model and the produced temperature set-points are integrated as inputs into the MPC controller, which is then used to generate signal actions for the AC unit. To investigate and demonstrate the effectiveness of the proposed approach, a simulation based experimental analysis is presented using real-life pricing data. An actual prototype for the proposed HVAC load control strategy is then built and a series of prototype experiments are conducted similar to the simulation studies. The experiments reveal that the MPC strategy can lead to significant reductions in overall energy consumption and cost savings for the consumer. Results suggest that by providing an efficient response strategy for the consumers, the proposed MPC strategy can enable the utility providers to adopt efficient demand management policies using real-time pricing. Finally, a cost-benefit analysis is performed to display the economic feasibility of implementing such a controller as part of a building energy management system, and the payback period is identified considering cost of prototype build and cost savings to help the adoption of this controller in the building HVAC control industry.

lazar: a modular predictive toxicology framework

PubMed Central

Maunz, Andreas; Gütlein, Martin; Rautenberg, Micha; Vorgrimmler, David; Gebele, Denis; Helma, Christoph

2013-01-01

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. PMID:23761761

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

A comprehensive pipeline for multi-resolution modeling of the mitral valve: Validation, computational efficiency, and predictive capability.

PubMed

Drach, Andrew; Khalighi, Amir H; Sacks, Michael S

2018-02-01

Multiple studies have demonstrated that the pathological geometries unique to each patient can affect the durability of mitral valve (MV) repairs. While computational modeling of the MV is a promising approach to improve the surgical outcomes, the complex MV geometry precludes use of simplified models. Moreover, the lack of complete in vivo geometric information presents significant challenges in the development of patient-specific computational models. There is thus a need to determine the level of detail necessary for predictive MV models. To address this issue, we have developed a novel pipeline for building attribute-rich computational models of MV with varying fidelity directly from the in vitro imaging data. The approach combines high-resolution geometric information from loaded and unloaded states to achieve a high level of anatomic detail, followed by mapping and parametric embedding of tissue attributes to build a high-resolution, attribute-rich computational models. Subsequent lower resolution models were then developed and evaluated by comparing the displacements and surface strains to those extracted from the imaging data. We then identified the critical levels of fidelity for building predictive MV models in the dilated and repaired states. We demonstrated that a model with a feature size of about 5 mm and mesh size of about 1 mm was sufficient to predict the overall MV shape, stress, and strain distributions with high accuracy. However, we also noted that more detailed models were found to be needed to simulate microstructural events. We conclude that the developed pipeline enables sufficiently complex models for biomechanical simulations of MV in normal, dilated, repaired states. Copyright © 2017 John Wiley & Sons, Ltd.

Concepts and tools for predictive modeling of microbial dynamics.

PubMed

Bernaerts, Kristel; Dens, Els; Vereecken, Karen; Geeraerd, Annemie H; Standaert, Arnout R; Devlieghere, Frank; Debevere, Johan; Van Impe, Jan F

2004-09-01

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

A Computational Workflow for the Automated Generation of Models of Genetic Designs.

PubMed

Misirli, Göksel; Nguyen, Tramy; McLaughlin, James Alastair; Vaidyanathan, Prashant; Jones, Timothy S; Densmore, Douglas; Myers, Chris; Wipat, Anil

2018-06-05

Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modeling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models have still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoded using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Methodology for estimating human perception to tremors in high-rise buildings

NASA Astrophysics Data System (ADS)

Du, Wenqi; Goh, Key Seng; Pan, Tso-Chien

2017-07-01

Human perception to tremors during earthquakes in high-rise buildings is usually associated with psychological discomfort such as fear and anxiety. This paper presents a methodology for estimating the level of perception to tremors for occupants living in high-rise buildings subjected to ground motion excitations. Unlike other approaches based on empirical or historical data, the proposed methodology performs a regression analysis using the analytical results of two generic models of 15 and 30 stories. The recorded ground motions in Singapore are collected and modified for structural response analyses. Simple predictive models are then developed to estimate the perception level to tremors based on a proposed ground motion intensity parameter—the average response spectrum intensity in the period range between 0.1 and 2.0 s. These models can be used to predict the percentage of occupants in high-rise buildings who may perceive the tremors at a given ground motion intensity. Furthermore, the models are validated with two recent tremor events reportedly felt in Singapore. It is found that the estimated results match reasonably well with the reports in the local newspapers and from the authorities. The proposed methodology is applicable to urban regions where people living in high-rise buildings might feel tremors during earthquakes.

Auto-DR and Pre-cooling of Buildings at Tri-City Corporate Center

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

Yin, Rongxin; Xu, Peng; Kiliccote, Sila

2008-11-01

Over the several past years, Lawrence Berkeley National Laboratory (LBNL) has conducted field tests for different pre-cooling strategies in different commercial buildings within California. The test results indicated that pre-cooling strategies were effective in reducing electric demand in these buildings during peak periods. This project studied how to optimize pre-cooling strategies for eleven buildings in the Tri-City Corporate Center, San Bernardino, California with the assistance of a building energy simulation tool -- the Demand Response Quick Assessment Tool (DRQAT) developed by LBNL's Demand Response Research Center funded by the California Energy Commission's Public Interest Energy Research (PIER) Program. From themore » simulation results of these eleven buildings, optimal pre-cooling and temperature reset strategies were developed. The study shows that after refining and calibrating initial models with measured data, the accuracy of the models can be greatly improved and the models can be used to predict load reductions for automated demand response (Auto-DR) events. This study summarizes the optimization experience of the procedure to develop and calibrate building models in DRQAT. In order to confirm the actual effect of demand response strategies, the simulation results were compared to the field test data. The results indicated that the optimal demand response strategies worked well for all buildings in the Tri-City Corporate Center. This study also compares DRQAT with other building energy simulation tools (eQUEST and BEST). The comparison indicate that eQUEST and BEST underestimate the actual demand shed of the pre-cooling strategies due to a flaw in DOE2's simulation engine for treating wall thermal mass. DRQAT is a more accurate tool in predicting thermal mass effects of DR events.« less

Simulating the effect of slab features on vapor intrusion of crack entry

PubMed Central

Yao, Yijun; Pennell, Kelly G.; Suuberg, Eric M.

2012-01-01

In vapor intrusion screening models, a most widely employed assumption in simulating the entry of contaminant into a building is that of a crack in the building foundation slab. Some modelers employed a perimeter crack hypothesis while others chose not to identify the crack type. However, few studies have systematically investigated the influence on vapor intrusion predictions of slab crack features, such as the shape and distribution of slab cracks and related to this overall building foundation footprint size. In this paper, predictions from a three-dimensional model of vapor intrusion are used to compare the contaminant mass flow rates into buildings with different foundation slab crack features. The simulations show that the contaminant mass flow rate into the building does not change much for different assumed slab crack shapes and locations, and the foundation footprint size does not play a significant role in determining contaminant mass flow rate through a unit area of crack. Moreover, the simulation helped reveal the distribution of subslab contaminant soil vapor concentration beneath the foundation, and the results suggest that in most cases involving no biodegradation, the variation in subslab concentration should not exceed an order of magnitude, and is often significantly less than this. PMID:23359620

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

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

Heo, Yeonsook; Augenbroe, Godfried; Graziano, Diane

2015-05-01

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustratesmore » both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.« less

Three-dimensional vapor intrusion modeling approach that combines wind and stack effects on indoor, atmospheric, and subsurface domains.

PubMed

Shirazi, Elham; Pennell, Kelly G

2017-12-13

Vapor intrusion (IV) exposure risks are difficult to characterize due to the role of atmospheric, building and subsurface processes. This study presents a three-dimensional VI model that extends the common subsurface fate and transport equations to incorporate wind and stack effects on indoor air pressure, building air exchange rate (AER) and indoor contaminant concentration to improve VI exposure risk estimates. The model incorporates three modeling programs: (1) COMSOL Multiphysics to model subsurface fate and transport processes, (2) CFD0 to model atmospheric air flow around the building, and (3) CONTAM to model indoor air quality. The combined VI model predicts AER values, zonal indoor air pressures and zonal indoor air contaminant concentrations as a function of wind speed, wind direction and outdoor and indoor temperature. Steady state modeling results for a single-story building with a basement demonstrate that wind speed, wind direction and opening locations in a building play important roles in changing the AER, indoor air pressure, and indoor air contaminant concentration. Calculated indoor air pressures ranged from approximately -10 Pa to +4 Pa depending on weather conditions and building characteristics. AER values, mass entry rates and indoor air concentrations vary depending on weather conditions and building characteristics. The presented modeling approach can be used to investigate the relationship between building features, AER, building pressures, soil gas concentrations, indoor air concentrations and VI exposure risks.

The Five Key Questions of Human Performance Modeling.

PubMed

Wu, Changxu

2018-01-01

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

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

[A prediction model for internet game addiction in adolescents: using a decision tree analysis].

PubMed

Kim, Ki Sook; Kim, Kyung Hee

2010-06-01

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

Modelling the effect of temperature, water activity and pH on the growth of Serpula lacrymans.

PubMed

Maurice, S; Coroller, L; Debaets, S; Vasseur, V; Le Floch, G; Barbier, G

2011-12-01

To predict the risk factors for building infestation by Serpula lacrymans, which is one of the most destructive fungi causing timber decay in buildings. The growth rate was assessed on malt extract agar media at temperatures between 1.5 and 45°C, at water activity (a(w)) over the range of 0.800-0.993 and at pH ranges from 1.5 to 11.0. The radial growth rate (μ) and the lag phase (λ) were estimated from the radial growth kinetics via the plots radius vs time. These parameters were then modelled as a function of the environmental factors tested. Models derived from the cardinal model (CM) were used to fit the experimental data and allowed an estimation of the optimal and limit values for fungal growth. Optimal growth rate occurred at 20°C, at high a(w) level (0.993) and at a pH range between 4.0 and 6.0. The strain effect on the temperature parameters was further evaluated using 14 strains of S. lacrymans. The robustness of the temperature model was validated on data sets measured in two different wood-based media (Quercus robur L. and Picea abies). The two-step procedure of exponential model with latency followed by the CM with inflection gives reliable predictions for the growth conditions of a filamentous fungus in our study. The procedure was validated for the study of abiotic factors on the growth rate of S. lacrymans. This work describes the usefulness of evaluating the effect of physico-chemical factors on fungal growth in predictive building mycology. Consequently, the developed mathematical models for predicting fungal growth on a macroscopic scale can be used as a tool for risk assessment of timber decay in buildings. © 2011 The Authors. Journal of Applied Microbiology © 2011 The Society for Applied Microbiology.

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

High resolution tempo-spatial ozone prediction with SVM and LSTM

NASA Astrophysics Data System (ADS)

Gao, D.; Zhang, Y.; Qu, Z.; Sadighi, K.; Coffey, E.; LIU, Q.; Hannigan, M.; Henze, D. K.; Dick, R.; Shang, L.; Lv, Q.

2017-12-01

To investigate and predict the exposure of ozone and other pollutants in urban areas, we utilize data from various infrastructures including EPA, NOAA and RIITS from government of Los Angeles and construct statistical models to conduct ozone concentration prediction in Los Angeles areas at finer spatial and temporal granularity. Our work involves cyber data such as traffic, roads and population data as features for prediction. Two statistical models, Support Vector Machine (SVM) and Long Short-term Memory (LSTM, deep learning method) are used for prediction. . Our experiments show that kernelized SVM gains better prediction performance when taking traffic counts, road density and population density as features, with a prediction RMSE of 7.99 ppb for all-time ozone and 6.92 ppb for peak-value ozone. With simulated NOx from Chemical Transport Model(CTM) as features, SVM generates even better prediction performance, with a prediction RMSE of 6.69ppb. We also build LSTM, which has shown great advantages at dealing with temporal sequences, to predict ozone concentration by treating ozone concentration as spatial-temporal sequences. Trained by ozone concentration measurements from the 13 EPA stations in LA area, the model achieves 4.45 ppb RMSE. Besides, we build a variant of this model which adds spatial dynamics into the model in the form of transition matrix that reveals new knowledge on pollutant transition. The forgetting gate of the trained LSTM is consistent with the delay effect of ozone concentration and the trained transition matrix shows spatial consistency with the common direction of winds in LA area.

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

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

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

The potential of large studies for building genetic risk prediction models

Cancer.gov

NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

Technological aspects of lift-slab method in high-rise-building construction.

NASA Astrophysics Data System (ADS)

Gaidukov, Pavel V.; Pugach, Evgeny M.

2018-03-01

The utilization efficiency of slab lifting technology for high-rise-building construction is regarded in the present article. The main problem of the article is organizing technology abilities indication, which proves the method application possibility. There is the comparing of lifting technologies and sequential concrete-frame extension, as follows: the first one: the parameters are defined, and the second one: the organizational model is executed. This model defines borders of the usage methods, as well. There is the mathematic model creating, which describes boundary conditions of the present technologies usage. This model allows to predict construction efficiency for different stored-number buildings.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

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

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

NASA Astrophysics Data System (ADS)

Burger, Eric M.

This dissertation presents techniques for the numerical modeling and control of building systems, with an emphasis on thermostatically controlled loads. The primary objective of this work is to address technical challenges related to the management of energy use in commercial and residential buildings. This work is motivated by the need to enhance the performance of building systems and by the potential for aggregated loads to perform load following and regulation ancillary services, thereby enabling the further adoption of intermittent renewable energy generation technologies. To increase the generalizability of the techniques, an emphasis is placed on recursive and adaptive methods which minimize the need for customization to specific buildings and applications. The techniques presented in this dissertation can be divided into two general categories: modeling and control. Modeling techniques encompass the processing of data streams from sensors and the training of numerical models. These models enable us to predict the energy use of a building and of sub-systems, such as a heating, ventilation, and air conditioning (HVAC) unit. Specifically, we first present an ensemble learning method for the short-term forecasting of total electricity demand in buildings. As the deployment of intermittent renewable energy resources continues to rise, the generation of accurate building-level electricity demand forecasts will be valuable to both grid operators and building energy management systems. Second, we present a recursive parameter estimation technique for identifying a thermostatically controlled load (TCL) model that is non-linear in the parameters. For TCLs to perform demand response services in real-time markets, online methods for parameter estimation are needed. Third, we develop a piecewise linear thermal model of a residential building and train the model using data collected from a custom-built thermostat. This model is capable of approximating unmodeled dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

NASA's Prediction Of Worldwide Energy Resource (POWER) Project Unveils a New Geospatial Data Portal

Atmospheric Science Data Center

2018-03-01

The Prediction Of Worldwide Energy Resource (POWER) Project facilitates access to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications.  A   new ...

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

NASA Astrophysics Data System (ADS)

Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

2008-06-01

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

Human Behavior & Low Energy Architecture: Linking Environmental Adaptation, Personal Comfort, & Energy Use in the Built Environment

NASA Astrophysics Data System (ADS)

Langevin, Jared

Truly sustainable buildings serve to enrich the daily sensory experience of their human inhabitants while consuming the least amount of energy possible; yet, building occupants and their environmentally adaptive behaviors remain a poorly characterized variable in even the most "green" building design and operation approaches. This deficiency has been linked to gaps between predicted and actual energy use, as well as to eventual problems with occupant discomfort, productivity losses, and health issues. Going forward, better tools are needed for considering the human-building interaction as a key part of energy efficiency strategies that promote good Indoor Environmental Quality (IEQ) in buildings. This dissertation presents the development and implementation of a Human and Building Interaction Toolkit (HABIT), a framework for the integrated simulation of office occupants' thermally adaptive behaviors, IEQ, and building energy use as part of sustainable building design and operation. Development of HABIT begins with an effort to devise more reliable methods for predicting individual occupants' thermal comfort, considered the driving force behind the behaviors of focus for this project. A long-term field study of thermal comfort and behavior is then presented, and the data it generates are used to develop and validate an agent-based behavior simulation model. Key aspects of the agent-based behavior model are described, and its predictive abilities are shown to compare favorably to those of multiple other behavior modeling options. Finally, the agent-based behavior model is linked with whole building energy simulation in EnergyPlus, forming the full HABIT program. The program is used to evaluate the energy and IEQ impacts of several occupant behavior scenarios in the simulation of a case study office building for the Philadelphia climate. Results indicate that more efficient local heating/cooling options may be paired with wider set point ranges to yield up to 24/28% HVAC energy savings in the winter/summer while also reducing thermal unacceptability; however, it is shown that the source of energy being saved must be considered in each case, as local heating options end up replacing cheaper, more carbon-friendly gas heating with expensive, emissions-heavy plug load electricity. The dissertation concludes with a summary of key outcomes and suggests how HABIT may be further developed in the future.

RCrane: semi-automated RNA model building.

PubMed

Keating, Kevin S; Pyle, Anna Marie

2012-08-01

RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems.

United Space Alliance LLC Parachute Refurbishment Facility Model

NASA Technical Reports Server (NTRS)

Esser, Valerie; Pessaro, Martha; Young, Angela

2007-01-01

The Parachute Refurbishment Facility Model was created to reflect the flow of hardware through the facility using anticipated start and delivery times from a project level IV schedule. Distributions for task times were built using historical build data for SFOC work and new data generated for CLV/ARES task times. The model currently processes 633 line items from 14 SFOC builds for flight readiness, 16 SFOC builds returning from flight for defoul, wash, and dry operations, 12 builds for CLV manufacturing operations, and 1 ARES 1X build. Modeling the planned workflow through the PRF is providing a reliable way to predict the capability of the facility as well as the manpower resource need. Creating a real world process allows for real world problems to be identified and potential workarounds to be implemented in a safe, simulated world before taking it to the next step, implementation in the real world.

The analysis of thermal comfort requirements through the simulation of an occupied building.

PubMed

Thellier, F; Cordier, A; Monchoux, F

1994-05-01

Building simulation usually focuses on the study of physical indoor parameters, but we must not forget the main aim of a house: to provide comfort to the occupants. This study was undertaken in order to build a complete tool to model thermal behaviour that will enable the prediction of thermal sensations of humans in a real environment. A human thermoregulation model was added to TRNSYS, a building simulation program. For our purposes, improvements had to be made to the original physiological model, by refining the calculation of all heat exchanges with the environment and adding a representation of clothes. This paper briefly describes the program, its modifications, and compares its results with experimental ones. An example of potential use is given, which points out the usefulness of such models in seeking the best solutions to reach optimal environmental conditions for global, and specially local comfort, of building occupants.

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

Building Bridges between Neuroscience, Cognition and Education with Predictive Modeling

ERIC Educational Resources Information Center

Stringer, Steve; Tommerdahl, Jodi

2015-01-01

As the field of Mind, Brain, and Education seeks new ways to credibly bridge the gap between neuroscience, the cognitive sciences, and education, various connections are being developed and tested. This article presents a framework and offers examples of one approach, predictive modeling within a virtual educational system that can include…

Limiting conditions for decay in wood systems

Treesearch

Paul I. Morris; Jerrold E. Winandy

2002-01-01

Hygrothermal models can predict temperature and moisture conditions in wall components subjected to real weather data, but specific data and a fundamental understanding of how temperature and wood moisture content dictate the progression of decay under these conditions is required for modellers to predict consequences of decay on building performance. It is well...

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

A Learning Framework for Control-Oriented Modeling of Buildings

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

Rubio-Herrero, Javier; Chandan, Vikas; Siegel, Charles M.

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and bigmore » data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.« less

Investigations of VOCs in and around buildings close to service stations

NASA Astrophysics Data System (ADS)

Hicklin, William; Farrugia, Pierre S.; Sinagra, Emmanuel

2018-01-01

Gas service stations are one of the major sources of volatile organic compounds in urban environments. Their emissions are expected not only to affect the ambient air quality but also that in any nearby buildings. This is particularly the case in Malta where most service stations have been built within residential zones. For this reason, it is important to understand the dispersion of volatile organic compounds (VOCs) from service stations and their infiltration into nearby residences. Two models were considered; one to predict the dispersion of VOCs in the outdoor environment in the vicinity of the service station and another one to predict the filtration of the compounds indoors. The two models can be used in tandem to predict the concentration of indoor VOCs that originate from a service station in the vicinity. Outdoor and indoor concentrations of VOCs around a service station located in a street canyon were measured, and the results used to validate the models. Predictions made using the models were found to be in general agreement with the measured concentrations of the pollutants.

Characterizing ground motions that collapse steel special moment-resisting frames or make them unrepairable

USGS Publications Warehouse

Olsen, Anna H.; Heaton, Thomas H.; Hall, John F.

2015-01-01

This work applies 64,765 simulated seismic ground motions to four models each of 6- or 20-story, steel special moment-resisting frame buildings. We consider two vector intensity measures and categorize the building response as “collapsed,” “unrepairable,” or “repairable.” We then propose regression models to predict the building responses from the intensity measures. The best models for “collapse” or “unrepairable” use peak ground displacement and velocity as intensity measures, and the best models predicting peak interstory drift ratio, given that the frame model is “repairable,” use spectral acceleration and epsilon (ϵ) as intensity measures. The more flexible frame is always more likely than the stiffer frame to “collapse” or be “unrepairable.” A frame with fracture-prone welds is substantially more susceptible to “collapse” or “unrepairable” damage than the equivalent frame with sound welds. The 20-story frames with fracture-prone welds are more vulnerable to P-delta instability and have a much higher probability of collapse than do any of the 6-story frames.

How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

PubMed

Ichikawa, Daisuke; Saito, Toki; Ujita, Waka; Oyama, Hiroshi

2016-12-01

Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test. Data were collected during annual health check-ups performed in Japan between 2011 and 2013 (inclusive). We prepared training and test datasets from the health check-up data to build prediction models; these were composed of 43,524 and 17,789 persons, respectively. Gradient-boosting decision tree (GBDT), random forest (RF), and logistic regression (LR) approaches were trained using the training dataset and were then used to predict hyperuricemia in the test dataset. Undersampling was applied to build the prediction models to deal with the imbalanced class dataset. The results showed that the RF and GBDT approaches afforded the best performances in terms of sensitivity and specificity, respectively. The area under the curve (AUC) values of the models, which reflected the total discriminative ability of the classification, were 0.796 [95% confidence interval (CI): 0.766-0.825] for the GBDT, 0.784 [95% CI: 0.752-0.815] for the RF, and 0.785 [95% CI: 0.752-0.819] for the LR approaches. No significant differences were observed between pairs of each approach. Small changes occurred in the AUCs after applying undersampling to build the models. We developed a virtual health check-up that predicted the development of hyperuricemia using machine-learning methods. The GBDT, RF, and LR methods had similar predictive capability. Undersampling did not remarkably improve predictive power. Copyright © 2016 Elsevier Inc. All rights reserved.

A concept of integrated environmental approach for building upgrades and new construction: Part 1—setting the stage

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

Bomberg, Mark; Gibson, Michael; Zhang, Jian

This article highlights the need for an active role for building physics in the development of near-zero energy buildings while analyzing an example of an integrated system for the upgrade of existing buildings. The science called either Building Physics in Europe or Building Science in North America has so far a passive role in explaining observed failures in construction practice. In its new role, it would be integrating modeling and testing to provide predictive capability, so much needed in the development of near-zero energy buildings. The authors attempt to create a compact package, applicable to different climates with small modificationsmore » of some hygrothermal properties of materials. This universal solution is based on a systems approach that is routine for building physics but in contrast to separately conceived sub-systems that are typical for the design of buildings today. One knows that the building structure, energy efficiency, indoor environmental quality, and moisture management all need to be considered to ensure durability of materials and control cost of near-zero energy buildings. These factors must be addressed through contributions of the whole design team. The same approach must be used for the retrofit of buildings. As this integrated design paradigm resulted from demands of sustainable built environment approach, building physics must drop its passive role and improve two critical domains of analysis: (i) linked, real-time hygrothermal and energy models capable of predicting the performance of existing buildings after renovation and (ii) basic methods of indoor environment and moisture management when the exterior of the building cannot be modified.« less

The Extravert Advantage: How and When Extraverts Build Rapport With Other People.

PubMed

Duffy, Korrina A; Chartrand, Tanya L

2015-11-01

Extraverts are better than introverts at building rapport, but it remains unknown what they do behaviorally to better connect with other individuals. We hypothesized that extraverts mimic more than introverts as a way to build rapport; however, we predicted that this social skillfulness of extraverts emerges only when they are motivated to affiliate. In Study 1, we found that extraversion predicted increased mimicry when an affiliation goal was present, but not when an affiliation goal was absent. In Study 2, we found that mimicry mediates the relationship between extraversion and rapport, but only when an affiliation goal is present. Our findings are the first to identify a behavior that extraverts engage in that helps them build rapport. Furthermore, our studies show that this skillfulness of extraverts emerges only when they are motivated to affiliate, providing evidence in favor of the reward-sensitivity-as-core model of extraversion over the sociability-as-core model of extraversion. © The Author(s) 2015.

Metering Best Practices Applied in the National Renewable Energy Laboratory's Research Support Facility: A Primer to the 2011 Measured and Modeled Energy Consumption Datasets

DOE Data Explorer

Sheppy, Michael; Beach, A.; Pless, Shanti

2016-08-09

Modern buildings are complex energy systems that must be controlled for energy efficiency. The Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL) has hundreds of controllers -- computers that communicate with the building's various control systems -- to control the building based on tens of thousands of variables and sensor points. These control strategies were designed for the RSF's systems to efficiently support research activities. Many events that affect energy use cannot be reliably predicted, but certain decisions (such as control strategies) must be made ahead of time. NREL researchers modeled the RSF systems to predict how they might perform. They then monitor these systems to understand how they are actually performing and reacting to the dynamic conditions of weather, occupancy, and maintenance.

A Comparison of Two Balance Calibration Model Building Methods

NASA Technical Reports Server (NTRS)

DeLoach, Richard; Ulbrich, Norbert

2007-01-01

Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.

Using simplifications of reality in the real world: Robust benefits of models for decision making

NASA Astrophysics Data System (ADS)

Hunt, R. J.

2008-12-01

Models are by definition simplifications of reality; the degree and nature of simplification, however, is debated. One view is "the world is 3D, heterogeneous, and transient, thus good models are too" - the more a model directly simulates the complexity of the real world the better it is considered to be. An alternative view is to only use simple models up front because real-world complexity can never be truly known. A third view is construct and calibrate as many models as predictions. A fourth is to build highly parameterized models and either look at an ensemble of results, or use mathematical regularization to identify an optimal most reasonable parameter set and fit. Although each view may have utility for a given decision-making process, there are common threads that perhaps run through all views. First, the model-construction process itself can help the decision-making process because it raises the discussion of opposing parties from one of contrasting professional opinions to discussion of reasonable types and ranges of model inputs and processes. Secondly, no matter what view is used to guide the model building, model predictions for the future might be expected to perform poorly in the future due to unanticipated future changes and stressors to the underlying system simulated. Although this does not reduce the obligation of the modeler to build representative tools for the system, it should serve to temper expectations of model performance. Finally, perhaps the most under-appreciated utility of models is for calculating the reduction in prediction uncertainty resulting from different data collection strategies - an attractive feature separate from the calculation and minimization of absolute prediction uncertainty itself. This type of model output facilitates focusing on efficient use of current and future monitoring resources - something valued by many decision-makers regardless of background, system managed, and societal context.

Data-driven modeling, control and tools for cyber-physical energy systems

NASA Astrophysics Data System (ADS)

Behl, Madhur

Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system's dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Thermal Destruction Of CB Contaminants Bound On Building ...

EPA Pesticide Factsheets

Symposium Paper An experimental and theoretical program has been initiated by the U.S. EPA to investigate issues of chemical/biological agent destruction in incineration systems when the agent in question is bound on common porous building interior materials. This program includes 3-dimensional computational fluid dynamics modeling with matrix-bound agent destruction kinetics, bench-scale experiments to determine agent destruction kinetics while bound on various matrices, and pilot-scale experiments to scale-up the bench-scale experiments to a more practical scale. Finally, model predictions are made to predict agent destruction and combustion conditions in two full-scale incineration systems that are typical of modern combustor design.

An evaluation of the accuracy of the Federal Transit Administration approved, groundborne noise and vibration prediction model for rail transit systems; a case study for a new subway line using post construction, transit operation measurements

NASA Astrophysics Data System (ADS)

Carman, Richard A.; Reyes, Carlos H.

2005-09-01

The groundborne noise and vibration model developed by Nelson and Saurenman in 1984, now recognized by the Federal Transit Administration as the approved model for new transit system facilities, is entering its third decade of use by engineers and consultants in the transit industry. The accuracy of the model has been explored in the past (e.g., Carman and Wolfe). New data obtained for a recently completed extension to a major heavy rail transit system provides an opportunity to evaluate the accuracy of the model once more. During the engineering design phase of the project, noise and vibration predictions were performed for numerous buildings adjacent to the new subway line. The values predicted by the model were used to determine the need for and type of noise and/or vibration control measures. After the start of transit operations on the new line, noise and vibration measurements were made inside several of the buildings to determine whether the criteria were in fact achieved. The measurement results are compared with the values predicted by the model. The predicted and measured, overall noise and vibration levels show very good agreement, whereas the spectral comparisons indicate some differences. Possible reasons for these differences are offered.

Review of Methods for Buildings Energy Performance Modelling

NASA Astrophysics Data System (ADS)

Krstić, Hrvoje; Teni, Mihaela

2017-10-01

Research presented in this paper gives a brief review of methods used for buildings energy performance modelling. This paper gives also a comprehensive review of the advantages and disadvantages of available methods as well as the input parameters used for modelling buildings energy performance. European Directive EPBD obliges the implementation of energy certification procedure which gives an insight on buildings energy performance via exiting energy certificate databases. Some of the methods for buildings energy performance modelling mentioned in this paper are developed by employing data sets of buildings which have already undergone an energy certification procedure. Such database is used in this paper where the majority of buildings in the database have already gone under some form of partial retrofitting - replacement of windows or installation of thermal insulation but still have poor energy performance. The case study presented in this paper utilizes energy certificates database obtained from residential units in Croatia (over 400 buildings) in order to determine the dependence between buildings energy performance and variables from database by using statistical dependencies tests. Building energy performance in database is presented with building energy efficiency rate (from A+ to G) which is based on specific annual energy needs for heating for referential climatic data [kWh/(m2a)]. Independent variables in database are surfaces and volume of the conditioned part of the building, building shape factor, energy used for heating, CO2 emission, building age and year of reconstruction. Research results presented in this paper give an insight in possibilities of methods used for buildings energy performance modelling. Further on it gives an analysis of dependencies between buildings energy performance as a dependent variable and independent variables from the database. Presented results could be used for development of new building energy performance predictive model.

Prediction models for CO2 emission in Malaysia using best subsets regression and multi-linear regression

NASA Astrophysics Data System (ADS)

Tan, C. H.; Matjafri, M. Z.; Lim, H. S.

2015-10-01

This paper presents the prediction models which analyze and compute the CO2 emission in Malaysia. Each prediction model for CO2 emission will be analyzed based on three main groups which is transportation, electricity and heat production as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data and followed by multi linear regression to produce the prediction models. From the results, high R-square (prediction) value was obtained and this implies that the models are reliable to predict the CO2 emission by using specific data. In addition, the CO2 emissions from these three groups are forecasted using trend analysis plots for observation purpose.

Framework for Smart Electronic Health Record- Linked Predictive Models to Optimize Care for Complex Digestive Diseases

DTIC Science & Technology

2013-08-01

surgeries, hospitalizations, etc). Once our model is developed we hope to apply our model at an outside institution, specifically University of...to build predictive models with the hope of improving disease management. It is difficult to find these factors in EMR systems as the...death, surgeries, hospitalizations, etc.) Once our model is developed, we hope to apply the model to de-identified data set from the University of

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

PubMed Central

Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

PubMed

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

Preliminary Empirical Models for Predicting Shrinkage, Part Geometry and Metallurgical Aspects of Ti-6Al-4V Shaped Metal Deposition Builds

NASA Astrophysics Data System (ADS)

Escobar-Palafox, Gustavo; Gault, Rosemary; Ridgway, Keith

2011-12-01

Shaped Metal Deposition (SMD) is an additive manufacturing process which creates parts layer by layer by weld depositions. In this work, empirical models that predict part geometry (wall thickness and outer diameter) and some metallurgical aspects (i.e. surface texture, portion of finer Widmanstätten microstructure) for the SMD process were developed. The models are based on an orthogonal fractional factorial design of experiments with four factors at two levels. The factors considered were energy level (a relationship between heat source power and the rate of raw material input.), step size, programmed diameter and travel speed. The models were validated using previous builds; the prediction error for part geometry was under 11%. Several relationships between the factors and responses were identified. Current had a significant effect on wall thickness; thickness increases with increasing current. Programmed diameter had a significant effect on percentage of shrinkage; this decreased with increasing component size. Surface finish decreased with decreasing step size and current.

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

On entropy, financial markets and minority games

NASA Astrophysics Data System (ADS)

Zapart, Christopher A.

2009-04-01

The paper builds upon an earlier statistical analysis of financial time series with Shannon information entropy, published in [L. Molgedey, W. Ebeling, Local order, entropy and predictability of financial time series, European Physical Journal B-Condensed Matter and Complex Systems 15/4 (2000) 733-737]. A novel generic procedure is proposed for making multistep-ahead predictions of time series by building a statistical model of entropy. The approach is first demonstrated on the chaotic Mackey-Glass time series and later applied to Japanese Yen/US dollar intraday currency data. The paper also reinterprets Minority Games [E. Moro, The minority game: An introductory guide, Advances in Condensed Matter and Statistical Physics (2004)] within the context of physical entropy, and uses models derived from minority game theory as a tool for measuring the entropy of a model in response to time series. This entropy conditional upon a model is subsequently used in place of information-theoretic entropy in the proposed multistep prediction algorithm.

Transforming RNA-Seq data to improve the performance of prognostic gene signatures.

PubMed

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

PubMed Central

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. PMID:24416353

BESTEST-EX | Buildings | NREL

Science.gov Websites

method for testing home energy audit software and associated calibration methods. BESTEST-EX is one of Energy Analysis Model Calibration Methods. When completed, the ANSI/RESNET SMOT will specify test procedures for evaluating calibration methods used in conjunction with predicting building energy use and

Optimizing Energy Consumption in Building Designs Using Building Information Model (BIM)

NASA Astrophysics Data System (ADS)

Egwunatum, Samuel; Joseph-Akwara, Esther; Akaigwe, Richard

2016-09-01

Given the ability of a Building Information Model (BIM) to serve as a multi-disciplinary data repository, this paper seeks to explore and exploit the sustainability value of Building Information Modelling/models in delivering buildings that require less energy for their operation, emit less CO2 and at the same time provide a comfortable living environment for their occupants. This objective was achieved by a critical and extensive review of the literature covering: (1) building energy consumption, (2) building energy performance and analysis, and (3) building information modeling and energy assessment. The literature cited in this paper showed that linking an energy analysis tool with a BIM model helped project design teams to predict and create optimized energy consumption. To validate this finding, an in-depth analysis was carried out on a completed BIM integrated construction project using the Arboleda Project in the Dominican Republic. The findings showed that the BIM-based energy analysis helped the design team achieve the world's first 103% positive energy building. From the research findings, the paper concludes that linking an energy analysis tool with a BIM model helps to expedite the energy analysis process, provide more detailed and accurate results as well as deliver energy-efficient buildings. The study further recommends that the adoption of a level 2 BIM and the integration of BIM in energy optimization analyse should be made compulsory for all projects irrespective of the method of procurement (government-funded or otherwise) or its size.

The OPALS Plan for Operations: Use of ISS Trajectory and Attitude Models in the OPALS Pointing Strategy

NASA Technical Reports Server (NTRS)

Abrahamson, Matthew J.; Oaida, Bogdan; Erkmen, Baris

2013-01-01

This paper will discuss the OPALS pointing strategy, focusing on incorporation of ISS trajectory and attitude models to build pointing predictions. Methods to extrapolate an ISS prediction based on past data will be discussed and will be compared to periodically published ISS predictions and Two-Line Element (TLE) predictions. The prediction performance will also be measured against GPS states available in telemetry. The performance of the pointing products will be compared to the allocated values in the OPALS pointing budget to assess compliance with requirements.

Flood management: prediction of microbial contamination in large-scale floods in urban environments.

PubMed

Taylor, Jonathon; Lai, Ka Man; Davies, Mike; Clifton, David; Ridley, Ian; Biddulph, Phillip

2011-07-01

With a changing climate and increased urbanisation, the occurrence and the impact of flooding is expected to increase significantly. Floods can bring pathogens into homes and cause lingering damp and microbial growth in buildings, with the level of growth and persistence dependent on the volume and chemical and biological content of the flood water, the properties of the contaminating microbes, and the surrounding environmental conditions, including the restoration time and methods, the heat and moisture transport properties of the envelope design, and the ability of the construction material to sustain the microbial growth. The public health risk will depend on the interaction of these complex processes and the vulnerability and susceptibility of occupants in the affected areas. After the 2007 floods in the UK, the Pitt review noted that there is lack of relevant scientific evidence and consistency with regard to the management and treatment of flooded homes, which not only put the local population at risk but also caused unnecessary delays in the restoration effort. Understanding the drying behaviour of flooded buildings in the UK building stock under different scenarios, and the ability of microbial contaminants to grow, persist, and produce toxins within these buildings can help inform recovery efforts. To contribute to future flood management, this paper proposes the use of building simulations and biological models to predict the risk of microbial contamination in typical UK buildings. We review the state of the art with regard to biological contamination following flooding, relevant building simulation, simulation-linked microbial modelling, and current practical considerations in flood remediation. Using the city of London as an example, a methodology is proposed that uses GIS as a platform to integrate drying models and microbial risk models with the local building stock and flood models. The integrated tool will help local governments, health authorities, insurance companies and residents to better understand, prepare for and manage a large-scale flood in urban environments. Copyright © 2011 Elsevier Ltd. All rights reserved.

PredicT-ML: a tool for automating machine learning model building with big clinical data.

PubMed

Luo, Gang

2016-01-01

Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

PubMed

Savolainen, Otto; Fagerberg, Björn; Vendelbo Lind, Mads; Sandberg, Ann-Sofie; Ross, Alastair B; Bergström, Göran

2017-01-01

The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711]). Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.

Biomarkers for predicting type 2 diabetes development—Can metabolomics improve on existing biomarkers?

PubMed Central

Savolainen, Otto; Fagerberg, Björn; Vendelbo Lind, Mads; Sandberg, Ann-Sofie; Ross, Alastair B.; Bergström, Göran

2017-01-01

Aim The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. Methods Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. Results Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738–0.850]) and 0.808 [0.749–0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577–0.736]). Prediction based on non-blood based measures was 0.638 [0.565–0.711]). Conclusions Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model. PMID:28692646

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

PubMed Central

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L.

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. PMID:26107615

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

PubMed

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Model calibration of a variable refrigerant flow system with a dedicated outdoor air system: A case study

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

Kim, Dongsu; Cox, Sam J.; Cho, Heejin

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial modelmore » is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. As a result, the whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period.« less

Building Protection Against External Ionizing Fallout Radiation

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

Dillon, Michael B.; Homann, Steven G.

A nuclear explosion has the potential to injure or kill tens to hundreds of thousands of people through exposure to fallout (external gamma) radiation. Existing buildings can protect their occupants (reducing external radiation exposures) by placing material and distance between fallout particles and indoor individuals. This protection is not well captured in current fallout risk assessment models and so the US Department of Defense is implementing the Regional Shelter Analysis methodology to improve the ability of the Hazard Prediction and Assessment Capability (HPAC) model to account for building protection. This report supports the HPAC improvement effort by identifying a setmore » of building attributes (next page) that, when collectively specified, are sufficient to calculate reasonably accurate, i.e., within a factor of 2, fallout shelter quality estimates for many individual buildings. The set of building attributes were determined by first identifying the key physics controlling building protection from fallout radiation and then assessing which building attributes are relevant to the identified physics. This approach was evaluated by developing a screening model (PFscreen) based on the identified physics and comparing the screening model results against the set of existing independent experimental, theoretical, and modeled building protection estimates. In the interests of transparency, we have developed a benchmark dataset containing (a) most of the relevant primary experimental data published by prior generations of fallout protection scientists as well as (b) the screening model results.« less

Learning to REDUCE: A Reduced Electricity Consumption Prediction Ensemble

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

Aman, Saima; Chelmis, Charalampos; Prasanna, Viktor

Utilities use Demand Response (DR) to balance supply and demand in the electric grid by involving customers in efforts to reduce electricity consumption during peak periods. To implement and adapt DR under dynamically changing conditions of the grid, reliable prediction of reduced consumption is critical. However, despite the wealth of research on electricity consumption prediction and DR being long in practice, the problem of reduced consumption prediction remains largely un-addressed. In this paper, we identify unique computational challenges associated with the prediction of reduced consumption and contrast this to that of normal consumption and DR baseline prediction.We propose a novelmore » ensemble model that leverages different sequences of daily electricity consumption on DR event days as well as contextual attributes for reduced consumption prediction. We demonstrate the success of our model on a large, real-world, high resolution dataset from a university microgrid comprising of over 950 DR events across a diverse set of 32 buildings. Our model achieves an average error of 13.5%, an 8.8% improvement over the baseline. Our work is particularly relevant for buildings where electricity consumption is not tied to strict schedules. Our results and insights should prove useful to the researchers and practitioners working in the sustainable energy domain.« less

Prediction Models for Dynamic Demand Response

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

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

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 (D 2R) 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 D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R 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 D 2R. 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 D 2R. 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 D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2R.« less

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

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

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

Validation and Use of a Predictive Modeling Tool: Employing Scientific Findings to Improve Responsible Conduct of Research Education.

PubMed

Mulhearn, Tyler J; Watts, Logan L; Todd, E Michelle; Medeiros, Kelsey E; Connelly, Shane; Mumford, Michael D

2017-01-01

Although recent evidence suggests ethics education can be effective, the nature of specific training programs, and their effectiveness, varies considerably. Building on a recent path modeling effort, the present study developed and validated a predictive modeling tool for responsible conduct of research education. The predictive modeling tool allows users to enter ratings in relation to a given ethics training program and receive instantaneous evaluative information for course refinement. Validation work suggests the tool's predicted outcomes correlate strongly (r = 0.46) with objective course outcomes. Implications for training program development and refinement are discussed.

Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

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

Jones, Christian Birk; Robinson, Matt; Yasaei, Yasser

Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often beenmore » perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.« less

Building predictive models for MERS-CoV infections using data mining techniques.

PubMed

Al-Turaiki, Isra; Alshahrani, Mona; Almutairi, Tahani

Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections. The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute. The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%. We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections. Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Building Models to Predict Hint-or-Attempt Actions of Students

ERIC Educational Resources Information Center

Castro, Francisco Enrique Vicente; Adjei, Seth; Colombo, Tyler; Heffernan, Neil

2015-01-01

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to…

Grand Junction Projects Office Remedial Action Project Building 2 public dose evaluation. Final report

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

Morris, R.

1996-05-01

Building 2 on the U.S. Department of Energy (DOE) Grand Junction Projects Office (GJPO) site, which is operated by Rust Geotech, is part of the GJPO Remedial Action Program. This report describes measurements and modeling efforts to evaluate the radiation dose to members of the public who might someday occupy or tear down Building 2. The assessment of future doses to those occupying or demolishing Building 2 is based on assumptions about future uses of the building, measured data when available, and predictive modeling when necessary. Future use of the building is likely to be as an office facility. Themore » DOE sponsored program, RESRAD-BUILD, Version. 1.5 was chosen for the modeling tool. Releasing the building for unrestricted use instead of demolishing it now could save a substantial amount of money compared with the baseline cost estimate because the site telecommunications system, housed in Building 2, would not be disabled and replaced. The information developed in this analysis may be used as part of an as low as reasonably achievable (ALARA) cost/benefit determination regarding disposition of Building 2.« less

Validating computational predictions of night-time ventilation in Stanford's Y2E2 building

NASA Astrophysics Data System (ADS)

Chen, Chen; Lamberti, Giacomo; Gorle, Catherine

2017-11-01

Natural ventilation can significantly reduce building energy consumption, but robust design is a challenging task. We previously presented predictions of natural ventilation performance in Stanford's Y2E2 building using two models with different levels of fidelity, embedded in an uncertainty quantification framework to identify the dominant uncertain parameters and predict quantified confidence intervals. The results showed a slightly high cooling rate for the volume-averaged temperature, and the initial thermal mass temperature and window discharge coefficients were found to have an important influence on the results. To further investigate the potential role of these parameters on the observed discrepancies, the current study is performing additional measurements in the Y2E2 building. Wall temperatures are recorded throughout the nightflush using thermocouples; flow rates through windows are measured using hotwires; and spatial variability in the air temperature is explored. The measured wall temperatures are found the be within the range of our model assumptions, and the measured velocities agree reasonably well with our CFD predications. Considerable local variations in the indoor air temperature have been recorded, largely explaining the discrepancies in our earlier validation study. Future work will therefore focus on a local validation of the CFD results with the measurements. Center for Integrated Facility Engineering (CIFE).

Protein structure modeling and refinement by global optimization in CASP12.

PubMed

Hong, Seung Hwan; Joung, InSuk; Flores-Canales, Jose C; Manavalan, Balachandran; Cheng, Qianyi; Heo, Seungryong; Kim, Jong Yun; Lee, Sun Young; Nam, Mikyung; Joo, Keehyoung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

2018-03-01

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded. © 2017 Wiley Periodicals, Inc.

Development and validation of a building design waste reduction model.

PubMed

Llatas, C; Osmani, M

2016-10-01

Reduction in construction waste is a pressing need in many countries. The design of building elements is considered a pivotal process to achieve waste reduction at source, which enables an informed prediction of their wastage reduction levels. However the lack of quantitative methods linking design strategies to waste reduction hinders designing out waste practice in building projects. Therefore, this paper addresses this knowledge gap through the design and validation of a Building Design Waste Reduction Strategies (Waste ReSt) model that aims to investigate the relationships between design variables and their impact on onsite waste reduction. The Waste ReSt model was validated in a real-world case study involving 20 residential buildings in Spain. The validation process comprises three stages. Firstly, design waste causes were analyzed. Secondly, design strategies were applied leading to several alternative low waste building elements. Finally, their potential source reduction levels were quantified and discussed within the context of the literature. The Waste ReSt model could serve as an instrumental tool to simulate designing out strategies in building projects. The knowledge provided by the model could help project stakeholders to better understand the correlation between the design process and waste sources and subsequently implement design practices for low-waste buildings. Copyright © 2016 Elsevier Ltd. All rights reserved.

FireStem2D — A two-dimensional heat transfer model for simulating tree stem injury in fires

Treesearch

Efthalia K. Chatziefstratiou; Gil Bohrer; Anthony S. Bova; Ravishankar Subramanian; Renato P.M. Frasson; Amy Scherzer; Bret W. Butler; Matthew B. Dickinson

2013-01-01

FireStem2D, a software tool for predicting tree stem heating and injury in forest fires, is a physically-based, two-dimensional model of stem thermodynamics that results from heating at the bark surface. It builds on an earlier one-dimensional model (FireStem) and provides improved capabilities for predicting fire-induced mortality and injury before a fire occurs by...

Application of an Integrated HPC Reliability Prediction Framework to HMMWV Suspension System

DTIC Science & Technology

2010-09-13

model number M966 (TOW Missle Carrier, Basic Armor without weapons), since they were available. Tires used for all simulations were the bias-type...vehicle fleet, including consideration of all kinds of uncertainty, especially including model uncertainty. The end result will be a tool to use...building an adequate vehicle reliability prediction framework for military vehicles is the accurate modeling of the integration of various types of

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

PubMed

Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Intelligent demand side management of residential building energy systems

NASA Astrophysics Data System (ADS)

Sinha, Maruti N.

Advent of modern sensing technologies, data processing capabilities and rising cost of energy are driving the implementation of intelligent systems in buildings and houses which constitute 41% of total energy consumption. The primary motivation has been to provide a framework for demand-side management and to improve overall reliability. The entire formulation is to be implemented on NILM (Non-Intrusive Load Monitoring System), a smart meter. This is going to play a vital role in the future of demand side management. Utilities have started deploying smart meters throughout the world which will essentially help to establish communication between utility and consumers. This research is focused on investigation of a suitable thermal model of residential house, building up control system and developing diagnostic and energy usage forecast tool. The present work has considered measurement based approach to pursue. Identification of building thermal parameters is the very first step towards developing performance measurement and controls. The proposed identification technique is PEM (Prediction Error Method) based, discrete state-space model. The two different models have been devised. First model is focused toward energy usage forecast and diagnostics. Here one of the novel idea has been investigated which takes integral of thermal capacity to identify thermal model of house. The purpose of second identification is to build up a model for control strategy. The controller should be able to take into account the weather forecast information, deal with the operating point constraints and at the same time minimize the energy consumption. To design an optimal controller, MPC (Model Predictive Control) scheme has been implemented instead of present thermostatic/hysteretic control. This is a receding horizon approach. Capability of the proposed schemes has also been investigated.

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

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

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

DOE PAGES

Wetter, Michael; Bonvini, Marco; Nouidui, Thierry S.

2016-04-01

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller thatmore » adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution« less

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

Jiang, Like; Kang, Jian, E-mail: j.kang@sheffield.ac.uk; Schroth, Olaf

Large scale transportation projects can adversely affect the visual perception of environmental quality and require adequate visual impact assessment. In this study, we investigated the effects of the characteristics of the road project and the character of the existing landscape on the perceived visual impact of motorways, and developed a GIS-based prediction model based on the findings. An online survey using computer-visualised scenes of different motorway and landscape scenarios was carried out to obtain perception-based judgements on the visual impact. Motorway scenarios simulated included the baseline scenario without road, original motorway, motorways with timber noise barriers, transparent noise barriers andmore » tree screen; different landscape scenarios were created by changing land cover of buildings and trees in three distance zones. The landscape content of each scene was measured in GIS. The result shows that presence of a motorway especially with the timber barrier significantly decreases the visual quality of the view. The resulted visual impact tends to be lower where it is less visually pleasant with more buildings in the view, and can be slightly reduced by the visual absorption effect of the scattered trees between the motorway and the viewpoint. Based on the survey result, eleven predictors were identified for the visual impact prediction model which was applied in GIS to generate maps of visual impact of motorways in different scenarios. The proposed prediction model can be used to achieve efficient and reliable assessment of visual impact of motorways. - Highlights: • Motorways induce significant visual impact especially with timber noise barriers. • Visual impact is negatively correlated with amount of buildings in the view. • Visual impact is positively correlated with percentage of trees in the view. • Perception-based motorway visual impact prediction model using mapped predictors • Predicted visual impacts in different scenarios are mapped in GIS.« less

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

PubMed

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

2014-07-01

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

Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species

PubMed Central

Copeland, Holly E.; Doherty, Kevin E.; Naugle, David E.; Pocewicz, Amy; Kiesecker, Joseph M.

2009-01-01

Background Many studies have quantified the indirect effect of hydrocarbon-based economies on climate change and biodiversity, concluding that a significant proportion of species will be threatened with extinction. However, few studies have measured the direct effect of new energy production infrastructure on species persistence. Methodology/Principal Findings We propose a systematic way to forecast patterns of future energy development and calculate impacts to species using spatially-explicit predictive modeling techniques to estimate oil and gas potential and create development build-out scenarios by seeding the landscape with oil and gas wells based on underlying potential. We illustrate our approach for the greater sage-grouse (Centrocercus urophasianus) in the western US and translate the build-out scenarios into estimated impacts on sage-grouse. We project that future oil and gas development will cause a 7–19 percent decline from 2007 sage-grouse lek population counts and impact 3.7 million ha of sagebrush shrublands and 1.1 million ha of grasslands in the study area. Conclusions/Significance Maps of where oil and gas development is anticipated in the US Intermountain West can be used by decision-makers intent on minimizing impacts to sage-grouse. This analysis also provides a general framework for using predictive models and build-out scenarios to anticipate impacts to species. These predictive models and build-out scenarios allow tradeoffs to be considered between species conservation and energy development prior to implementation. PMID:19826472

Variability in wood-frame building damage using broad-band synthetic ground motions: a comparative numerical study with recorded motions

USGS Publications Warehouse

Pei, Shiling; van de Lindt, John W.; Hartzell, Stephen; Luco, Nicolas

2014-01-01

Earthquake damage to light-frame wood buildings is a major concern for North America because of the volume of this construction type. In order to estimate wood building damage using synthetic ground motions, we need to verify the ability of synthetically generated ground motions to simulate realistic damage for this structure type. Through a calibrated damage potential indicator, four different synthetic ground motion models are compared with the historically recorded ground motions at corresponding sites. We conclude that damage for sites farther from the fault (>20 km) is under-predicted on average and damage at closer sites is sometimes over-predicted.

The Significance of Temperature Based Approach Over the Energy Based Approaches in the Buildings Thermal Assessment

NASA Astrophysics Data System (ADS)

Albatayneh, Aiman; Alterman, Dariusz; Page, Adrian; Moghtaderi, Behdad

2017-05-01

The design of low energy buildings requires accurate thermal simulation software to assess the heating and cooling loads. Such designs should sustain thermal comfort for occupants and promote less energy usage over the life time of any building. One of the house energy rating used in Australia is AccuRate, star rating tool to assess and compare the thermal performance of various buildings where the heating and cooling loads are calculated based on fixed operational temperatures between 20 °C to 25 °C to sustain thermal comfort for the occupants. However, these fixed settings for the time and temperatures considerably increase the heating and cooling loads. On the other hand the adaptive thermal model applies a broader range of weather conditions, interacts with the occupants and promotes low energy solutions to maintain thermal comfort. This can be achieved by natural ventilation (opening window/doors), suitable clothes, shading and low energy heating/cooling solutions for the occupied spaces (rooms). These activities will save significant amount of operating energy what can to be taken into account to predict energy consumption for a building. Most of the buildings thermal assessment tools depend on energy-based approaches to predict the thermal performance of any building e.g. AccuRate in Australia. This approach encourages the use of energy to maintain thermal comfort. This paper describes the advantages of a temperature-based approach to assess the building's thermal performance (using an adaptive thermal comfort model) over energy based approach (AccuRate Software used in Australia). The temperature-based approach was validated and compared with the energy-based approach using four full scale housing test modules located in Newcastle, Australia (Cavity Brick (CB), Insulated Cavity Brick (InsCB), Insulated Brick Veneer (InsBV) and Insulated Reverse Brick Veneer (InsRBV)) subjected to a range of seasonal conditions in a moderate climate. The time required for heating and/or cooling using the adaptive thermal comfort approach and AccuRate predictions were estimated. Significant savings (of about 50 %) in energy consumption in minimising the time required for heating and cooling were achieved by using the adaptive thermal comfort model.

Population activity statistics dissect subthreshold and spiking variability in V1.

PubMed

Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

2017-07-01

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity. Copyright © 2017 the American Physiological Society.

Some Unsolved Problems, Questions, and Applications of the Brightsen Nucleon Cluster Model

NASA Astrophysics Data System (ADS)

Smarandache, Florentin

2010-10-01

Brightsen Model is opposite to the Standard Model, and it was build on John Weeler's Resonating Group Structure Model and on Linus Pauling's Close-Packed Spheron Model. Among Brightsen Model's predictions and applications we cite the fact that it derives the average number of prompt neutrons per fission event, it provides a theoretical way for understanding the low temperature / low energy reactions and for approaching the artificially induced fission, it predicts that forces within nucleon clusters are stronger than forces between such clusters within isotopes; it predicts the unmatter entities inside nuclei that result from stable and neutral union of matter and antimatter, and so on. But these predictions have to be tested in the future at the new CERN laboratory.

Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review

NASA Astrophysics Data System (ADS)

Lateh, Masitah Abdul; Kamilah Muda, Azah; Yusof, Zeratul Izzah Mohd; Azilah Muda, Noor; Sanusi Azmi, Mohd

2017-09-01

The emerging era of big data for past few years has led to large and complex data which needed faster and better decision making. However, the small dataset problems still arise in a certain area which causes analysis and decision are hard to make. In order to build a prediction model, a large sample is required as a training sample of the model. Small dataset is insufficient to produce an accurate prediction model. This paper will review an artificial data generation approach as one of the solution to solve the small dataset problem.

Tsunami Simulators in Physical Modelling - Concept to Practical Solutions

NASA Astrophysics Data System (ADS)

Chandler, Ian; Allsop, William; Robinson, David; Rossetto, Tiziana; McGovern, David; Todd, David

2017-04-01

Whilst many researchers have conducted simple 'tsunami impact' studies, few engineering tools are available to assess the onshore impacts of tsunami, with no agreed methods available to predict loadings on coastal defences, buildings or related infrastructure. Most previous impact studies have relied upon unrealistic waveforms (solitary or dam-break waves and bores) rather than full-duration tsunami waves, or have used simplified models of nearshore and over-land flows. Over the last 10+ years, pneumatic Tsunami Simulators for the hydraulic laboratory have been developed into an exciting and versatile technology, allowing the forces of real-world tsunami to be reproduced and measured in a laboratory environment for the first time. These devices have been used to model generic elevated and N-wave tsunamis up to and over simple shorelines, and at example coastal defences and infrastructure. They have also reproduced full-duration tsunamis including Mercator 2004 and Tohoku 2011, both at 1:50 scale. Engineering scale models of these tsunamis have measured wave run-up on simple slopes, forces on idealised sea defences, pressures / forces on buildings, and scour at idealised buildings. This presentation will describe how these Tsunami Simulators work, demonstrate how they have generated tsunami waves longer than the facilities within which they operate, and will present research results from three generations of Tsunami Simulators. Highlights of direct importance to natural hazard modellers and coastal engineers include measurements of wave run-up levels, forces on single and multiple buildings and comparison with previous theoretical predictions. Multiple buildings have two malign effects. The density of buildings to flow area (blockage ratio) increases water depths and flow velocities in the 'streets'. But the increased building densities themselves also increase the cost of flow per unit area (both personal and monetary). The most recent study with the Tsunami Simulators therefore focussed on the influence of multiple buildings (up to 4 rows) which showed (for instance) that the greatest forces can act on the landward (not seaward) rows of buildings. Studies in the 70m long, 4m wide main channel of the Fast Flow Facility on tsunami defence structures have also measured forces on buildings in the lee of a failed defence wall and tsunami induced scour. Supporting presentations at this conference: McGovern et al on tsunami induced scour at coastal structures and Foster et al on building loads.

Parametrization of Drag and Turbulence for Urban Neighbourhoods with Trees

NASA Astrophysics Data System (ADS)

Krayenhoff, E. S.; Santiago, J.-L.; Martilli, A.; Christen, A.; Oke, T. R.

2015-08-01

Urban canopy parametrizations designed to be coupled with mesoscale models must predict the integrated effect of urban obstacles on the flow at each height in the canopy. To assess these neighbourhood-scale effects, results of microscale simulations may be horizontally-averaged. Obstacle-resolving computational fluid dynamics (CFD) simulations of neutrally-stratified flow through canopies of blocks (buildings) with varying distributions and densities of porous media (tree foliage) are conducted, and the spatially-averaged impacts on the flow of these building-tree combinations are assessed. The accuracy with which a one-dimensional (column) model with a one-equation (-) turbulence scheme represents spatially-averaged CFD results is evaluated. Individual physical mechanisms by which trees and buildings affect flow in the column model are evaluated in terms of relative importance. For the treed urban configurations considered, effects of buildings and trees may be considered independently. Building drag coefficients and length scale effects need not be altered due to the presence of tree foliage; therefore, parametrization of spatially-averaged flow through urban neighbourhoods with trees is greatly simplified. The new parametrization includes only source and sink terms significant for the prediction of spatially-averaged flow profiles: momentum drag due to buildings and trees (and the associated wake production of turbulent kinetic energy), modification of length scales by buildings, and enhanced dissipation of turbulent kinetic energy due to the small scale of tree foliage elements. Coefficients for the Santiago and Martilli (Boundary-Layer Meteorol 137: 417-439, 2010) parametrization of building drag coefficients and length scales are revised. Inclusion of foliage terms from the new parametrization in addition to the Santiago and Martilli building terms reduces root-mean-square difference (RMSD) of the column model streamwise velocity component and turbulent kinetic energy relative to the CFD model by 89 % in the canopy and 71 % above the canopy on average for the highest leaf area density scenarios tested: . RMSD values with the new parametrization are less than 20 % of mean layer magnitude for the streamwise velocity component within and above the canopy, and for above-canopy turbulent kinetic energy; RMSD values for within-canopy turbulent kinetic energy are negligible for most scenarios. The foliage-related portion of the new parametrization is required for scenarios with tree foliage of equal or greater height than the buildings, and for scenarios with foliage below roof height for building plan area densities less than approximately 0.25.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms.

PubMed

Chen, Hongming; Carlsson, Lars; Eriksson, Mats; Varkonyi, Peter; Norinder, Ulf; Nilsson, Ingemar

2013-06-24

A novel methodology was developed to build Free-Wilson like local QSAR models by combining R-group signatures and the SVM algorithm. Unlike Free-Wilson analysis this method is able to make predictions for compounds with R-groups not present in a training set. Eleven public data sets were chosen as test cases for comparing the performance of our new method with several other traditional modeling strategies, including Free-Wilson analysis. Our results show that the R-group signature SVM models achieve better prediction accuracy compared with Free-Wilson analysis in general. Moreover, the predictions of R-group signature models are also comparable to the models using ECFP6 fingerprints and signatures for the whole compound. Most importantly, R-group contributions to the SVM model can be obtained by calculating the gradient for R-group signatures. For most of the studied data sets, a significant correlation with that of a corresponding Free-Wilson analysis is shown. These results suggest that the R-group contribution can be used to interpret bioactivity data and highlight that the R-group signature based SVM modeling method is as interpretable as Free-Wilson analysis. Hence the signature SVM model can be a useful modeling tool for any drug discovery project.

Key Questions in Building Defect Prediction Models in Practice

NASA Astrophysics Data System (ADS)

Ramler, Rudolf; Wolfmaier, Klaus; Stauder, Erwin; Kossak, Felix; Natschläger, Thomas

The information about which modules of a future version of a software system are defect-prone is a valuable planning aid for quality managers and testers. Defect prediction promises to indicate these defect-prone modules. However, constructing effective defect prediction models in an industrial setting involves a number of key questions. In this paper we discuss ten key questions identified in context of establishing defect prediction in a large software development project. Seven consecutive versions of the software system have been used to construct and validate defect prediction models for system test planning. Furthermore, the paper presents initial empirical results from the studied project and, by this means, contributes answers to the identified questions.

Building a three-dimensional model of CYP2C9 inhibition using the Autocorrelator: an autonomous model generator.

PubMed

Lardy, Matthew A; Lebrun, Laurie; Bullard, Drew; Kissinger, Charles; Gobbi, Alberto

2012-05-25

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC₅₀) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.

APPLICATION OF A FULLY DISTRIBUTED WASHOFF AND TRANSPORT MODEL FOR A GULF COAST WATERSHED

EPA Science Inventory

Advances in hydrologic modeling have been shown to improve the accuracy of rainfall runoff simulation and prediction. Building on the capabilities of distributed hydrologic modeling, a water quality model was developed to simulate buildup, washoff, and advective transport of a co...

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

EPA Science Inventory

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

Probabilistic forecasting for extreme NO2 pollution episodes.

PubMed

Aznarte, José L

2017-10-01

In this study, we investigate the convenience of quantile regression to predict extreme concentrations of NO 2 . Contrarily to the usual point-forecasting, where a single value is forecast for each horizon, probabilistic forecasting through quantile regression allows for the prediction of the full probability distribution, which in turn allows to build models specifically fit for the tails of this distribution. Using data from the city of Madrid, including NO 2 concentrations as well as meteorological measures, we build models that predict extreme NO 2 concentrations, outperforming point-forecasting alternatives, and we prove that the predictions are accurate, reliable and sharp. Besides, we study the relative importance of the independent variables involved, and show how the important variables for the median quantile are different than those important for the upper quantiles. Furthermore, we present a method to compute the probability of exceedance of thresholds, which is a simple and comprehensible manner to present probabilistic forecasts maximizing their usefulness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

Inverse and Predictive Modeling

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

Syracuse, Ellen Marie

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 evenmore » greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.« less

Prediction using patient comparison vs. modeling: a case study for mortality prediction.

PubMed

Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

2016-08-01

Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups.

PubMed

Deurenberg, P; Deurenberg-Yap, M; Schouten, F J M

2002-03-01

To test the impact of body build factors on the validity of impedance-based body composition predictions across (ethnic) population groups and to study the suitability of segmental impedance measurements. Cross-sectional observational study. Ministry of Health and School of Physical Education, Nanyang Technological University, Singapore. A total of 291 female and male Chinese, Malays and Indian Singaporeans, aged 18-69, body mass index (BMI) 16.0-40.2 kg/ m2. Anthropometric parameters were measured in addition to impedance (100 kHz) of the total body, arms and legs. Impedance indexes were calculated as height2/impedance. Arm length (span) and leg length (sitting height), wrist and knee width were measured from which body build indices were calculated. Total body water (TBW) was measured using deuterium oxide dilution. Extra cellular water (ECW) was measured using bromide dilution. Body fat percentage was determined using a chemical four-compartment model. The bias of TBW predicted from total body impedance index (bias: measured minus predicted TBW) was different among the three ethnic groups, TBW being significantly underestimated in Indians compared to Chinese and Malays. This bias was found to be dependent on body water distribution (ECW/TBW) and parameters of body build, mainly relative (to height) arm length. After correcting for differences in body water distribution and body build parameters the differences in bias across the ethnic groups disappeared. The impedance index using total body impedance was better correlated with TBW than the impedance index of arm or leg impedance, even after corrections for body build parameters. The study shows that ethnic-specific bias of impedance-based prediction formulas for body composition is due mainly to differences in body build among the ethnic groups. This means that the use of 'general' prediction equations across different (ethnic) population groups without prior testing of their validity should be avoided. Total body impedance has higher predictive value than segmental impedance.

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

A Voltammetric Electronic Tongue for the Resolution of Ternary Nitrophenol Mixtures

PubMed Central

González-Calabuig, Andreu; Cetó, Xavier

2018-01-01

This work reports the applicability of a voltammetric sensor array able to quantify the content of 2,4-dinitrophenol, 4-nitrophenol, and picric acid in artificial samples using the electronic tongue (ET) principles. The ET is based on cyclic voltammetry signals, obtained from an array of metal disk electrodes and a graphite epoxy composite electrode, compressed using discrete wavelet transform with chemometric tools such as artificial neural networks (ANNs). ANNs were employed to build the quantitative prediction model. In this manner, a set of standards based on a full factorial design, ranging from 0 to 300 mg·L−1, was prepared to build the model; afterward, the model was validated with a completely independent set of standards. The model successfully predicted the concentration of the three considered phenols with a normalized root mean square error of 0.030 and 0.076 for the training and test subsets, respectively, and r ≥ 0.948. PMID:29342848

Active Player Modeling in the Iterated Prisoner's Dilemma

PubMed Central

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions. PMID:26989405

Active Player Modeling in the Iterated Prisoner's Dilemma.

PubMed

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions.

Empirical Evaluation of Hunk Metrics as Bug Predictors

NASA Astrophysics Data System (ADS)

Ferzund, Javed; Ahsan, Syed Nadeem; Wotawa, Franz

Reducing the number of bugs is a crucial issue during software development and maintenance. Software process and product metrics are good indicators of software complexity. These metrics have been used to build bug predictor models to help developers maintain the quality of software. In this paper we empirically evaluate the use of hunk metrics as predictor of bugs. We present a technique for bug prediction that works at smallest units of code change called hunks. We build bug prediction models using random forests, which is an efficient machine learning classifier. Hunk metrics are used to train the classifier and each hunk metric is evaluated for its bug prediction capabilities. Our classifier can classify individual hunks as buggy or bug-free with 86 % accuracy, 83 % buggy hunk precision and 77% buggy hunk recall. We find that history based and change level hunk metrics are better predictors of bugs than code level hunk metrics.

Statistical considerations on prognostic models for glioma

PubMed Central

Molinaro, Annette M.; Wrensch, Margaret R.; Jenkins, Robert B.; Eckel-Passow, Jeanette E.

2016-01-01

Given the lack of beneficial treatments in glioma, there is a need for prognostic models for therapeutic decision making and life planning. Recently several studies defining subtypes of glioma have been published. Here, we review the statistical considerations of how to build and validate prognostic models, explain the models presented in the current glioma literature, and discuss advantages and disadvantages of each model. The 3 statistical considerations to establishing clinically useful prognostic models are: study design, model building, and validation. Careful study design helps to ensure that the model is unbiased and generalizable to the population of interest. During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate prediction performance via internal validation. Via external validation, an independent dataset can assess how well the model performs. It is imperative that published models properly detail the study design and methods for both model building and validation. This provides readers the information necessary to assess the bias in a study, compare other published models, and determine the model's clinical usefulness. As editors, reviewers, and readers of the relevant literature, we should be cognizant of the needed statistical considerations and insist on their use. PMID:26657835

Building energy modeling for green architecture and intelligent dashboard applications

NASA Astrophysics Data System (ADS)

DeBlois, Justin

Buildings are responsible for 40% of the carbon emissions in the United States. Energy efficiency in this sector is key to reducing overall greenhouse gas emissions. This work studied the passive technique called the roof solar chimney for reducing the cooling load in homes architecturally. Three models of the chimney were created: a zonal building energy model, computational fluid dynamics model, and numerical analytic model. The study estimated the error introduced to the building energy model (BEM) through key assumptions, and then used a sensitivity analysis to examine the impact on the model outputs. The conclusion was that the error in the building energy model is small enough to use it for building simulation reliably. Further studies simulated the roof solar chimney in a whole building, integrated into one side of the roof. Comparisons were made between high and low efficiency constructions, and three ventilation strategies. The results showed that in four US climates, the roof solar chimney results in significant cooling load energy savings of up to 90%. After developing this new method for the small scale representation of a passive architecture technique in BEM, the study expanded the scope to address a fundamental issue in modeling - the implementation of the uncertainty from and improvement of occupant behavior. This is believed to be one of the weakest links in both accurate modeling and proper, energy efficient building operation. A calibrated model of the Mascaro Center for Sustainable Innovation's LEED Gold, 3,400 m2 building was created. Then algorithms were developed for integration to the building's dashboard application that show the occupant the energy savings for a variety of behaviors in real time. An approach using neural networks to act on real-time building automation system data was found to be the most accurate and efficient way to predict the current energy savings for each scenario. A stochastic study examined the impact of the representation of unpredictable occupancy patterns on model results. Combined, these studies inform modelers and researchers on frameworks for simulating holistically designed architecture and improving the interaction between models and building occupants, in residential and commercial settings. v

Datamining approaches for modeling tumor control probability.

PubMed

Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

2010-11-01

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Development of a 3D finite element acoustic model to predict the sound reduction index of stud based double-leaf walls

NASA Astrophysics Data System (ADS)

Arjunan, A.; Wang, C. J.; Yahiaoui, K.; Mynors, D. J.; Morgan, T.; Nguyen, V. B.; English, M.

2014-11-01

Building standards incorporating quantitative acoustical criteria to ensure adequate sound insulation are now being implemented. Engineers are making great efforts to design acoustically efficient double-wall structures. Accordingly, efficient simulation models to predict the acoustic insulation of double-leaf wall structures are needed. This paper presents the development of a numerical tool that can predict the frequency dependent sound reduction index R of stud based double-leaf walls at one-third-octave band frequency range. A fully vibro-acoustic 3D model consisting of two rooms partitioned using a double-leaf wall, considering the structure and acoustic fluid coupling incorporating the existing fluid and structural solvers are presented. The validity of the finite element (FE) model is assessed by comparison with experimental test results carried out in a certified laboratory. Accurate representation of the structural damping matrix to effectively predict the R values are studied. The possibilities of minimising the simulation time using a frequency dependent mesh model was also investigated. The FEA model presented in this work is capable of predicting the weighted sound reduction index Rw along with A-weighted pink noise C and A-weighted urban noise Ctr within an error of 1 dB. The model developed can also be used to analyse the acoustically induced frequency dependent geometrical behaviour of the double-leaf wall components to optimise them for best acoustic performance. The FE modelling procedure reported in this paper can be extended to other building components undergoing fluid-structure interaction (FSI) to evaluate their acoustic insulation.

Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

PubMed

Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

2017-11-01

It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

Representing, Running, and Revising Mental Models: A Computational Model

ERIC Educational Resources Information Center

Friedman, Scott; Forbus, Kenneth; Sherin, Bruce

2018-01-01

People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will…

Unstructured meshing and parameter estimation for urban dam-break flood modeling: building treatments and implications for accuracy and efficiency

NASA Astrophysics Data System (ADS)

Schubert, J. E.; Sanders, B. F.

2011-12-01

Urban landscapes are at the forefront of current research efforts in the field of flood inundation modeling for two major reasons. First, urban areas hold relatively large economic and social importance and as such it is imperative to avoid or minimize future damages. Secondly, urban flooding is becoming more frequent as a consequence of continued development of impervious surfaces, population growth in cities, climate change magnifying rainfall intensity, sea level rise threatening coastal communities, and decaying flood defense infrastructure. In reality urban landscapes are particularly challenging to model because they include a multitude of geometrically complex features. Advances in remote sensing technologies and geographical information systems (GIS) have promulgated fine resolution data layers that offer a site characterization suitable for urban inundation modeling including a description of preferential flow paths, drainage networks and surface dependent resistances to overland flow. Recent research has focused on two-dimensional modeling of overland flow including within-curb flows and over-curb flows across developed parcels. Studies have focused on mesh design and parameterization, and sub-grid models that promise improved performance relative to accuracy and/or computational efficiency. This presentation addresses how fine-resolution data, available in Los Angeles County, are used to parameterize, initialize and execute flood inundation models for the 1963 Baldwin Hills dam break. Several commonly used model parameterization strategies including building-resistance, building-block and building hole are compared with a novel sub-grid strategy based on building-porosity. Performance of the models is assessed based on the accuracy of depth and velocity predictions, execution time, and the time and expertise required for model set-up. The objective of this study is to assess field-scale applicability, and to obtain a better understanding of advantages and drawbacks of each method, and to recommend best practices for future studies. The Baldwin Hills dam-break flood is interesting for a couple of reasons. First, the flood caused high velocity, rapidly varied flow through a residential neighborhood and extensive damage to dozens residential structures. These conditions pose a challenge for many numerical models, the test is a rigorous one. Second, previous research has shown that flood extent predictions are sensitive to topographic data and stream flow predictions are sensitive to resistance parameters. Given that the representation of buildings affects the modeling of topography and resistance, a sensitivity to the representation of buildings is expected. Lastly, the site is supported by excellent geospatial data including validation datasets, and is made available through the Los Angeles County Imagery Acquisition Consortium (LAR-IAC), a joint effort of many public agencies in Los Angeles County to provide county-wide data. Hence, a broader aim of this study is to characterize the most useful aspects of the LAR-IAC data from a flood mapping perspective.

Integrating Biological and Chemical Data for Hepatotoxicity Prediction (SOT)

EPA Science Inventory

The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. A set of 677 chemicals were represented by 711 bioactivity descriptors (from ToxCast assays),...

MollDE: a homology modeling framework you can click with.

PubMed

Canutescu, Adrian A; Dunbrack, Roland L

2005-06-15

Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. http://dunbrack.fccc.edu/molide/molide.php rl_dunbrack@fccc.edu.

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

PubMed Central

Lucas, Joseph E.; Bazemore, Taylor C.; Alo, Celan; Monahan, Patrick B.

2017-01-01

HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits. PMID:29155848

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

Chen, Yixing; Zhang, Jianshun; Pelken, Michael

Executive Summary The objective of this study was to develop a “Virtual Design Studio (VDS)”: a software platform for integrated, coordinated and optimized design of green building systems with low energy consumption, high indoor environmental quality (IEQ), and high level of sustainability. This VDS is intended to assist collaborating architects, engineers and project management team members throughout from the early phases to the detailed building design stages. It can be used to plan design tasks and workflow, and evaluate the potential impacts of various green building strategies on the building performance by using the state of the art simulation toolsmore » as well as industrial/professional standards and guidelines for green building system design. Engaged in the development of VDS was a multi-disciplinary research team that included architects, engineers, and software developers. Based on the review and analysis of how existing professional practices in building systems design operate, particularly those used in the U.S., Germany and UK, a generic process for performance-based building design, construction and operation was proposed. It distinguishes the whole process into five distinct stages: Assess, Define, Design, Apply, and Monitoring (ADDAM). The current VDS is focused on the first three stages. The VDS considers building design as a multi-dimensional process, involving multiple design teams, design factors, and design stages. The intersection among these three dimensions defines a specific design task in terms of “who”, “what” and “when”. It also considers building design as a multi-objective process that aims to enhance the five aspects of performance for green building systems: site sustainability, materials and resource efficiency, water utilization efficiency, energy efficiency and impacts to the atmospheric environment, and IEQ. The current VDS development has been limited to energy efficiency and IEQ performance, with particular focus on evaluating thermal performance, air quality and lighting environmental quality because of their strong interaction with the energy performance of buildings. The VDS software framework contains four major functions: 1) Design coordination: It enables users to define tasks using the Input-Process-Output flow approach, which specifies the anticipated activities (i.e., the process), required input and output information, and anticipated interactions with other tasks. It also allows task scheduling to define the work flow, and sharing of the design data and information via the internet. 2) Modeling and simulation: It enables users to perform building simulations to predict the energy consumption and IEQ conditions at any of the design stages by using EnergyPlus and a combined heat, air, moisture and pollutant simulation (CHAMPS) model. A method for co-simulation was developed to allow the use of both models at the same time step for the combined energy and indoor air quality analysis. 3) Results visualization: It enables users to display a 3-D geometric design of the building by reading BIM (building information model) file generated by design software such as SketchUp, and the predicted results of heat, air, moisture, pollutant and light distributions in the building. 4) Performance evaluation: It enables the users to compare the performance of a proposed building design against a reference building that is defined for the same type of buildings under the same climate condition, and predicts the percent of improvements over the minimum requirements specified in ASHRAE Standard 55-2010, 62.1-2010 and 90.1-2010. An approach was developed to estimate the potential impact of a design factor on the whole building performance, and hence can assist the user to identify areas that have most pay back for investment. The VDS software was developed by using C++ with the conventional Model, View and Control (MVC) software architecture. The software has been verified by using a simple 3-zone case building. The application of the VDS concepts and framework for building design and performance analysis has been illustrated by using a medium-sized, five story office building that received LEED Platinum Certification from USGBC.« less

Analysis of ecstasy tablets: comparison of reflectance and transmittance near infrared spectroscopy.

PubMed

Schneider, Ralph Carsten; Kovar, Karl-Artur

2003-07-08

Calibration models for the quantitation of commonly used ecstasy substances have been developed using near infrared spectroscopy (NIR) in diffuse reflectance and in transmission mode by applying seized ecstasy tablets for model building and validation. The samples contained amphetamine, N-methyl-3,4-methylenedioxy-amphetamine (MDMA) and N-ethyl-3,4-methylenedioxy-amphetamine (MDE) in different concentrations. All tablets were analyzed using high performance liquid chromatography (HPLC) with diode array detection as reference method. We evaluated the performance of each NIR measurement method with regard to its ability to predict the content of each tablet with a low root mean square error of prediction (RMSEP). Best calibration models could be generated by using NIR measurement in transmittance mode with wavelength selection and 1/x-transformation of the raw data. The models build in reflectance mode showed higher RMSEPs using as data pretreatment, wavelength selection, 1/x-transformation and a second order Savitzky-Golay derivative with five point smoothing was applied to obtain the best models. To estimate the influence of inhomogeneities in the illegal tablets, a calibration of the destroyed, i.e. triturated samples was build and compared to the corresponding data of the whole tablets. The calibrations using these homogenized tablets showed lower RMSEPs. We can conclude that NIR analysis of ecstasy tablets in transmission mode is more suitable than measurement in diffuse reflectance to obtain quantification models for their active ingredients with regard to low errors of prediction. Inhomogeneities in the samples are equalized when measuring the tablets as powdered samples.

Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 1. Evaluation and Adaptation of GastroPlus To Predict Bioavailability of Medchem Series.

PubMed

Daga, Pankaj R; Bolger, Michael B; Haworth, Ian S; Clark, Robert D; Martin, Eric J

2018-03-05

When medicinal chemists need to improve bioavailability (%F) within a chemical series during lead optimization, they synthesize new series members with systematically modified properties mainly by following experience and general rules of thumb. More quantitative models that predict %F of proposed compounds from chemical structure alone have proven elusive. Global empirical %F quantitative structure-property (QSPR) models perform poorly, and projects have too little data to train local %F QSPR models. Mechanistic oral absorption and physiologically based pharmacokinetic (PBPK) models simulate the dissolution, absorption, systemic distribution, and clearance of a drug in preclinical species and humans. Attempts to build global PBPK models based purely on calculated inputs have not achieved the <2-fold average error needed to guide lead optimization. In this work, local GastroPlus PBPK models are instead customized for individual medchem series. The key innovation was building a local QSPR for a numerically fitted effective intrinsic clearance (CL loc ). All inputs are subsequently computed from structure alone, so the models can be applied in advance of synthesis. Training CL loc on the first 15-18 rat %F measurements gave adequate predictions, with clear improvements up to about 30 measurements, and incremental improvements beyond that.

Chlorine truck attack consequences and mitigation.

PubMed

Barrett, Anthony Michael; Adams, Peter J

2011-08-01

We develop and apply an integrated modeling system to estimate fatalities from intentional release of 17 tons of chlorine from a tank truck in a generic urban area. A public response model specifies locations and actions of the populace. A chemical source term model predicts initial characteristics of the chlorine vapor and aerosol cloud. An atmospheric dispersion model predicts cloud spreading and movement. A building air exchange model simulates movement of chlorine from outdoors into buildings at each location. A dose-response model translates chlorine exposures into predicted fatalities. Important parameters outside defender control include wind speed, atmospheric stability class, amount of chlorine released, and dose-response model parameters. Without fast and effective defense response, with 2.5 m/sec wind and stability class F, we estimate approximately 4,000 (half within ∼10 minutes) to 30,000 fatalities (half within ∼20 minutes), depending on dose-response model. Although we assume 7% of the population was outdoors, they represent 60-90% of fatalities. Changing weather conditions result in approximately 50-90% lower total fatalities. Measures such as sheltering in place, evacuation, and use of security barriers and cryogenic storage can reduce fatalities, sometimes by 50% or more, depending on response speed and other factors. © 2011 Society for Risk Analysis.

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

Development of a Low-Lift Chiller Controller and Simplified Precooling Control Algorithm - Final Report

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

Gayeski, N.; Armstrong, Peter; Alvira, M.

2011-11-30

KGS Buildings LLC (KGS) and Pacific Northwest National Laboratory (PNNL) have developed a simplified control algorithm and prototype low-lift chiller controller suitable for model-predictive control in a demonstration project of low-lift cooling. Low-lift cooling is a highly efficient cooling strategy conceived to enable low or net-zero energy buildings. A low-lift cooling system consists of a high efficiency low-lift chiller, radiant cooling, thermal storage, and model-predictive control to pre-cool thermal storage overnight on an optimal cooling rate trajectory. We call the properly integrated and controlled combination of these elements a low-lift cooling system (LLCS). This document is the final report formore » that project.« less

Predicting the seismic performance of typical R/C healthcare facilities: emphasis on hospitals

NASA Astrophysics Data System (ADS)

Bilgin, Huseyin; Frangu, Idlir

2017-09-01

Reinforced concrete (RC) type of buildings constitutes an important part of the current building stock in earthquake prone countries such as Albania. Seismic response of structures during a severe earthquake plays a vital role in the extent of structural damage and resulting injuries and losses. In this context, this study evaluates the expected performance of a five-story RC healthcare facility, representative of common practice in Albania, designed according to older codes. The design was based on the code requirements used in this region during the mid-1980s. Non-linear static and dynamic time history analyses were conducted on the structural model using the Zeus NL computer program. The dynamic time history analysis was conducted with a set of ground motions from real earthquakes. The building responses were estimated in global levels. FEMA 356 criteria were used to predict the seismic performance of the building. The structural response measures such as capacity curve and inter-story drift under the set of ground motions and pushover analyses results were compared and detailed seismic performance assessment was done. The main aim of this study is considering the application and methodology for the earthquake performance assessment of existing buildings. The seismic performance of the structural model varied significantly under different ground motions. Results indicate that case study building exhibit inadequate seismic performance under different seismic excitations. In addition, reasons for the poor performance of the building is discussed.

Semiautomated model building for RNA crystallography using a directed rotameric approach.

PubMed

Keating, Kevin S; Pyle, Anna Marie

2010-05-04

Structured RNA molecules play essential roles in a variety of cellular processes; however, crystallographic studies of such RNA molecules present a large number of challenges. One notable complication arises from the low resolutions typical of RNA crystallography, which results in electron density maps that are imprecise and difficult to interpret. This problem is exacerbated by the lack of computational tools for RNA modeling, as many of the techniques commonly used in protein crystallography have no equivalents for RNA structure. This leads to difficulty and errors in the model building process, particularly in modeling of the RNA backbone, which is highly error prone due to the large number of variable torsion angles per nucleotide. To address this, we have developed a method for accurately building the RNA backbone into maps of intermediate or low resolution. This method is semiautomated, as it requires a crystallographer to first locate phosphates and bases in the electron density map. After this initial trace of the molecule, however, an accurate backbone structure can be built without further user intervention. To accomplish this, backbone conformers are first predicted using RNA pseudotorsions and the base-phosphate perpendicular distance. Detailed backbone coordinates are then calculated to conform both to the predicted conformer and to the previously located phosphates and bases. This technique is shown to produce accurate backbone structure even when starting from imprecise phosphate and base coordinates. A program implementing this methodology is currently available, and a plugin for the Coot model building program is under development.

Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

ERIC Educational Resources Information Center

Lee, Young-Jin

2015-01-01

This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

Static SPME sampling of VOCs emitted from indoor building materials: prediction of calibration curves of single compounds for two different emission cells.

PubMed

Mocho, Pierre; Desauziers, Valérie

2011-05-01

Solid-phase microextraction (SPME) is a powerful technique, easy to implement for on-site static sampling of indoor VOCs emitted by building materials. However, a major constraint lies in the establishment of calibration curves which requires complex generation of standard atmospheres. Thus, the purpose of this paper is to propose a model to predict adsorption kinetics (i.e., calibration curves) of four model VOCs. The model is based on Fick's laws for the gas phase and on the equilibrium or the solid diffusion model for the adsorptive phase. Two samplers (the FLEC® and a home-made cylindrical emission cell), coupled to SPME for static sampling of material emissions, were studied. A good agreement between modeling and experimental data is observed and results show the influence of sampling rate on mass transfer mode in function of sample volume. The equilibrium model is adapted to quite large volume sampler (cylindrical cell) while the solid diffusion model is dedicated to small volume sampler (FLEC®). The limiting steps of mass transfer are the diffusion in gas phase for the cylindrical cell and the pore surface diffusion for the FLEC®. In the future, this modeling approach could be a useful tool for time-saving development of SPME to study building material emission in static mode sampling.

Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.

PubMed

Sariyar, Murat; Hoffmann, Isabell; Binder, Harald

2014-02-26

Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are established techniques that combine parameter estimation and variable selection. One big challenge is to incorporate interactions into such prediction models. In this feasibility study, we present building blocks for evaluating and incorporating interactions terms in high-dimensional time-to-event settings, especially for settings in which it is computationally too expensive to check all possible interactions. We use a boosting technique for estimation of effects and the following building blocks for pre-selecting interactions: (1) resampling, (2) random forests and (3) orthogonalization as a data pre-processing step. In a simulation study, the strategy that uses all building blocks is able to detect true main effects and interactions with high sensitivity in different kinds of scenarios. The main challenge are interactions composed of variables that do not represent main effects, but our findings are also promising in this regard. Results on real world data illustrate that effect sizes of interactions frequently may not be large enough to improve prediction performance, even though the interactions are potentially of biological relevance. Screening interactions through random forests is feasible and useful, when one is interested in finding relevant two-way interactions. The other building blocks also contribute considerably to an enhanced pre-selection of interactions. We determined the limits of interaction detection in terms of necessary effect sizes. Our study emphasizes the importance of making full use of existing methods in addition to establishing new ones.

Automated Measurement and Verification and Innovative Occupancy Detection Technologies

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

Price, Phillip; Bruce, Nordman; Piette, Mary Ann

In support of DOE’s sensors and controls research, the goal of this project is to move toward integrated building to grid systems by building on previous work to develop and demonstrate a set of load characterization measurement and evaluation tools that are envisioned to be part of a suite of applications for transactive efficient buildings, built upon data-driven load characterization and prediction models. This will include the ability to include occupancy data in the models, plus data collection and archival methods to include different types of occupancy data with existing networks and a taxonomy for naming these data within amore » Volttron agent platform.« less

Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems

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

Najafi, Massieh; Auslander, David M.; Bartlett, Peter L.

2010-05-30

Many studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However, in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models aremore » imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.« less

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

NASA Astrophysics Data System (ADS)

Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

A Bayesian network based framework for real-time crash prediction on the basic freeway segments of urban expressways.

PubMed

Hossain, Moinul; Muromachi, Yasunori

2012-03-01

The concept of measuring the crash risk for a very short time window in near future is gaining more practicality due to the recent advancements in the fields of information systems and traffic sensor technology. Although some real-time crash prediction models have already been proposed, they are still primitive in nature and require substantial improvements to be implemented in real-life. This manuscript investigates the major shortcomings of the existing models and offers solutions to overcome them with an improved framework and modeling method. It employs random multinomial logit model to identify the most important predictors as well as the most suitable detector locations to acquire data to build such a model. Afterwards, it applies Bayesian belief net (BBN) to build the real-time crash prediction model. The model has been constructed using high resolution detector data collected from Shibuya 3 and Shinjuku 4 expressways under the jurisdiction of Tokyo Metropolitan Expressway Company Limited, Japan. It has been specifically built for the basic freeway segments and it predicts the chance of formation of a hazardous traffic condition within the next 4-9 min for a particular 250 meter long road section. The performance evaluation results reflect that at an average threshold value the model is able to successful classify 66% of the future crashes with a false alarm rate less than 20%. Copyright © 2011 Elsevier Ltd. All rights reserved.

Replacement of SSE (Release 6) with NASA's Prediction of Worldwide Energy Resource (POWER) Project GIS-enabled Web Data Portal:

Atmospheric Science Data Center

2018-03-15

... effort has been developed under the Prediction Of Worldwide Energy Resource (POWER) Project funded largely by NASA Earth Applied Sciences ... to NASA's satellite and modeling analysis for Renewable Energy, Sustainable Buildings and Agroclimatology applications.  A new POWER ...

EPA Project Updates: DSSTox and ToxCast Generating New Data and Data Linkages for Use in Predictive Modeling

EPA Science Inventory

EPAs National Center for Computational Toxicology is building capabilities to support a new paradigm for toxicity screening and prediction. The DSSTox project is improving public access to quality structure-annotated chemical toxicity information in less summarized forms than tr...

Predictive Signatures from ToxCast Data for Chronic, Developmental and Reproductive Toxicity Endpoints

EPA Science Inventory

The EPA ToxCast program is using in vitro assay data and chemical descriptors to build predictive models for in vivo toxicity endpoints. In vitro assays measure activity of chemicals against molecular targets such as enzymes and receptors (measured in cell-free and cell-based sys...

Construction of technological artifacts and teaching strategies to promote flexible scientific understanding

NASA Astrophysics Data System (ADS)

Spitulnik, Michele Wisnudel

Science education reforms advocate inquiry as a way to build explanations and make informed decisions. Based on this call this dissertation (1) defines flexible scientific understanding by elaborating on content, inquiry and epistemic understandings; (2) describes an inquiry based unit that integrates dynamic modeling software; (3) examines students' understandings as they construct models; and (4) identifies instructional strategies that support inquiry and model building. A curriculum unit was designed to engage students in inquiry by identifying problems and constructing models to represent, explain and predict phenomena. Ninth grade students in a public mid-western high school worked in teams of 2-3 to ask questions, find information and reflect on the purposes of models. Data sources including classroom video, observations, interviews, student models and handouts were used to formulate cases that examine how two groups construct understanding. A teacher case study identifies the teaching strategies that support understanding. Categories within content, inquiry and epistemic understandings were used to analyze student understandings and teaching supports. The findings demonstrate that students can build flexible understanding by constructing models. Students built: (1) content understanding by identifying key ideas and building relationships and explanations of phenomena; (2) inquiry understanding by defining problems, constructing models and developing positions; and (3) epistemic understanding by describing the purposes of models as generalizing phenomena, testing hypotheses and making predictions. However, students demonstrated difficulty in using evidence to defend scientific arguments. Strategies that support flexible understanding were also identified. Content supports include: setting expectations for explanations; using examples to illustrate explanations; modeling questions; and providing feedback that prompts detailed explanations. Supports for inquiry are setting expectations for data gathering; using examples that illustrate model building; modeling the development of an argument; and providing feedback to promote coherent models. Epistemic supports include: using examples to illustrate purposes and assumptions within models, and providing feedback as students evaluate their models. The dissertation demonstrates that teaching strategies impact student understanding but are challenging to implement. When strategies are not used, students do not necessarily construct desired outcomes such as, using evidence to support arguments.

Aquifer Thermal Energy Storage for Seasonal Thermal Energy Balance

NASA Astrophysics Data System (ADS)

Rostampour, Vahab; Bloemendal, Martin; Keviczky, Tamas

2017-04-01

Aquifer Thermal Energy Storage (ATES) systems allow storing large quantities of thermal energy in subsurface aquifers enabling significant energy savings and greenhouse gas reductions. This is achieved by injection and extraction of water into and from saturated underground aquifers, simultaneously. An ATES system consists of two wells and operates in a seasonal mode. One well is used for the storage of cold water, the other one for the storage of heat. In warm seasons, cold water is extracted from the cold well to provide cooling to a building. The temperature of the extracted cold water increases as it passes through the building climate control systems and then gets simultaneously, injected back into the warm well. This procedure is reversed during cold seasons where the flow direction is reversed such that the warmer water is extracted from the warm well to provide heating to a building. From the perspective of building climate comfort systems, an ATES system is considered as a seasonal storage system that can be a heat source or sink, or as a storage for thermal energy. This leads to an interesting and challenging optimal control problem of the building climate comfort system that can be used to develop a seasonal-based energy management strategy. In [1] we develop a control-oriented model to predict thermal energy balance in a building climate control system integrated with ATES. Such a model however cannot cope with off-nominal but realistic situations such as when the wells are completely depleted, or the start-up phase of newly installed wells, etc., leading to direct usage of aquifer ambient temperature. Building upon our previous work in [1], we here extend the mathematical model for ATES system to handle the above mentioned more realistic situations. Using our improved models, one can more precisely predict system behavior and apply optimal control strategies to manage the building climate comfort along with energy savings and greenhouse gas reductions. [1] V. Rostampour and T. Keviczky, "Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems," arXiv [math.OC], 10-Nov-2016.

Novel transformation-based response prediction of shear building using interval neural network

NASA Astrophysics Data System (ADS)

Chakraverty, S.; Sahoo, Deepti Moyi

2017-04-01

Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [ -1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data are converted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.

End-use energy consumption estimates for US commercial buildings, 1989

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

Belzer, D.B.; Wrench, L.E.; Marsh, T.L.

An accurate picture of how energy is used in the nation`s stock of commercial buildings can serve a variety of program planning and policy needs within the Department of Energy, by utilities, and other groups seeking to improve the efficiency of energy use in the building sector. This report describes an estimation of energy consumption by end use based upon data from the 1989 Commercial Building Energy Consumption Survey (CBECS). The methodology used in the study combines elements of engineering simulations and statistical analysis to estimate end-use intensities for heating, cooling, ventilation, lighting, refrigeration, hot water, cooking, and miscellaneous equipment.more » Billing data for electricity and natural gas were first decomposed into weather and nonweather dependent loads. Subsequently, Statistical Adjusted Engineering (SAE) models were estimated by building type with annual data. The SAE models used variables such as building size, vintage, climate region, weekly operating hours, and employee density to adjust the engineering model predicted loads to the observed consumption. End-use consumption by fuel was estimated for each of the 5,876 buildings in the 1989 CBECS. The report displays the summary results for eleven separate building types as well as for the total US commercial building stock.« less

Analysis of the economic impact of the national unified carbon trading market mechanism Hebei province, for example

NASA Astrophysics Data System (ADS)

Sun, Yuxing

2018-05-01

In this paper, a grey prediction model is used to predict the carbon emission in Hebei province, and the impact analysis model based on TermCo2 is established. At the same time, we read a lot about CGE and study on how to build the scene, the selection of key parameters, and sensitivity analysis of application scenarios do industry for reference.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

Building-to-Grid Integration through Commercial Building Portfolios Participating in Energy and Frequency Regulation Markets

NASA Astrophysics Data System (ADS)

Pavlak, Gregory S.

Building energy use is a significant contributing factor to growing worldwide energy demands. In pursuit of a sustainable energy future, commercial building operations must be intelligently integrated with the electric system to increase efficiency and enable renewable generation. Toward this end, a model-based methodology was developed to estimate the capability of commercial buildings to participate in frequency regulation ancillary service markets. This methodology was integrated into a supervisory model predictive controller to optimize building operation in consideration of energy prices, demand charges, and ancillary service revenue. The supervisory control problem was extended to building portfolios to evaluate opportunities for synergistic effect among multiple, centrally-optimized buildings. Simulation studies performed showed that the multi-market optimization was able to determine appropriate opportunities for buildings to provide frequency regulation. Total savings were increased by up to thirteen percentage points, depending on the simulation case. Furthermore, optimizing buildings as a portfolio achieved up to seven additional percentage points of savings, depending on the case. Enhanced energy and cost savings opportunities were observed by taking the novel perspective of optimizing building portfolios in multiple grid markets, motivating future pursuits of advanced control paradigms that enable a more intelligent electric grid.

Cumulative metal leaching from utilisation of secondary building materials in river engineering.

PubMed

Leuven, R S E W; Willems, F H G

2004-01-01

The present paper estimates the utilisation of bulky wastes (minestone, steel slag, phosphorus slag and demolition waste) in hydraulic engineering structures in Dutch parts of the rivers Rhine, Meuse and Scheldt over the period 1980-2025. Although they offer several economic, technical and environmental benefits, these secondary building materials contain various metals that may leach into river water. A leaching model was used to predict annual emissions of arsenic, cadmium, copper, chromium, lead, mercury, nickel and zinc. Under the current utilisation and model assumptions, the contribution of secondary building materials to metal pollution in Dutch surface waters is expected to be relatively low compared to other sources (less than 0.1% and 0.2% in the years 2000 and 2025, respectively). However, continued and widespread large-scale applications of secondary building materials will increase pollutant leaching and may require further cuts to be made in emissions from other sources to meet emission reduction targets and water quality standards. It is recommended to validate available leaching models under various field conditions. Complete registration of secondary building materials will be required to improve input data for leaching models.

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

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

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

PubMed

Kennedy, Curtis E; Turley, James P

2011-10-24

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

Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

PubMed

Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

2017-09-01

Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

Public Databases Supporting Computational Toxicology

EPA Science Inventory

A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, resea...

Data for Room Fire Model Comparisons

PubMed Central

Peacock, Richard D.; Davis, Sanford; Babrauskas, Vytenis

1991-01-01

With the development of models to predict fire growth and spread in buildings, there has been a concomitant evolution in the measurement and analysis of experimental data in real-scale fires. This report presents the types of analyses that can be used to examine large-scale room fire test data to prepare the data for comparison with zone-based fire models. Five sets of experimental data which can be used to test the limits of a typical two-zone fire model are detailed. A standard set of nomenclature describing the geometry of the building and the quantities measured in each experiment is presented. Availability of ancillary data (such as smaller-scale test results) is included. These descriptions, along with the data (available in computer-readable form) should allow comparisons between the experiment and model predictions. The base of experimental data ranges in complexity from one room tests with individual furniture items to a series of tests conducted in a multiple story hotel equipped with a zoned smoke control system. PMID:28184121

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

An In Silico Method for Screening Nicotine Derivatives as Cytochrome P450 2A6 Selective Inhibitors Based on Kernel Partial Least Squares

PubMed Central

Wang, Yonghua; Li, Yan; Wang, Bin

2007-01-01

Nicotine and a variety of other drugs and toxins are metabolized by cytochrome P450 (CYP) 2A6. The aim of the present study was to build a quantitative structure-activity relationship (QSAR) model to predict the activities of nicotine analogues on CYP2A6. Kernel partial least squares (K-PLS) regression was employed with the electro-topological descriptors to build the computational models. Both the internal and external predictabilities of the models were evaluated with test sets to ensure their validity and reliability. As a comparison to K-PLS, a standard PLS algorithm was also applied on the same training and test sets. Our results show that the K-PLS produced reasonable results that outperformed the PLS model on the datasets. The obtained K-PLS model will be helpful for the design of novel nicotine-like selective CYP2A6 inhibitors.

Data for Room Fire Model Comparisons.

PubMed

Peacock, Richard D; Davis, Sanford; Babrauskas, Vytenis

1991-01-01

With the development of models to predict fire growth and spread in buildings, there has been a concomitant evolution in the measurement and analysis of experimental data in real-scale fires. This report presents the types of analyses that can be used to examine large-scale room fire test data to prepare the data for comparison with zone-based fire models. Five sets of experimental data which can be used to test the limits of a typical two-zone fire model are detailed. A standard set of nomenclature describing the geometry of the building and the quantities measured in each experiment is presented. Availability of ancillary data (such as smaller-scale test results) is included. These descriptions, along with the data (available in computer-readable form) should allow comparisons between the experiment and model predictions. The base of experimental data ranges in complexity from one room tests with individual furniture items to a series of tests conducted in a multiple story hotel equipped with a zoned smoke control system.

A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London

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

Kolokotroni, Maria; Bhuiyan, Saiful; Davies, Michael

2010-12-15

This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found thatmore » site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West-East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis. (author)« less

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

Tsunami Simulators in Physical Modelling Laboratories - From Concept to Proven Technique

NASA Astrophysics Data System (ADS)

Allsop, W.; Chandler, I.; Rossetto, T.; McGovern, D.; Petrone, C.; Robinson, D.

2016-12-01

Before 2004, there was little public awareness around Indian Ocean coasts of the potential size and effects of tsunami. Even in 2011, the scale and extent of devastation by the Japan East Coast Tsunami was unexpected. There were very few engineering tools to assess onshore impacts of tsunami, so no agreement on robust methods to predict forces on coastal defences, buildings or related infrastructure. Modelling generally used substantial simplifications of either solitary waves (far too short durations) or dam break (unrealistic and/or uncontrolled wave forms).This presentation will describe research from EPI-centre, HYDRALAB IV, URBANWAVES and CRUST projects over the last 10 years that have developed and refined pneumatic Tsunami Simulators for the hydraulic laboratory. These unique devices have been used to model generic elevated and N-wave tsunamis up to and over simple shorelines, and at example defences. They have reproduced full-duration tsunamis including the Mercator trace from 2004 at 1:50 scale. Engineering scale models subjected to those tsunamis have measured wave run-up on simple slopes, forces on idealised sea defences and pressures / forces on buildings. This presentation will describe how these pneumatic Tsunami Simulators work, demonstrate how they have generated tsunami waves longer than the facility within which they operate, and will highlight research results from the three generations of Tsunami Simulator. Of direct relevance to engineers and modellers will be measurements of wave run-up levels and comparison with theoretical predictions. Recent measurements of forces on individual buildings have been generalized by separate experiments on buildings (up to 4 rows) which show that the greatest forces can act on the landward (not seaward) buildings. Continuing research in the 70m long 4m wide Fast Flow Facility on tsunami defence structures have also measured forces on buildings in the lee of a failed defence wall.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Comparative Protein Structure Modeling Using MODELLER

PubMed Central

Webb, Benjamin; Sali, Andrej

2016-01-01

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. PMID:27322406

Benchmarking novel approaches for modelling species range dynamics

PubMed Central

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H.; Moore, Kara A.; Zimmermann, Niklaus E.

2016-01-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasise several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. PMID:26872305

Benchmarking novel approaches for modelling species range dynamics.

PubMed

Zurell, Damaris; Thuiller, Wilfried; Pagel, Jörn; Cabral, Juliano S; Münkemüller, Tamara; Gravel, Dominique; Dullinger, Stefan; Normand, Signe; Schiffers, Katja H; Moore, Kara A; Zimmermann, Niklaus E

2016-08-01

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species. © 2016 John Wiley & Sons Ltd.

Emotional Valence and Arousal Effects on Memory and Hemispheric Asymmetries

ERIC Educational Resources Information Center

Mneimne, Malek; Powers, Alice S.; Walton, Kate E.; Kosson, David S.; Fonda, Samantha; Simonetti, Jessica

2010-01-01

This study examined predictions based upon the right hemisphere (RH) model, the valence-arousal model, and a recently proposed integrated model (Killgore & Yurgelun-Todd, 2007) of emotion processing by testing immediate recall and recognition memory for positive, negative, and neutral verbal stimuli among 35 right-handed women. Building upon…

First Steps in Computational Systems Biology: A Practical Session in Metabolic Modeling and Simulation

ERIC Educational Resources Information Center

Reyes-Palomares, Armando; Sanchez-Jimenez, Francisca; Medina, Miguel Angel

2009-01-01

A comprehensive understanding of biological functions requires new systemic perspectives, such as those provided by systems biology. Systems biology approaches are hypothesis-driven and involve iterative rounds of model building, prediction, experimentation, model refinement, and development. Developments in computer science are allowing for ever…

A Probabilistic Model of Phonological Relationships from Contrast to Allophony

ERIC Educational Resources Information Center

Hall, Kathleen Currie

2009-01-01

This dissertation proposes a model of phonological relationships, the Probabilistic Phonological Relationship Model (PPRM), that quantifies how predictably distributed two sounds in a relationship are. It builds on a core premise of traditional phonological analysis, that the ability to define phonological relationships such as contrast and…

Forty years of Fanger's model of thermal comfort: comfort for all?

PubMed

van Hoof, J

2008-06-01

The predicted mean vote (PMV) model of thermal comfort, created by Fanger in the late 1960s, is used worldwide to assess thermal comfort. Fanger based his model on college-aged students for use in invariant environmental conditions in air-conditioned buildings in moderate thermal climate zones. Environmental engineering practice calls for a predictive method that is applicable to all types of people in any kind of building in every climate zone. In this publication, existing support and criticism, as well as modifications to the PMV model are discussed in light of the requirements by environmental engineering practice in the 21st century in order to move from a predicted mean vote to comfort for all. Improved prediction of thermal comfort can be achieved through improving the validity of the PMV model, better specification of the model's input parameters, and accounting for outdoor thermal conditions and special groups. The application range of the PMV model can be enlarged, for instance, by using the model to assess the effects of the thermal environment on productivity and behavior, and interactions with other indoor environmental parameters, and the use of information and communication technologies. Even with such modifications to thermal comfort evaluation, thermal comfort for all can only be achieved when occupants have effective control over their own thermal environment. The paper treats the assessment of thermal comfort using the PMV model of Fanger, and deals with the strengths and limitations of this model. Readers are made familiar to some opportunities for use in the 21st-century information society.

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building.

PubMed

Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

2016-07-01

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

PubMed Central

Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

2016-01-01

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement. PMID:27376303

Refining metabolic models and accounting for regulatory effects.

PubMed

Kim, Joonhoon; Reed, Jennifer L

2014-10-01

Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach

USGS Publications Warehouse

van Verseveld, H.C.W.; Van Dongeren, A. R.; Plant, Nathaniel G.; Jäger, W.S.; den Heijer, C.

2015-01-01

Hurricane flood impacts to residential buildings in coastal zones are caused by a number of hazards, such as inundation, overflow currents, erosion, and wave attack. However, traditional hurricane damage models typically make use of stage-damage functions, where the stage is related to flooding depth only. Moreover, these models are deterministic and do not consider the large amount of uncertainty associated with both the processes themselves and with the predictions. This uncertainty becomes increasingly important when multiple hazards (flooding, wave attack, erosion, etc.) are considered simultaneously. This paper focusses on establishing relationships between observed damage and multiple hazard indicators in order to make better probabilistic predictions. The concept consists of (1) determining Local Hazard Indicators (LHIs) from a hindcasted storm with use of a nearshore morphodynamic model, XBeach, and (2) coupling these LHIs and building characteristics to the observed damages. We chose a Bayesian Network approach in order to make this coupling and used the LHIs ‘Inundation depth’, ‘Flow velocity’, ‘Wave attack’, and ‘Scour depth’ to represent flooding, current, wave impacts, and erosion related hazards.The coupled hazard model was tested against four thousand damage observations from a case site at the Rockaway Peninsula, NY, that was impacted by Hurricane Sandy in late October, 2012. The model was able to accurately distinguish ‘Minor damage’ from all other outcomes 95% of the time and could distinguish areas that were affected by the storm, but not severely damaged, 68% of the time. For the most heavily damaged buildings (‘Major Damage’ and ‘Destroyed’), projections of the expected damage underestimated the observed damage. The model demonstrated that including multiple hazards doubled the prediction skill, with Log-Likelihood Ratio test (a measure of improved accuracy and reduction in uncertainty) scores between 0.02 and 0.17 when only one hazard is considered and a score of 0.37 when multiple hazards are considered simultaneously. The LHIs with the most predictive skill were ‘Inundation depth’ and ‘Wave attack’. The Bayesian Network approach has several advantages over the market-standard stage-damage functions: the predictive capacity of multiple indicators can be combined; probabilistic predictions can be obtained, which include uncertainty; and quantitative as well as descriptive information can be used simultaneously.

Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes

NASA Astrophysics Data System (ADS)

Toropov, Andrey A.; Toropova, Alla P.

2018-06-01

Predictive model of logP for Pt(II) and Pt(IV) complexes built up with the Monte Carlo method using the CORAL software has been validated with six different splits into the training and validation sets. The improving of the predictive potential of models for six different splits has been obtained using so-called index of ideality of correlation. The suggested models give possibility to extract molecular features, which cause the increase or vice versa decrease of the logP.

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

Crowd-sourced data collection to support automatic classification of building footprint data

NASA Astrophysics Data System (ADS)

Hecht, Robert; Kalla, Matthias; Krüger, Tobias

2018-05-01

Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of floors. For this reason, predictive modeling is becoming increasingly important in this context. The capabilities of machine learning allow for the prediction of building types and other building characteristics and thus, the efficient classification and description of the entire building stock of cities and regions. However, such data-driven approaches always require a sufficient amount of ground truth (reference) information for training and validation. The collection of reference data is usually cost-intensive and time-consuming. Experiences from other disciplines have shown that crowdsourcing offers the possibility to support the process of obtaining ground truth data. Therefore, this paper presents the results of an experimental study aiming at assessing the accuracy of non-expert annotations on street view images collected from an internet crowd. The findings provide the basis for a future integration of a crowdsourcing component into the process of land use mapping, particularly the automatic building classification.

PARAMO: A Parallel Predictive Modeling Platform for Healthcare Analytic Research using Electronic Health Records

PubMed Central

Ng, Kenney; Ghoting, Amol; Steinhubl, Steven R.; Stewart, Walter F.; Malin, Bradley; Sun, Jimeng

2014-01-01

Objective Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: 1) cohort construction, 2) feature construction, 3) cross-validation, 4) feature selection, and 5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. Methods To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which 1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, 2) schedules the tasks in a topological ordering of the graph, and 3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. Results We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3 hours in parallel compared to 9 days if running sequentially. Conclusion This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers. PMID:24370496

PARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.

PubMed

Ng, Kenney; Ghoting, Amol; Steinhubl, Steven R; Stewart, Walter F; Malin, Bradley; Sun, Jimeng

2014-04-01

Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3h in parallel compared to 9days if running sequentially. This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers. Copyright © 2013 Elsevier Inc. All rights reserved.

Contam airflow models of three large buildings: Model descriptions and validation

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

Black, Douglas R.; Price, Phillip N.

2009-09-30

Airflow and pollutant transport models are useful for several reasons, including protection from or response to biological terrorism. In recent years they have been used for deciding how many biological agent samplers are needed in a given building to detect the release of an agent; to figure out where those samplers should be located; to predict the number of people at risk in the event of a release of a given size and location; to devise response strategies in the event of a release; to determine optimal trade-offs between sampler characteristics (such as detection limit and response time); and somore » on. For some of these purposes it is necessary to model a specific building of interest: if you are trying to determine optimal sampling locations, you must have a model of your building and not some different building. But for many purposes generic or 'prototypical' building models would suffice. For example, for determining trade-offs between sampler characteristics, results from one building will carry over other, similar buildings. Prototypical building models are also useful for comparing or testing different algorithms or computational pproaches: different researchers can use the same models, thus allowing direct comparison of results in a way that is not otherwise possible. This document discusses prototypical building models developed by the Airflow and Pollutant Transport Group at Lawrence Berkeley National Laboratory. The models are implemented in the Contam v2.4c modeling program, available from the National Institutes for Standards and Technology. We present Contam airflow models of three virtual buildings: a convention center, an airport terminal, and a multi-story office building. All of the models are based to some extent on specific real buildings. Our goal is to produce models that are realistic, in terms of approximate magnitudes, directions, and speeds of airflow and pollutant transport. The three models vary substantially in detail. The airport model is the simplest; the onvention center model is more detailed; and the large office building model is quite complicated. We give several simplified floor plans in this document, to explain basic features of the buildings. The actual models are somewhat more complicated; for instance, spaces that are represented as rectangles in this document sometimes have more complicated shapes in the models. (However, note that the shape of a zone is irrelevant in Contam). Consult the Contam models themselves for detailed floor plans. Each building model is provided with three ventilation conditions, representing mechanical systems in which 20%, 50%, or 80% of the building air is recirculated and the rest is provided from outdoors. Please see the section on 'Use of the models' for important information about issues to consider if you wish to modify the models to provide no mechanical ventilation or eliminate provision of outdoor air.« less

Predicting Cost/Performance Trade-Offs for Whitney: A Commodity Computing Cluster

NASA Technical Reports Server (NTRS)

Becker, Jeffrey C.; Nitzberg, Bill; VanderWijngaart, Rob F.; Kutler, Paul (Technical Monitor)

1997-01-01

Recent advances in low-end processor and network technology have made it possible to build a "supercomputer" out of commodity components. We develop simple models of the NAS Parallel Benchmarks version 2 (NPB 2) to explore the cost/performance trade-offs involved in building a balanced parallel computer supporting a scientific workload. We develop closed form expressions detailing the number and size of messages sent by each benchmark. Coupling these with measured single processor performance, network latency, and network bandwidth, our models predict benchmark performance to within 30%. A comparison based on total system cost reveals that current commodity technology (200 MHz Pentium Pros with 100baseT Ethernet) is well balanced for the NPBs up to a total system cost of around $1,000,000.

Statistical Power for a Simultaneous Test of Factorial and Predictive Invariance

ERIC Educational Resources Information Center

Olivera-Aguilar, Margarita; Millsap, Roger E.

2013-01-01

A common finding in studies of differential prediction across groups is that although regression slopes are the same or similar across groups, group differences exist in regression intercepts. Building on earlier work by Birnbaum (1979), Millsap (1998) presented an invariant factor model that would explain such intercept differences as arising due…

Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures

NASA Astrophysics Data System (ADS)

Emory, Michael; Larsson, Johan; Iaccarino, Gianluca

2013-11-01

Estimation of the uncertainty in numerical predictions by Reynolds-averaged Navier-Stokes closures is a vital step in building confidence in such predictions. An approach to model-form uncertainty quantification that does not assume the eddy-viscosity hypothesis to be exact is proposed. The methodology for estimation of uncertainty is demonstrated for plane channel flow, for a duct with secondary flows, and for the shock/boundary-layer interaction over a transonic bump.

Model-based tomographic reconstruction

DOEpatents

Chambers, David H; Lehman, Sean K; Goodman, Dennis M

2012-06-26

A model-based approach to estimating wall positions for a building is developed and tested using simulated data. It borrows two techniques from geophysical inversion problems, layer stripping and stacking, and combines them with a model-based estimation algorithm that minimizes the mean-square error between the predicted signal and the data. The technique is designed to process multiple looks from an ultra wideband radar array. The processed signal is time-gated and each section processed to detect the presence of a wall and estimate its position, thickness, and material parameters. The floor plan of a building is determined by moving the array around the outside of the building. In this paper we describe how the stacking and layer stripping algorithms are combined and show the results from a simple numerical example of three parallel walls.

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

Hao, He; Sun, Yannan; Carroll, Thomas E.

We propose a coordination algorithm for cooperative power allocation among a collection of commercial buildings within a campus. We introduced thermal and power models of a typical commercial building Heating, Ventilation, and Air Conditioning (HVAC) system, and utilize model predictive control to characterize their power flexibility. The power allocation problem is formulated as a cooperative game using the Nash Bargaining Solution (NBS) concept, in which buildings collectively maximize the product of their utilities subject to their local flexibility constraints and a total power limit set by the campus coordinator. To solve the optimal allocation problem, a distributed protocol is designedmore » using dual decomposition of the Nash bargaining problem. Numerical simulations are performed to demonstrate the efficacy of our proposed allocation method« less

Numerical simulation to determine the effects of incident wind shear and turbulence level on the flow around a building

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

Zhang, Y.Q.; Huber, A.H.; Arya, S.P.S.

The effects of incident shear and turbulence on flow around a cubical building are being investigated by a turbulent kinetic energy/dissipation model (TEMPEST). The numerical simulations demonstrate significant effects due to the differences in the incident flow. The addition of upstream turbulence and shear results in a reduced size of the cavity directly behind the building. The accuracy of numerical simulations is verified by comparing the predicted mean flow fields with the available wind-tunnel measurements of Castro and Robins (1977). Comparing the authors' results with experimental data, the authors show that the TEMPEST model can reasonably simulate the mean flow.

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP and serum Ca2+ individually (P-value<0.01). Verification of the internal validity of the models by bootstrapping is favorable. BISAP and serum Ca2+ have high predictive value for the severity of AP. However, the model built by combining BISAP and serum Ca2+ is remarkably superior to those of BISAP and serum Ca2+ individually. Furthermore, this model is simple, practical and appropriate for clinical use. Copyright © 2016. Published by Elsevier Masson SAS.

The use of least squares methods in functional optimization of energy use prediction models

NASA Astrophysics Data System (ADS)

Bourisli, Raed I.; Al-Shammeri, Basma S.; AlAnzi, Adnan A.

2012-06-01

The least squares method (LSM) is used to optimize the coefficients of a closed-form correlation that predicts the annual energy use of buildings based on key envelope design and thermal parameters. Specifically, annual energy use is related to a number parameters like the overall heat transfer coefficients of the wall, roof and glazing, glazing percentage, and building surface area. The building used as a case study is a previously energy-audited mosque in a suburb of Kuwait City, Kuwait. Energy audit results are used to fine-tune the base case mosque model in the VisualDOE{trade mark, serif} software. Subsequently, 1625 different cases of mosques with varying parameters were developed and simulated in order to provide the training data sets for the LSM optimizer. Coefficients of the proposed correlation are then optimized using multivariate least squares analysis. The objective is to minimize the difference between the correlation-predicted results and the VisualDOE-simulation results. It was found that the resulting correlation is able to come up with coefficients for the proposed correlation that reduce the difference between the simulated and predicted results to about 0.81%. In terms of the effects of the various parameters, the newly-defined weighted surface area parameter was found to have the greatest effect on the normalized annual energy use. Insulating the roofs and walls also had a major effect on the building energy use. The proposed correlation and methodology can be used during preliminary design stages to inexpensively assess the impacts of various design variables on the expected energy use. On the other hand, the method can also be used by municipality officials and planners as a tool for recommending energy conservation measures and fine-tuning energy codes.

Construction of ground-state preserving sparse lattice models for predictive materials simulations

NASA Astrophysics Data System (ADS)

Huang, Wenxuan; Urban, Alexander; Rong, Ziqin; Ding, Zhiwei; Luo, Chuan; Ceder, Gerbrand

2017-08-01

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1-x)O2 and Li2xTi2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.

PubMed

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.

A Research Methodology for Studying What Makes Some Problems Difficult to Solve

ERIC Educational Resources Information Center

Gulacar, Ozcan; Fynewever, Herb

2010-01-01

We present a quantitative model for predicting the level of difficulty subjects will experience with specific problems. The model explicitly accounts for the number of subproblems a problem can be broken into and the difficultly of each subproblem. Although the model builds on previously published models, it is uniquely suited for blending with…

Optimal temperature for malaria transmission is dramaticallylower than previously predicted

USGS Publications Warehouse

Mordecai, Eerin A.; Paaijmans, Krijin P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

Optimal temperature for malaria transmission is dramatically lower than previously predicted

USGS Publications Warehouse

Mordecai, Erin A.; Paaijmans, Krijn P.; Johnson, Leah R.; Balzer, Christian; Ben-Horin, Tal; de Moor, Emily; McNally, Amy; Pawar, Samraat; Ryan, Sadie J.; Smith, Thomas C.; Lafferty, Kevin D.

2013-01-01

The ecology of mosquito vectors and malaria parasites affect the incidence, seasonal transmission and geographical range of malaria. Most malaria models to date assume constant or linear responses of mosquito and parasite life-history traits to temperature, predicting optimal transmission at 31 °C. These models are at odds with field observations of transmission dating back nearly a century. We build a model with more realistic ecological assumptions about the thermal physiology of insects. Our model, which includes empirically derived nonlinear thermal responses, predicts optimal malaria transmission at 25 °C (6 °C lower than previous models). Moreover, the model predicts that transmission decreases dramatically at temperatures > 28 °C, altering predictions about how climate change will affect malaria. A large data set on malaria transmission risk in Africa validates both the 25 °C optimum and the decline above 28 °C. Using these more accurate nonlinear thermal-response models will aid in understanding the effects of current and future temperature regimes on disease transmission.

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model.

PubMed

Saini, Harsh; Raicar, Gaurav; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

2015-12-07

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Kaplanis, S., E-mail: kaplanis@teipat.gr; Kaplani, E.

The paper presents the design features, the energy modelling and optical performance details of two pilot Intelligent Energy Buildings, (IEB). Both are evolution of the Zero Energy Building (ZEB) concept. RES innovations backed up by signal processing, simulation models and ICT tools were embedded into the building structures in order to implement a new predictive energy management concept. In addition, nano-coatings, produced by TiO2 and ITO nano-particles, were deposited on the IEB structural elements and especially on the window panes and the PV glass covers. They exhibited promising SSP values which lowered the cooling loads and increased the PV modulesmore » yield. Both pilot IEB units were equipped with an on-line dynamic hourly solar radiation prediction model, implemented by sensors and the related software to manage effectively the energy source, the loads and the storage or the backup system. The IEB energy sources covered the thermal loads via a south façade embedded in the wall and a solar roof which consists of a specially designed solar collector type, while a PV generator is part of the solar roof, like a compact BIPV in hybrid configuration to a small wind turbine.« less

A Systematic Approach to the Study of Accelerated weathering of Building Joint Sealants

Treesearch

Christopher C. White; Donald L. Hunston; Kar Tean Tan; James J. Filliben; Adam L. Pintar; Greg Schueneman

2012-01-01

An accurate service life prediction model is needed for building joint sealants in order to greatly reduce the time to market of a new product and reduce the risk of introducing a poorly performing product into the marketplace. A stepping stone to the success of this effort is the precise control of environmental variables in a laboratory accelerated test apparatus in...

Examination of thermal comfort in a hospital using PMV-PPD model.

PubMed

Pourshaghaghy, A; Omidvari, M

2012-11-01

In this study, the performance of air conditioning system and the level of thermal comfort are determined in a state hospital located in Kermanshah city in the west of Iran in winter and summer using the Predicted Mean Vote (PMV) model which has been presented by ISO-7730 (2005). The Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) indices were computed using the data acquired from the experimental measurements performed in the building. The results showed that the values of PMV in some parts of the building, both for men and women, are not within the standard acceptable range defined by ISO. It was found that the most thermal problems in winter occur in morning work shift, and the worst thermal conditions in summer occur in noon work shift. The t-test results revealed that there is no noticeable difference between the thermal conditions of some rooms and those of the surroundings. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

What do we gain from simplicity versus complexity in species distribution models?

USGS Publications Warehouse

Merow, Cory; Smith, Matthew J.; Edwards, Thomas C.; Guisan, Antoine; McMahon, Sean M.; Normand, Signe; Thuiller, Wilfried; Wuest, Rafael O.; Zimmermann, Niklaus E.; Elith, Jane

2014-01-01

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Forces associated with pneumatic power screwdriver operation: statics and dynamics.

PubMed

Lin, Jia-Hua; Radwin, Robert G; Fronczak, Frank J; Richard, Terry G

2003-10-10

The statics and dynamics of pneumatic power screwdriver operation were investigated in the context of predicting forces acting against the human operator. A static force model is described in the paper, based on tool geometry, mass, orientation in space, feed force, torque build up, and stall torque. Three common power hand tool shapes are considered, including pistol grip, right angle, and in-line. The static model estimates handle force needed to support a power nutrunner when it acts against the tightened fastener with a constant torque. A system of equations for static force and moment equilibrium conditions are established, and the resultant handle force (resolved in orthogonal directions) is calculated in matrix form. A dynamic model is formulated to describe pneumatic motor torque build-up characteristics dependent on threaded fastener joint hardness. Six pneumatic tools were tested to validate the deterministic model. The average torque prediction error was 6.6% (SD = 5.4%) and the average handle force prediction error was 6.7% (SD = 6.4%) for a medium-soft threaded fastener joint. The average torque prediction error was 5.2% (SD = 5.3%) and the average handle force prediction error was 3.6% (SD = 3.2%) for a hard threaded fastener joint. Use of these equations for estimating handle forces based on passive mechanical elements representing the human operator is also described. These models together should be useful for considering tool handle force in the selection and design of power screwdrivers, particularly for minimizing handle forces in the prevention of injuries and work related musculoskeletal disorders.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

PubMed

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

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

Baker, Kyri A; Shi, Ying; Christensen, Dane T

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

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

Raszmann, Emma; Baker, Kyri; Shi, Ying

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

PubMed

Fenlon, Caroline; O'Grady, Luke; Butler, Stephen; Doherty, Michael L; Dunnion, John

2017-01-01

Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

PubMed

Hieke, Stefanie; Benner, Axel; Schlenl, Richard F; Schumacher, Martin; Bullinger, Lars; Binder, Harald

2016-08-30

High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well. We consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.

Optimized endogenous post-stratification in forest inventories

Treesearch

Paul L. Patterson

2012-01-01

An example of endogenous post-stratification is the use of remote sensing data with a sample of ground data to build a logistic regression model to predict the probability that a plot is forested and using the predicted probabilities to form categories for post-stratification. An optimized endogenous post-stratified estimator of the proportion of forest has been...

Prediction of a Therapeutic Dose for Buagafuran, a Potent Anxiolytic Agent by Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Starting from Pharmacokinetics in Rats and Human.

PubMed

Yang, Fen; Wang, Baolian; Liu, Zhihao; Xia, Xuejun; Wang, Weijun; Yin, Dali; Sheng, Li; Li, Yan

2017-01-01

Physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) models can contribute to animal-to-human extrapolation and therapeutic dose predictions. Buagafuran is a novel anxiolytic agent and phase I clinical trials of buagafuran have been completed. In this paper, a potentially effective dose for buagafuran of 30 mg t.i.d. in human was estimated based on the human brain concentration predicted by a PBPK/PD modeling. The software GastroPlus TM was used to build the PBPK/PD model for buagafuran in rat which related the brain tissue concentrations of buagafuran and the times of animals entering the open arms in the pharmacological model of elevated plus-maze. Buagafuran concentrations in human plasma were fitted and brain tissue concentrations were predicted by using a human PBPK model in which the predicted plasma profiles were in good agreement with observations. The results provided supportive data for the rational use of buagafuran in clinic.

NOAA Climate Program Office Contributions to National ESPC

NASA Astrophysics Data System (ADS)

Higgins, W.; Huang, J.; Mariotti, A.; Archambault, H. M.; Barrie, D.; Lucas, S. E.; Mathis, J. T.; Legler, D. M.; Pulwarty, R. S.; Nierenberg, C.; Jones, H.; Cortinas, J. V., Jr.; Carman, J.

2016-12-01

NOAA is one of five federal agencies (DOD, DOE, NASA, NOAA, and NSF) which signed an updated charter in 2016 to partner on the National Earth System Prediction Capability (ESPC). Situated within NOAA's Office of Oceanic and Atmospheric Research (OAR), NOAA Climate Program Office (CPO) programs contribute significantly to the National ESPC goals and activities. This presentation will provide an overview of CPO contributions to National ESPC. First, we will discuss selected CPO research and transition activities that directly benefit the ESPC coupled model prediction capability, including The North American Multi-Model Ensemble (NMME) seasonal prediction system The Subseasonal Experiment (SubX) project to test real-time subseasonal ensemble prediction systems. Improvements to the NOAA operational Climate Forecast System (CFS), including software infrastructure and data assimilation. Next, we will show how CPO's foundational research activities are advancing future ESPC capabilities. Highlights will include: The Tropical Pacific Observing System (TPOS) to provide the basis for predicting climate on subseasonal to decadal timescales. Subseasonal-to-Seasonal (S2S) processes and predictability studies to improve understanding, modeling and prediction of the MJO. An Arctic Research Program to address urgent needs for advancing monitoring and prediction capabilities in this major area of concern. Advances towards building an experimental multi-decadal prediction system through studies on the Atlantic Meridional Overturning Circulation (AMOC). Finally, CPO has embraced Integrated Information Systems (IIS's) that build on the innovation of programs such as the National Integrated Drought Information System (NIDIS) to develop and deliver end to end environmental information for key societal challenges (e.g. extreme heat; coastal flooding). These contributions will help the National ESPC better understand and address societal needs and decision support requirements.

Reserve-building activities attenuate treatment burden in chronic illness: The mediating role of appraisal and social support

PubMed Central

Schwartz, Carolyn E; Zhang, Jie; Michael, Wesley; Eton, David T; Rapkin, Bruce D

2018-01-01

This study examines the importance of four psychosocial factors—personality, cognitive appraisal of quality of life, social support, and current reserve-building—in predicting treatment burden in chronically ill patients. Chronically ill patients (n = 446) completed web-based measures. Structural equation modeling was used to investigate psychosocial factors predicting treatment burden. Reserve-building activities indirectly reduced treatment burden by: (1) reducing health worries appraisals, (2) reducing financial difficulties, (3) increasing calm and peaceful appraisals, and (4) increasing perceived social support. These findings point to key behaviors that chronically ill people can use to attenuate their treatment burden. PMID:29785278

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Technology Solutions Case Study: Predicting Envelope Leakage in Attached Dwellings

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

None

2013-11-01

The most common method of measuring air leakage is to perform single (or solo) blower door pressurization and/or depressurization test. In detached housing, the single blower door test measures leakage to the outside. In attached housing, however, this “solo” test method measures both air leakage to the outside and air leakage between adjacent units through common surfaces. In an attempt to create a simplified tool for predicting leakage to the outside, Building America team Consortium for Advanced Residential Buildings (CARB) performed a preliminary statistical analysis on blower door test results from 112 attached dwelling units in four apartment complexes. Althoughmore » the subject data set is limited in size and variety, the preliminary analyses suggest significant predictors are present and support the development of a predictive model. Further data collection is underway to create a more robust prediction tool for use across different construction types, climate zones, and unit configurations.« less

Energy simulation and optimization for a small commercial building through Modelica

NASA Astrophysics Data System (ADS)

Rivas, Bryan

Small commercial buildings make up the majority of buildings in the United States. Energy consumed by these buildings is expected to drastically increase in the next few decades, with a large percentage of the energy consumed attributed to cooling systems. This work presents the simulation and optimization of a thermostat schedule to minimize energy consumption in a small commercial building test bed during the cooling season. The simulation occurs through the use of the multi-engineering domain Dymola environment based on the Modelica open source programming language and is optimized with the Java based optimization program GenOpt. The simulation uses both physically based modeling utilizing heat transfer principles for the building and regression analysis for energy consumption. GenOpt is dynamically coupled to Dymola through various interface files. There are very few studies that have coupled GenOpt to a building simulation program and even fewer studies have used Dymola for building simulation as extensively as the work presented here. The work presented proves Dymola as a viable alternative to other building simulation programs such as EnergyPlus and MatLab. The model developed is used to simulate the energy consumption of a test bed, a commissioned real world small commercial building, while maintaining indoor thermal comfort. Potential applications include smart or intelligent building systems, predictive simulation of small commercial buildings, and building diagnostics.

Occupancy schedules learning process through a data mining framework

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

D'Oca, Simona; Hong, Tianzhen

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Estimating Building Age with 3d GIS

NASA Astrophysics Data System (ADS)

Biljecki, F.; Sindram, M.

2017-10-01

Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

Occupancy schedules learning process through a data mining framework

DOE PAGES

D'Oca, Simona; Hong, Tianzhen

2014-12-17

Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10more » minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.« less

Predicting the mortality from asbestos-related diseases based on the amount of asbestos used and the effects of slate buildings in Korea.

PubMed

Kim, Su-Young; Kim, Young-Chan; Kim, Yongku; Hong, Won-Hwa

2016-01-15

Asbestos has been used since ancient times, owing to its heat-resistant, rot-proof, and insulating qualities, and its usage rapidly increased after the industrial revolution. In Korea, all slates were previously manufactured in a mixture of about 90% cement and 10% chrysotile (white asbestos). This study used a Generalized Poisson regression (GPR) model after creating databases of the mortality from asbestos-related diseases and of the amount of asbestos used in Korea as a means to predict the future mortality of asbestos-related diseases and mesothelioma in Korea. Moreover, to predict the future mortality according to the effects of slate buildings, a comparative analysis based on the result of the GPR model was conducted after creating databases of the amount of asbestos used in Korea and of the amount of asbestos used in making slates. We predicted the mortality from asbestos-related diseases by year, from 2014 to 2036, according to the amount of asbestos used. As a result, it was predicted that a total of 1942 people (maximum, 3476) will die by 2036. Moreover, based on the comparative analysis according to the influence index, it was predicted that a maximum of 555 people will die from asbestos-related diseases by 2031 as a result of the effects of asbestos-containing slate buildings, and the mortality was predicted to peak in 2021, with 53 cases. Although mesothelioma and pulmonary asbestosis were considered as asbestos-related diseases, these are not the only two diseases caused by asbestos. However the results of this study are highly important and relevant, as, for the first time in Korea, the future mortality from asbestos-related diseases was predicted. These findings are expected to contribute greatly to the Korean government's policies related to the compensation for asbestos victims. Copyright © 2015 Elsevier B.V. All rights reserved.

Coupling of three-dimensional field and human thermoregulatory models in a crowded enclosure

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

Xue, H.; Kang, Z.J.; Bong, T.Y.

1999-11-12

Health, comfort, and energy conservation are important factors to consider in the design of a building and its HVAC systems. Advanced tools are required to evaluate parameters regarding airflow, temperature, and humidity ratio in buildings, with the end results being better indoor air quality and thermal environment as well as increased confidence in the performance of buildings. A numerical model coupling the three-dimensional field and human thermoregulatory models is proposed and developed. A high-Re {kappa}-{epsilon} turbulence model is used for the field simulation. A modified 25-mode model of human thermoregulation is adopted to predict human thermal response in physiological parameters,more » such as body temperature and body heat loss. Distributions of air velocity, temperature, and moisture content are demonstrated in a crowded enclosure with mechanical ventilation under two ventilation rates. The results are analyzed and discussed. The coupling model is useful in assisting and verifying ventilation and air-conditioning system designs.« less

Measurement of ground and nearby building vibration and noise induced by trains in a metro depot.

PubMed

Zou, Chao; Wang, Yimin; Wang, Peng; Guo, Jixing

2015-12-01

Metro depots are where subway trains are parked and where maintenance is carried out. They usually occupy the largest ground areas in metro projects. Due to land utilization problems, Chinese cities have begun to develop over-track buildings above metro depots for people's life and work. The frequently moving trains, when going into and out of metro depots, can cause excessive vibration and noise to over-track buildings and adversely affect the living quality of the building occupants. Considering the current need of reliable experimental data for the construction of metro depots, field measurements of vibration and noise on the ground and inside a nearby 3-story building subjected to moving subway trains were conducted in a metro depot at Guangzhou, China. The amplitudes and frequency contents of velocity levels were quantified and compared. The composite A-weighted equivalent sound levels and maximum sound levels were captured. The predicted models for vibration and noise of metro depot were proposed based on existing models and verified. It was found that the vertical vibrations were significantly greater than the horizontal vibrations on the ground and inside the building near the testing line. While at the throat area, the horizontal vibrations near the curved track were remarkably greater than the vertical vibrations. The attenuation of the vibrations with frequencies above 50 Hz was larger than the ones below 50 Hz, and the frequencies of vibration transmitting to adjacent buildings were mainly within 10-50 Hz. The largest equivalent sound level generated in the throat area was smaller than the testing line one, but the instantaneous maximum sound level induced by wheels squeal, contact between wheels and rail joints as well as turnout was close to or even greater than the testing line one. The predicted models gave a first estimation for design and assessment of newly built metro depots. Copyright © 2015 Elsevier B.V. All rights reserved.

Simulation of probabilistic wind loads and building analysis

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Chamis, Christos C.

1991-01-01

Probabilistic wind loads likely to occur on a structure during its design life are predicted. Described here is a suitable multifactor interactive equation (MFIE) model and its use in the Composite Load Spectra (CLS) computer program to simulate the wind pressure cumulative distribution functions on four sides of a building. The simulated probabilistic wind pressure load was applied to a building frame, and cumulative distribution functions of sway displacements and reliability against overturning were obtained using NESSUS (Numerical Evaluation of Stochastic Structure Under Stress), a stochastic finite element computer code. The geometry of the building and the properties of building members were also considered as random in the NESSUS analysis. The uncertainties of wind pressure, building geometry, and member section property were qualified in terms of their respective sensitivities on the structural response.

Prediction of Fracture Initiation in Hot Compression of Burn-Resistant Ti-35V-15Cr-0.3Si-0.1C Alloy

NASA Astrophysics Data System (ADS)

Zhang, Saifei; Zeng, Weidong; Zhou, Dadi; Lai, Yunjin

2015-11-01

An important concern in hot working of metals is whether the desired deformation can be accomplished without fracture of the material. This paper builds a fracture prediction model to predict fracture initiation in hot compression of a burn-resistant beta-stabilized titanium alloy Ti-35V-15Cr-0.3Si-0.1C using a combined approach of upsetting experiments, theoretical failure criteria and finite element (FE) simulation techniques. A series of isothermal compression experiments on cylindrical specimens were conducted in temperature range of 900-1150 °C, strain rate of 0.01-10 s-1 first to obtain fracture samples and primary reduction data. Based on that, a comparison of eight commonly used theoretical failure criteria was made and Oh criterion was selected and coded into a subroutine. FE simulation of upsetting experiments on cylindrical specimens was then performed to determine the fracture threshold values of Oh criterion. By building a correlation between threshold values and the deforming parameters (temperature and strain rate, or Zener-Hollomon parameter), a new fracture prediction model based on Oh criterion was established. The new model shows an exponential decay relationship between threshold values and Zener-Hollomon parameter (Z), and the relative error of the model is less than 15%. This model was then applied successfully in the cogging of Ti-35V-15Cr-0.3Si-0.1C billet.

A growth inhibitory model with SOx influenced effective growth rate for estimation of algal biomass concentration under flue gas atmosphere

USDA-ARS?s Scientific Manuscript database

A theoretical model for the prediction of biomass concentration under real flue gas emission has been developed. The model considers the CO2 mass transfer rate, the critical SOx concentration and its role on pH based inter-conversion of bicarbonate in model building. The calibration and subsequent v...

Tensor Basis Neural Network v. 1.0 (beta)

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

Ling, Julia; Templeton, Jeremy

This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

Large-Eddy Simulation on Plume Dispersion within Regular Arrays of Cubic Buildings

NASA Astrophysics Data System (ADS)

Nakayama, H.; Jurcakova, K.; Nagai, H.

2010-09-01

There is a potential problem that hazardous and flammable materials are accidentally or intentionally released into the atmosphere, either within or close to populated urban areas. For the assessment of human health hazard from toxic substances, the existence of high concentration peaks in a plume should be considered. For the safety analysis of flammable gas, certain critical threshold levels should be evaluated. Therefore, in such a situation, not only average levels but also instantaneous magnitudes of concentration should be accurately predicted. However, plume dispersion is an extremely complicated process strongly influenced by the existence of buildings. In complex turbulent flows, such as impinging, separated and circulation flows around buildings, plume behaviors can be no longer accurately predicted using empirical Gaussian-type plume model. Therefore, we perform Large-Eddy Simulations (LES) on turbulent flows and plume dispersions within and over regular arrays of cubic buildings with various roughness densities and investigate the influence of the building arrangement pattern on the characteristics of mean and fluctuation concentrations. The basic equations for the LES model are composed of the spatially filtered continuity equation, Navier-Stokes equation and transport equation of concentration. The standard Smagorinsky model (Smagorinsky, 1963) that has enough potential for environment flows is used and its constant is set to 0.12 for estimating the eddy viscosity. The turbulent Schmidt number is 0.5. In our LES model, two computational regions are set up. One is a driver region for generation of inflow turbulence and the other is a main region for LES of plume dispersion within a regular array of cubic buildings. First, inflow turbulence is generated by using Kataoka's method (2002) in the driver region and then, its data are imposed at the inlet of the main computational region at each time step. In this study, the cubic building arrays with λf=0.16, 0.25 and 0.33 are set up (λf: the building frontal area index). These surface geometries consist of 20×6, 25×7 and 28×9 arrays in streamwise and spanwise directions, respectively. Three cases of plume source located at the ground surface behind the building in the 6th, 7th and 8th row of the building array are tested. It is found that the patterns of the dispersion behavior depending on roughness density are successfully simulated and the spatial distributions of mean and fluctuating concentrations are also captured within and over the building arrays in comparison with the wind tunnel experiments conducted by Bezpalcová (2008).

Decompression models: review, relevance and validation capabilities.

PubMed

Hugon, J

2014-01-01

For more than a century, several types of mathematical models have been proposed to describe tissue desaturation mechanisms in order to limit decompression sickness. These models are statistically assessed by DCS cases, and, over time, have gradually included bubble formation biophysics. This paper proposes to review this evolution and discuss its limitations. This review is organized around the comparison of decompression model biophysical criteria and theoretical foundations. Then, the DCS-predictive capability was analyzed to assess whether it could be improved by combining different approaches. Most of the operational decompression models have a neo-Haldanian form. Nevertheless, bubble modeling has been gaining popularity, and the circulating bubble amount has become a major output. By merging both views, it seems possible to build a relevant global decompression model that intends to simulate bubble production while predicting DCS risks for all types of exposures and decompression profiles. A statistical approach combining both DCS and bubble detection databases has to be developed to calibrate a global decompression model. Doppler ultrasound and DCS data are essential: i. to make correlation and validation phases reliable; ii. to adjust biophysical criteria to fit at best the observed bubble kinetics; and iii. to build a relevant risk function.

Modeling study of deposition locations in the 291-Z plenum

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

Mahoney, L.A.; Glissmeyer, J.A.

The TEMPEST (Trent and Eyler 1991) and PART5 computer codes were used to predict the probable locations of particle deposition in the suction-side plenum of the 291-Z building in the 200 Area of the Hanford Site, the exhaust fan building for the 234-5Z, 236-Z, and 232-Z buildings in the 200 Area of the Hanford Site. The Tempest code provided velocity fields for the airflow through the plenum. These velocity fields were then used with TEMPEST to provide modeling of near-floor particle concentrations without particle sticking (100% resuspension). The same velocity fields were also used with PART5 to provide modeling ofmore » particle deposition with sticking (0% resuspension). Some of the parameters whose importance was tested were particle size, point of injection and exhaust fan configuration.« less

Dam-Break Flooding and Structural Damage in a Residential Neighborhood: Performance of a coupled hydrodynamic-damage model

NASA Astrophysics Data System (ADS)

Sanders, B. F.; Gallegos, H. A.; Schubert, J. E.

2011-12-01

The Baldwin Hills dam-break flood and associated structural damage is investigated in this study. The flood caused high velocity flows exceeding 5 m/s which destroyed 41 wood-framed residential structures, 16 of which were completed washed out. Damage is predicted by coupling a calibrated hydrodynamic flood model based on the shallow-water equations to structural damage models. The hydrodynamic and damage models are two-way coupled so building failure is predicted upon exceedance of a hydraulic intensity parameter, which in turn triggers a localized reduction in flow resistance which affects flood intensity predictions. Several established damage models and damage correlations reported in the literature are tested to evaluate the predictive skill for two damage states defined by destruction (Level 2) and washout (Level 3). Results show that high-velocity structural damage can be predicted with a remarkable level of skill using established damage models, but only with two-way coupling of the hydrodynamic and damage models. In contrast, when structural failure predictions have no influence on flow predictions, there is a significant reduction in predictive skill. Force-based damage models compare well with a subset of the damage models which were devised for similar types of structures. Implications for emergency planning and preparedness as well as monetary damage estimation are discussed.

Probability models for growth and aflatoxin B1 production as affected by intraspecies variability in Aspergillus flavus.

PubMed

Aldars-García, Laila; Berman, María; Ortiz, Jordi; Ramos, Antonio J; Marín, Sonia

2018-06-01

The probability of growth and aflatoxin B 1 (AFB 1 ) production of 20 isolates of Aspergillus flavus were studied using a full factorial design with eight water activity levels (0.84-0.98 a w ) and six temperature levels (15-40 °C). Binary data obtained from growth studies were modelled using linear logistic regression analysis as a function of temperature, water activity and time for each isolate. In parallel, AFB 1 was extracted at different times from newly formed colonies (up to 20 mm in diameter). Although a total of 950 AFB 1 values over time for all conditions studied were recorded, they were not considered to be enough to build probability models over time, and therefore, only models at 30 days were built. The confidence intervals of the regression coefficients of the probability of growth models showed some differences among the 20 growth models. Further, to assess the growth/no growth and AFB 1 /no- AFB 1 production boundaries, 0.05 and 0.5 probabilities were plotted at 30 days for all of the isolates. The boundaries for growth and AFB 1 showed that, in general, the conditions for growth were wider than those for AFB 1 production. The probability of growth and AFB 1 production seemed to be less variable among isolates than AFB 1 accumulation. Apart from the AFB 1 production probability models, using growth probability models for AFB 1 probability predictions could be, although conservative, a suitable alternative. Predictive mycology should include a number of isolates to generate data to build predictive models and take into account the genetic diversity of the species and thus make predictions as similar as possible to real fungal food contamination. Copyright © 2017 Elsevier Ltd. All rights reserved.

Aftershock collapse vulnerability assessment of reinforced concrete frame structures

USGS Publications Warehouse

Raghunandan, Meera; Liel, Abbie B.; Luco, Nicolas

2015-01-01

In a seismically active region, structures may be subjected to multiple earthquakes, due to mainshock–aftershock phenomena or other sequences, leaving no time for repair or retrofit between the events. This study quantifies the aftershock vulnerability of four modern ductile reinforced concrete (RC) framed buildings in California by conducting incremental dynamic analysis of nonlinear MDOF analytical models. Based on the nonlinear dynamic analysis results, collapse and damage fragility curves are generated for intact and damaged buildings. If the building is not severely damaged in the mainshock, its collapse capacity is unaffected in the aftershock. However, if the building is extensively damaged in the mainshock, there is a significant reduction in its collapse capacity in the aftershock. For example, if an RC frame experiences 4% or more interstory drift in the mainshock, the median capacity to resist aftershock shaking is reduced by about 40%. The study also evaluates the effectiveness of different measures of physical damage observed in the mainshock-damaged buildings for predicting the reduction in collapse capacity of the damaged building in subsequent aftershocks. These physical damage indicators for the building are chosen such that they quantify the qualitative red tagging (unsafe for occupation) criteria employed in post-earthquake evaluation of RC frames. The results indicated that damage indicators related to the drift experienced by the damaged building best predicted the reduced aftershock collapse capacities for these ductile structures.

Modeling Preservice Teachers' TPACK Competencies Based on ICT Usage

ERIC Educational Resources Information Center

Yurdakul, I. Kabakci; Coklar, A. N.

2014-01-01

The purpose of this study was to build a model that predicts the relationships between the Technological Pedagogical Content Knowledge (TPACK) competencies and information and communication technology (ICT) usages. Research data were collected from 3105 Turkish preservice teachers. The TPACK-Deep Scale, ICT usage phase survey and the ICT usage…

Balancing building and maintenance costs in growing transport networks

NASA Astrophysics Data System (ADS)

Bottinelli, Arianna; Louf, Rémi; Gherardi, Marco

2017-09-01

The costs associated to the length of links impose unavoidable constraints to the growth of natural and artificial transport networks. When future network developments cannot be predicted, the costs of building and maintaining connections cannot be minimized simultaneously, requiring competing optimization mechanisms. Here, we study a one-parameter nonequilibrium model driven by an optimization functional, defined as the convex combination of building cost and maintenance cost. By varying the coefficient of the combination, the model interpolates between global and local length minimization, i.e., between minimum spanning trees and a local version known as dynamical minimum spanning trees. We show that cost balance within this ensemble of dynamical networks is a sufficient ingredient for the emergence of tradeoffs between the network's total length and transport efficiency, and of optimal strategies of construction. At the transition between two qualitatively different regimes, the dynamics builds up power-law distributed waiting times between global rearrangements, indicating a point of nonoptimality. Finally, we use our model as a framework to analyze empirical ant trail networks, showing its relevance as a null model for cost-constrained network formation.

Modeling posttraumatic growth among cancer patients: The roles of social support, appraisals, and adaptive coping.

PubMed

Cao, Weidan; Qi, Xiaona; Cai, Deborah A; Han, Xuanye

2018-01-01

The purpose of the study was to build a model to explain the relationships between social support, uncontrollability appraisal, adaptive coping, and posttraumatic growth (PTG) among cancer patients in China. The participants who were cancer patients in a cancer hospital in China filled out a survey. The final sample size was 201. Structural equation modeling was used to build a model explaining PTG. Structural equation modeling results indicated that higher levels of social support predicted higher levels of adaptive coping, higher levels of uncontrollability appraisal predicted lower levels of adaptive coping, and higher levels of adaptive coping predicted higher levels of PTG. Moreover, adaptive coping was a mediator between social support and growth, as well as a mediator between uncontrollability and growth. The direct effects of social support and uncontrollability on PTG were insignificant. The model demonstrated the relationships between social support, uncontrollability appraisal, adaptive coping, and PTG. It could be concluded that uncontrollability appraisal was a required but not sufficient condition for PTG. Neither social support nor uncontrollability appraisal had direct influence on PTG. However, social support and uncontrollability might indirectly influence PTG, through adaptive coping. It implies that both internal factors (eg, cognitive appraisal and coping) and external factors (eg, social support) are required in order for growth to happen. Copyright © 2017 John Wiley & Sons, Ltd.

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

PubMed

Dai, Hanjun; Umarov, Ramzan; Kuwahara, Hiroyuki; Li, Yu; Song, Le; Gao, Xin

2017-11-15

An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. Our program is freely available at https://github.com/ramzan1990/sequence2vec. xin.gao@kaust.edu.sa or lsong@cc.gatech.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method

PubMed Central

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-01-01

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds. PMID:27270206

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method.

PubMed

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-06-08

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.

Web-based remote sensing of building energy performance

NASA Astrophysics Data System (ADS)

Martin, William; Nassiopoulos, Alexandre; Le Cam, Vincent; Kuate, Raphaël; Bourquin, Frédéric

2013-04-01

The present paper describes the design and the deployment of an instrumentation system enabling the energy monitoring of a building in a smart-grid context. The system is based on a network of wireless low power IPv6 sensors. Ambient temperature and electrical power for heating are measured. The management, storage, visualisation and treatment of the data is done through a web-based application that can be deployed as an online web service. The same web-based framework enables the acquisition of distant measured data such as those coming from a nearby weather station. On-site sensor and weather station data are then adequately treated based on inverse identification methods. The algorithms aim at determining the parameters of a numerical model suitable for a short-time horizon prediction of indoor climate. The model is based on standard multi-zone modelling assumptions and takes into account solar, airflow and conductive transfers. It was specially designed to render accurately inertia effects that are used in a demand-response strategy. All the hardware or software technologies that are used in the system are open and low cost so that they comply with the constraints of on-site deployment in buildings. The measured data as well as the model predictions can be accessed ubiquously through the web. This feature enables to consider a wide range of energy management applications at the disctrict, city or national level. The entire system has been deployed and tested in an experimental office building in Angers, France. It demonstrates the potential of ICT technologies to enable remotely controlled monitoring and surveillance in real time.

Base Flow Model Validation

NASA Technical Reports Server (NTRS)

Sinha, Neeraj; Brinckman, Kevin; Jansen, Bernard; Seiner, John

2011-01-01

A method was developed of obtaining propulsive base flow data in both hot and cold jet environments, at Mach numbers and altitude of relevance to NASA launcher designs. The base flow data was used to perform computational fluid dynamics (CFD) turbulence model assessments of base flow predictive capabilities in order to provide increased confidence in base thermal and pressure load predictions obtained from computational modeling efforts. Predictive CFD analyses were used in the design of the experiments, available propulsive models were used to reduce program costs and increase success, and a wind tunnel facility was used. The data obtained allowed assessment of CFD/turbulence models in a complex flow environment, working within a building-block procedure to validation, where cold, non-reacting test data was first used for validation, followed by more complex reacting base flow validation.

What is Neptune's D/H ratio really telling us about its water abundance?

NASA Astrophysics Data System (ADS)

Ali-Dib, Mohamad; Lakhlani, Gunjan

2018-05-01

We investigate the deep-water abundance of Neptune using a simple two-component (core + envelope) toy model. The free parameters of the model are the total mass of heavy elements in the planet (Z), the mass fraction of Z in the envelope (fenv), and the D/H ratio of the accreted building blocks (D/Hbuild).We systematically search the allowed parameter space on a grid and constrain it using Neptune's bulk carbon abundance, D/H ratio, and interior structure models. Assuming solar C/O ratio and cometary D/H for the accreted building blocks are forming the planet, we can fit all of the constraints if less than ˜15 per cent of Z is in the envelope (f_{env}^{median} ˜ 7 per cent), and the rest is locked in a solid core. This model predicts a maximum bulk oxygen abundance in Neptune of 65× solar value. If we assume a C/O of 0.17, corresponding to clathrate-hydrates building blocks, we predict a maximum oxygen abundance of 200× solar value with a median value of ˜140. Thus, both cases lead to oxygen abundance significantly lower than the preferred value of Cavalié et al. (˜540× solar), inferred from model-dependent deep CO observations. Such high-water abundances are excluded by our simple but robust model. We attribute this discrepancy to our imperfect understanding of either the interior structure of Neptune or the chemistry of the primordial protosolar nebula.

Automated Predictive Big Data Analytics Using Ontology Based Semantics.

PubMed

Nural, Mustafa V; Cotterell, Michael E; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A

2015-10-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology.

Automated Predictive Big Data Analytics Using Ontology Based Semantics

PubMed Central

Nural, Mustafa V.; Cotterell, Michael E.; Peng, Hao; Xie, Rui; Ma, Ping; Miller, John A.

2017-01-01

Predictive analytics in the big data era is taking on an ever increasingly important role. Issues related to choice on modeling technique, estimation procedure (or algorithm) and efficient execution can present significant challenges. For example, selection of appropriate and optimal models for big data analytics often requires careful investigation and considerable expertise which might not always be readily available. In this paper, we propose to use semantic technology to assist data analysts and data scientists in selecting appropriate modeling techniques and building specific models as well as the rationale for the techniques and models selected. To formally describe the modeling techniques, models and results, we developed the Analytics Ontology that supports inferencing for semi-automated model selection. The SCALATION framework, which currently supports over thirty modeling techniques for predictive big data analytics is used as a testbed for evaluating the use of semantic technology. PMID:29657954

Dust in the wind: challenges for urban aerodynamics

NASA Astrophysics Data System (ADS)

Boris, Jay P.

2007-04-01

The fluid dynamics of airflow through a city controls the transport and dispersion of airborne contaminants. This is urban aerodynamics, not meteorology. The average flow, large-scale fluctuations and turbulence are closely coupled to the building geometry. Buildings create large "rooster-tail" wakes; there are systematic fountain flows up the backs of tall buildings; and dust in the wind can move perpendicular to or even against the locally prevailing wind. Requirements for better prediction accuracy demand time-dependent, three-dimensional CFD computations that include solar heating and buoyancy, complete landscape and building geometry specification including foliage and, realistic wind fluctuations. This fundamental prediction capability is necessary to assess urban visibility and line-of-sight sensor performance in street canyons and rugged terrain. Computing urban aerodynamics accurately is clearly a time-dependent High Performance Computing (HPC) problem. In an emergency, on the other hand, prediction technology to assess crisis information, sensor performance, and obscured line-of-sight propagation in the face of industrial spills, transportation accidents, or terrorist attacks has very tight time requirements that suggest simple approximations which tend to produce inaccurate results. In the past we have had to choose one or the other: a fast, inaccurate model or a slow accurate model. Using new fluid-dynamic principles, an urban-oriented emergency assessment system called CT-Analyst® was invented that solves this dilemma. It produces HPC-quality results for airborne contaminant scenarios nearly instantly and has unique new capabilities suited to sensor optimization. This presentation treats the design and use of CT-Analyst and discusses the developments needed for widespread use with advanced sensor and communication systems.

Wind-induced contaminant transport in near-surface soils with application to radon entry into buildings

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

Riley, William Jowett

1996-05-01

Indoor air exposures to gaseous contaminants originating in soil can cause large human health risks. To predict and control these exposures, the mechanisms that affect vapor transport in near-surface soils need to be understood. In particular, radon exposure is a concern since average indoor radon concentrations lead to much higher risks than are generally accepted for exposure to other environmental contaminants. This dissertation examines an important component of the indoor radon problem: the impacts of wind on soil-gas and radon transport and entry into buildings. The research includes experimental and modeling studies of wind`s interactions with a building`s superstructure andmore » the resulting soil-gas and radon flows in the surrounding soil. In addition to exploring the effects of steady winds, a novel modeling technique is developed to examine the impacts of fluctuating winds on soil-gas and radon transport.« less

Ocean acidification affects coral growth by reducing skeletal density.

PubMed

Mollica, Nathaniel R; Guo, Weifu; Cohen, Anne L; Huang, Kuo-Fang; Foster, Gavin L; Donald, Hannah K; Solow, Andrew R

2018-02-20

Ocean acidification (OA) is considered an important threat to coral reef ecosystems, because it reduces the availability of carbonate ions that reef-building corals need to produce their skeletons. However, while theory predicts that coral calcification rates decline as carbonate ion concentrations decrease, this prediction is not consistently borne out in laboratory manipulation experiments or in studies of corals inhabiting naturally low-pH reefs today. The skeletal growth of corals consists of two distinct processes: extension (upward growth) and densification (lateral thickening). Here, we show that skeletal density is directly sensitive to changes in seawater carbonate ion concentration and thus, to OA, whereas extension is not. We present a numerical model of Porites skeletal growth that links skeletal density with the external seawater environment via its influence on the chemistry of coral calcifying fluid. We validate the model using existing coral skeletal datasets from six Porites species collected across five reef sites and use this framework to project the impact of 21st century OA on Porites skeletal density across the global tropics. Our model predicts that OA alone will drive up to 20.3 ± 5.4% decline in the skeletal density of reef-building Porites corals.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.

PubMed

Henriques, David; Villaverde, Alejandro F; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R

2017-02-01

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

PubMed Central

Henriques, David; Villaverde, Alejandro F.; Banga, Julio R.

2017-01-01

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM’s ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge. PMID:28166222

Experimental Demonstration of Frequency Regulation by Commercial Buildings – Part II: Results and Performance Evaluation

DOE PAGES

Vrettos, Evangelos; Kara, Emre Can; MacDonald, Jason; ...

2016-11-15

This paper is the second part of a two-part series presenting the results from an experimental demonstration of frequency regulation in a commercial building test facility. We developed relevant building models and designed a hierarchical controller for reserve scheduling, building climate control and frequency regulation in Part I. In Part II, we introduce the communication architecture and experiment settings, and present extensive experimental results under frequency regulation. More specifically, we compute the day-ahead reserve capacity of the test facility under different assumptions and conditions. Furthermore, we demonstrate the ability of model predictive control to satisfy comfort constraints under frequency regulation,more » and show that fan speed control can track the fast-moving RegD signal of the Pennsylvania, Jersey, and Maryland Power Market (PJM) very accurately. In addition, we discuss potential effects of frequency regulation on building operation (e.g., increase in energy consumption, oscillations in supply air temperature, and effect on chiller cycling), and provide suggestions for real-world implementation projects. Our results show that hierarchical control is appropriate for frequency regulation from commercial buildings.« less

Extending the Derek-Meteor Workflow to Predict Chemical-Toxicity Space Impacted by Metabolism: Application to ToxCast and Tox21 Chemical Inventories

EPA Science Inventory

A central aim of EPA’s ToxCast project is to use in vitro high-throughput screening (HTS) profiles to build predictive models of in vivo toxicity. Where assays lack metabolic capability, such efforts may need to anticipate the role of metabolic activation (or deactivation). A wo...

Predicting the Texas Windstorm Insurance Association claim payout of commercial buildings from Hurricane Ike

NASA Astrophysics Data System (ADS)

Kim, J. M.; Woods, P. K.; Park, Y. J.; Son, K.

2013-08-01

Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Analysis of Aurora's Performance Simulation Engine for Three Systems

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

Freeman, Janine; Simon, Joseph

2015-07-07

Aurora Solar Inc. is building a cloud-based optimization platform to automate the design, engineering, and permit generation process of solar photovoltaic (PV) installations. They requested that the National Renewable Energy Laboratory (NREL) validate the performance of the PV system performance simulation engine of Aurora Solar’s solar design platform, Aurora. In previous work, NREL performed a validation of multiple other PV modeling tools 1, so this study builds upon that work by examining all of the same fixed-tilt systems with available module datasheets that NREL selected and used in the aforementioned study. Aurora Solar set up these three operating PV systemsmore » in their modeling platform using NREL-provided system specifications and concurrent weather data. NREL then verified the setup of these systems, ran the simulations, and compared the Aurora-predicted performance data to measured performance data for those three systems, as well as to performance data predicted by other PV modeling tools.« less

Economic analysis of model validation for a challenge problem

DOE PAGES

Paez, Paul J.; Paez, Thomas L.; Hasselman, Timothy K.

2016-02-19

It is now commonplace for engineers to build mathematical models of the systems they are designing, building, or testing. And, it is nearly universally accepted that phenomenological models of physical systems must be validated prior to use for prediction in consequential scenarios. Yet, there are certain situations in which testing only or no testing and no modeling may be economically viable alternatives to modeling and its associated testing. This paper develops an economic framework within which benefit–cost can be evaluated for modeling and model validation relative to other options. The development is presented in terms of a challenge problem. Asmore » a result, we provide a numerical example that quantifies when modeling, calibration, and validation yield higher benefit–cost than a testing only or no modeling and no testing option.« less

On Winning the Race for Predicting the Indian Summer Monsoon Rainfall

NASA Astrophysics Data System (ADS)

Goswami, Bhupendra

2013-03-01

Skillful prediction of Indian summer monsoon rainfall (ISMR) one season in advance remains a ``grand challenge'' for the climate science community even though such forecasts have tremendous socio-economic implications over the region. Continued poor skill of the ocean-atmosphere coupled models in predicting ISMR is an enigma in the backdrop when these models have high skill in predicting seasonal mean rainfall over the rest of the Tropics. Here, I provide an overview of the fundamental processes responsible for limited skill of climate models and outline a framework for achieving the limit on potential predictability within a reasonable time frame. I also show that monsoon intra-seasonal oscillations (MISO) act as building blocks of the Asian monsoon and provide a bridge between the two problems, the potential predictability limit and the simulation of seasonal mean climate. The correlation between observed ISMR and ensemble mean of predicted ISMR (R) can still be used as a metric for forecast verification. Estimate of potential limit of predictability of Asian monsoon indicates that the highest achievable R is about 0.75. Improvements in climate models and data assimilation over the past one decade has slowly improved R from near zero a decade ago to about 0.4 currently. The race for achieving useful prediction can be won, if we can push this skill up to about 0.7. It requires focused research in improving simulations of MISO, monsoon seasonal cycle and ENSO-monsoon relationship by the climate models. In order to achieve this goal by 2015-16 timeframe, IITM is leading a Program called Monsoon Mission supported by the Ministry of Earth Sciences, Govt. of India (MoES). As improvement in skill of forecasts can come only if R & D is carried out on an operational modeling system, the Climate Forecast System of National Centre for Environmental Prediction (NCEP) of NOAA, U.S.A has been selected as our base system. The Mission envisages building partnership between operational forecasting agency and National and International R & D Organizations to work on improving modeling system. MoES has provided substantial funding to the Mission to fund proposals from International R & D Organizations to work with Indian Organizations in this Mission to achieve this goal. The conceptual framework and the roadmap for the Mission will be highlighted. Indian Institute of Tropical Meteorology is funded by Ministry of Earth Sciences, Govt. of India.

Method development of damage detection in asymmetric buildings

NASA Astrophysics Data System (ADS)

Wang, Yi; Thambiratnam, David P.; Chan, Tommy H. T.; Nguyen, Andy

2018-01-01

Aesthetics and functionality requirements have caused most buildings to be asymmetric in recent times. Such buildings exhibit complex vibration characteristics under dynamic loads as there is coupling between the lateral and torsional components of vibration, and are referred to as torsionally coupled buildings. These buildings require three dimensional modelling and analysis. In spite of much recent research and some successful applications of vibration based damage detection methods to civil structures in recent years, the applications to asymmetric buildings has been a challenging task for structural engineers. There has been relatively little research on detecting and locating damage specific to torsionally coupled asymmetric buildings. This paper aims to compare the difference in vibration behaviour between symmetric and asymmetric buildings and then use the vibration characteristics for predicting damage in them. The need for developing a special method to detect damage in asymmetric buildings thus becomes evident. Towards this end, this paper modifies the traditional modal strain energy based damage index by decomposing the mode shapes into their lateral and vertical components and to form component specific damage indices. The improved approach is then developed by combining the modified strain energy based damage indices with the modal flexibility method which was modified to suit three dimensional structures to form a new damage indicator. The procedure is illustrated through numerical studies conducted on three dimensional five-story symmetric and asymmetric frame structures with the same layout, after validating the modelling techniques through experimental testing of a laboratory scale asymmetric building model. Vibration parameters obtained from finite element analysis of the intact and damaged building models are then applied into the proposed algorithms for detecting and locating the single and multiple damages in these buildings. The results obtained from a number of different damage scenarios confirm the feasibility of the proposed vibration based damage detection method for three dimensional asymmetric buildings.

Machine Learning Predictions of a Multiresolution Climate Model Ensemble

NASA Astrophysics Data System (ADS)

Anderson, Gemma J.; Lucas, Donald D.

2018-05-01

Statistical models of high-resolution climate models are useful for many purposes, including sensitivity and uncertainty analyses, but building them can be computationally prohibitive. We generated a unique multiresolution perturbed parameter ensemble of a global climate model. We use a novel application of a machine learning technique known as random forests to train a statistical model on the ensemble to make high-resolution model predictions of two important quantities: global mean top-of-atmosphere energy flux and precipitation. The random forests leverage cheaper low-resolution simulations, greatly reducing the number of high-resolution simulations required to train the statistical model. We demonstrate that high-resolution predictions of these quantities can be obtained by training on an ensemble that includes only a small number of high-resolution simulations. We also find that global annually averaged precipitation is more sensitive to resolution changes than to any of the model parameters considered.

Estimating the circuit delay of FPGA with a transfer learning method

NASA Astrophysics Data System (ADS)

Cui, Xiuhai; Liu, Datong; Peng, Yu; Peng, Xiyuan

2017-10-01

With the increase of FPGA (Field Programmable Gate Array, FPGA) functionality, FPGA has become an on-chip system platform. Due to increase the complexity of FPGA, estimating the delay of FPGA is a very challenge work. To solve the problems, we propose a transfer learning estimation delay (TLED) method to simplify the delay estimation of different speed grade FPGA. In fact, the same style different speed grade FPGA comes from the same process and layout. The delay has some correlation among different speed grade FPGA. Therefore, one kind of speed grade FPGA is chosen as a basic training sample in this paper. Other training samples of different speed grade can get from the basic training samples through of transfer learning. At the same time, we also select a few target FPGA samples as training samples. A general predictive model is trained by these samples. Thus one kind of estimation model is used to estimate different speed grade FPGA circuit delay. The framework of TRED includes three phases: 1) Building a basic circuit delay library which includes multipliers, adders, shifters, and so on. These circuits are used to train and build the predictive model. 2) By contrasting experiments among different algorithms, the forest random algorithm is selected to train predictive model. 3) The target circuit delay is predicted by the predictive model. The Artix-7, Kintex-7, and Virtex-7 are selected to do experiments. Each of them includes -1, -2, -2l, and -3 different speed grade. The experiments show the delay estimation accuracy score is more than 92% with the TLED method. This result shows that the TLED method is a feasible delay assessment method, especially in the high-level synthesis stage of FPGA tool, which is an efficient and effective delay assessment method.

Computational fluid dynamic modeling of the summit of Mt. Hopkins for the MMT Observatory

NASA Astrophysics Data System (ADS)

Callahan, S.

2010-07-01

Over the past three decades, the staff of the MMT observatory used a variety of techniques to predict the summit wind characteristics including wind tunnel modeling and the release of smoke bombs. With the planned addition of a new instrument repair facility to be constructed on the summit of Mt. Hopkins, new computational fluid dynamic (CFD) models were made to determine the building's influence on the thermal environment around the telescope. The models compared the wind profiles and density contours above the telescope enclosure with and without the new building. The results show the steeply-sided Mount Hopkins dominates the summit wind profiles. In typical winds, the height of the telescope remains above the ground layer and is sufficiently separated from the new facility to insure the heat from the new building does not interfere with the telescope. The results also confirmed the observatories waste heat exhaust duct location needs to be relocated to prevent heat from being trapped in the wind shadow of the new building and lofting above the telescope. These useful models provide many insights into understanding the thermal environment of the summit.

Building development and roads: implications for the distribution of stone curlews across the Brecks.

PubMed

Clarke, Ralph T; Liley, Durwyn; Sharp, Joanna M; Green, Rhys E

2013-01-01

Substantial new housing and infrastructure development planned within England has the potential to conflict with the nature conservation interests of protected sites. The Breckland area of eastern England (the Brecks) is designated as a Special Protection Area for a number of bird species, including the stone curlew (for which it holds more than 60% of the UK total population). We explore the effect of buildings and roads on the spatial distribution of stone curlew nests across the Brecks in order to inform strategic development plans to avoid adverse effects on such European protected sites. Using data across all years (and subsets of years) over the period 1988-2006 but restricted to habitat areas of arable land with suitable soils, we assessed nest density in relation to the distances to nearest settlements and to major roads. Measures of the local density of nearby buildings, roads and traffic levels were assessed using normal kernel distance-weighting functions. Quasi-Poisson generalised linear mixed models allowing for spatial auto-correlation were fitted. Significantly lower densities of stone curlew nests were found at distances up to 1500m from settlements, and distances up to 1000m or more from major (trunk) roads. The best fitting models involved optimally distance-weighted variables for the extent of nearby buildings and the trunk road traffic levels. The results and predictions from this study of past data suggests there is cause for concern that future housing development and associated road infrastructure within the Breckland area could have negative impacts on the nesting stone curlew population. Given the strict legal protection afforded to the SPA the planning and conservation bodies have subsequently agreed precautionary restrictions on building development within the distances identified and used the modelling predictions to agree mitigation measures for proposed trunk road developments.

Spacecraft Internal Acoustic Environment Modeling

NASA Technical Reports Server (NTRS)

Chu, S. Reynold; Allen, Chris

2009-01-01

The objective of the project is to develop an acoustic modeling capability, based on commercial off-the-shelf software, to be used as a tool for oversight of the future manned Constellation vehicles. The use of such a model will help ensure compliance with acoustic requirements. Also, this project includes modeling validation and development feedback via building physical mockups and conducting acoustic measurements to compare with the predictions.

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

PubMed

Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch

2017-06-06

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

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

PubMed

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

2018-02-01

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

Experimental validation of finite element model analysis of a steel frame in simulated post-earthquake fire environments

NASA Astrophysics Data System (ADS)

Huang, Ying; Bevans, W. J.; Xiao, Hai; Zhou, Zhi; Chen, Genda

2012-04-01

During or after an earthquake event, building system often experiences large strains due to shaking effects as observed during recent earthquakes, causing permanent inelastic deformation. In addition to the inelastic deformation induced by the earthquake effect, the post-earthquake fires associated with short fuse of electrical systems and leakage of gas devices can further strain the already damaged structures during the earthquakes, potentially leading to a progressive collapse of buildings. Under these harsh environments, measurements on the involved building by various sensors could only provide limited structural health information. Finite element model analysis, on the other hand, if validated by predesigned experiments, can provide detail structural behavior information of the entire structures. In this paper, a temperature dependent nonlinear 3-D finite element model (FEM) of a one-story steel frame is set up by ABAQUS based on the cited material property of steel from EN 1993-1.2 and AISC manuals. The FEM is validated by testing the modeled steel frame in simulated post-earthquake environments. Comparisons between the FEM analysis and the experimental results show that the FEM predicts the structural behavior of the steel frame in post-earthquake fire conditions reasonably. With experimental validations, the FEM analysis of critical structures could be continuously predicted for structures in these harsh environments for a better assistant to fire fighters in their rescue efforts and save fire victims.

A new flexible plug and play scheme for modeling, simulating, and predicting gastric emptying

PubMed Central

2014-01-01

Background In-silico models that attempt to capture and describe the physiological behavior of biological organisms, including humans, are intrinsically complex and time consuming to build and simulate in a computing environment. The level of detail of description incorporated in the model depends on the knowledge of the system’s behavior at that level. This knowledge is gathered from the literature and/or improved by knowledge obtained from new experiments. Thus model development is an iterative developmental procedure. The objective of this paper is to describe a new plug and play scheme that offers increased flexibility and ease-of-use for modeling and simulating physiological behavior of biological organisms. Methods This scheme requires the modeler (user) first to supply the structure of the interacting components and experimental data in a tabular format. The behavior of the components described in a mathematical form, also provided by the modeler, is externally linked during simulation. The advantage of the plug and play scheme for modeling is that it requires less programming effort and can be quickly adapted to newer modeling requirements while also paving the way for dynamic model building. Results As an illustration, the paper models the dynamics of gastric emptying behavior experienced by humans. The flexibility to adapt the model to predict the gastric emptying behavior under varying types of nutrient infusion in the intestine (ileum) is demonstrated. The predictions were verified with a human intervention study. The error in predicting the half emptying time was found to be less than 6%. Conclusions A new plug-and-play scheme for biological systems modeling was developed that allows changes to the modeled structure and behavior with reduced programming effort, by abstracting the biological system into a network of smaller sub-systems with independent behavior. In the new scheme, the modeling and simulation becomes an automatic machine readable and executable task. PMID:24917054

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

PubMed

Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-25

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Genomic selection accuracies within and between environments and small breeding groups in white spruce.

PubMed

Beaulieu, Jean; Doerksen, Trevor K; MacKay, John; Rainville, André; Bousquet, Jean

2014-12-02

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested. Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies. Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

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

NASA Astrophysics Data System (ADS)

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.

Indoor Multi-Sensor Acquisition System for Projects on Energy Renovation of Buildings.

PubMed

Armesto, Julia; Sánchez-Villanueva, Claudio; Patiño-Cambeiro, Faustino; Patiño-Barbeito, Faustino

2016-05-28

Energy rehabilitation actions in buildings have become a great economic opportunity for the construction sector. They also constitute a strategic goal in the European Union (EU), given the energy dependence and the compromises with climate change of its member states. About 75% of existing buildings in the EU were built when energy efficiency codes had not been developed. Approximately 75% to 90% of those standing buildings are expected to remain in use in 2050. Significant advances have been achieved in energy analysis, simulation tools, and computer fluid dynamics for building energy evaluation. However, the gap between predictions and real savings might still be improved. Geomatics and computer science disciplines can really help in modelling, inspection, and diagnosis procedures. This paper presents a multi-sensor acquisition system capable of automatically and simultaneously capturing the three-dimensional geometric information, thermographic, optical, and panoramic images, ambient temperature map, relative humidity map, and light level map. The system integrates a navigation system based on a Simultaneous Localization and Mapping (SLAM) approach that allows georeferencing every data to its position in the building. The described equipment optimizes the energy inspection and diagnosis steps and facilitates the energy modelling of the building.

Indoor Multi-Sensor Acquisition System for Projects on Energy Renovation of Buildings

PubMed Central

Armesto, Julia; Sánchez-Villanueva, Claudio; Patiño-Cambeiro, Faustino; Patiño-Barbeito, Faustino

2016-01-01

Energy rehabilitation actions in buildings have become a great economic opportunity for the construction sector. They also constitute a strategic goal in the European Union (EU), given the energy dependence and the compromises with climate change of its member states. About 75% of existing buildings in the EU were built when energy efficiency codes had not been developed. Approximately 75% to 90% of those standing buildings are expected to remain in use in 2050. Significant advances have been achieved in energy analysis, simulation tools, and computer fluid dynamics for building energy evaluation. However, the gap between predictions and real savings might still be improved. Geomatics and computer science disciplines can really help in modelling, inspection, and diagnosis procedures. This paper presents a multi-sensor acquisition system capable of automatically and simultaneously capturing the three-dimensional geometric information, thermographic, optical, and panoramic images, ambient temperature map, relative humidity map, and light level map. The system integrates a navigation system based on a Simultaneous Localization and Mapping (SLAM) approach that allows georeferencing every data to its position in the building. The described equipment optimizes the energy inspection and diagnosis steps and facilitates the energy modelling of the building. PMID:27240379

Building energy simulation in real time through an open standard interface

DOE PAGES

Pang, Xiufeng; Nouidui, Thierry S.; Wetter, Michael; ...

2015-10-20

Building energy models (BEMs) are typically used for design and code compliance for new buildings and in the renovation of existing buildings to predict energy use. We present the increasing adoption of BEM as standard practice in the building industry presents an opportunity to extend the use of BEMs into construction, commissioning and operation. In 2009, the authors developed a real-time simulation framework to execute an EnergyPlus model in real time to improve building operation. This paper reports an enhancement of that real-time energy simulation framework. The previous version only works with software tools that implement the custom co-simulation interfacemore » of the Building Controls Virtual Test Bed (BCVTB), such as EnergyPlus, Dymola and TRNSYS. The new version uses an open standard interface, the Functional Mockup Interface (FMI), to provide a generic interface to any application that supports the FMI protocol. In addition, the new version utilizes the Simple Measurement and Actuation Profile (sMAP) tool as the data acquisition system to acquire, store and present data. Lastly, this paper introduces the updated architecture of the real-time simulation framework using FMI and presents proof-of-concept demonstration results which validate the new framework.« less

Neutrino Telescopes

NASA Astrophysics Data System (ADS)

de Marzo, C. N.

2002-06-01

Neutrino astronomy is one of the frontier of the high energy astrophysics. I discuss how to build a neutrino telescope and which requirements such a detector must fulfil. A measurable flux of astrophysical neutrinos is predicted by several models for a detector at the cubic kilometer scale. The way pursued until now in building such huge apparatuses is Cherenkov light detection in water or in ice. There have been attempts to build neutrino telescopes and also some projects are yet under construction or under way to start. This situation is reviewed and also techniques alternatives to the Cherenkov light detection are mentioned.

The Anatomy of a Likely Donor: Econometric Evidence on Philanthropy to Higher Education

ERIC Educational Resources Information Center

Lara, Christen; Johnson, Daniel

2014-01-01

In 2011, philanthropic giving to higher education institutions totaled $30.3 billion, an 8.2% increase over the previous year. Roughly, 26% of those funds came from alumni donations. This article builds upon existing economic models to create an econometric model to explain and predict the pattern of alumni giving. We test the model using data…

The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series

NASA Astrophysics Data System (ADS)

Du, Kongchang; Zhao, Ying; Lei, Jiaqiang

2017-09-01

In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.

Testing an algebraic model of self-reflexion.

PubMed

Grice, James W; McDaniel, Brenda L; Thompsen, Dana

2005-06-01

Self-reflexion is the conscious process of taking the position of an observer in relation to one's own thoughts, feelings, and experiences. Building on the work of Lefebvre, Lefebvre, and Adams-Webber, we used a formal algebraic model of self-reflexion to derive several predictions regarding the frequencies with which individuals would rate themselves and others positively on bipolar scales anchored by adjective terms. The current results from 108 participants (41 men, 67 women; M age= 20.2 yr.) confirmed two predictions derived from the model. Three other predictions, however, were not supported even though the observed frequencies were close to the predicted values. Although not as promising as results reported by Lefebvre, et al., these mixed findings were interpreted as encouraging support for the validity of Lefebvre's algebraic model of self-reflexion. Differences between the current methods and those from previous investigations were also examined, and methodological implications for further studies were discussed.

Pesticide Environmental Fate Research for the 21st Century: Building Bridges Between Laboratory and Field Studies at Varying Scales

USDA-ARS?s Scientific Manuscript database

Accurate determination of predicted environmental concentrations (PECs) is a continuing and often elusive goal of pesticide risk assessment. PECs are typically derived using simulation models that depend on laboratory generated data for key input parameters (t1/2, Koc, etc.). Model flexibility in ...

Pesticide Environmental Fate Research for the 21st Century: Building Bridges Between Laboratory and Field Studies at Varying Scales

USDA-ARS?s Scientific Manuscript database

Accurate determination of predicted environmental concentrations (PECs) is a continuing and often elusive goal of pesticide risk assessment. PECs are typically derived using simulation models that depend on laboratory generated data for key input parameters (t1/2, Koc, etc.). Model flexibility in ev...

Using Indigenous Materials for Construction

DTIC Science & Technology

2015-07-01

Theoretical models were devised for prediction of the structural attributes of indigenous ferrocement sheets and sandwich composite panels comprising the...indigenous ferrocement skins and aerated concrete core. Structural designs were developed for these indigenous sandwich composite panels in typical...indigenous materials and building systems developed in the project were evaluated. Numerical modeling capabilities were developed for structural

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations

PubMed Central

Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge. PMID:27313604

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations.

PubMed

Castelli, Mauro; Manzoni, Luca; Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge.

Lessons learned from recent geomagnetic disturbance model validation activities

NASA Astrophysics Data System (ADS)

Pulkkinen, A. A.; Welling, D. T.

2017-12-01

Due to concerns pertaining to geomagnetically induced current impact on ground-based infrastructure, there has been significantly elevated interest in applying models for local geomagnetic disturbance or "delta-B" predictions. Correspondingly there has been elevated need for testing the quality of the delta-B predictions generated by the modern empirical and physics-based models. To address this need, community-wide activities were launched under the GEM Challenge framework and one culmination of the activities was the validation and selection of models that were transitioned into operations at NOAA SWPC. The community-wide delta-B action is continued under the CCMC-facilitated International Forum for Space Weather Capabilities Assessment and its "Ground Magnetic Perturbations: dBdt, delta-B, GICs, FACs" working group. The new delta-B working group builds on the past experiences and expands the collaborations to cover the entire international space weather community. In this paper, we discuss the key lessons learned from the past delta-B validation exercises and lay out the path forward for building on those experience under the new delta-B working group.

Modeling indoor particulate exposures in inner city school classrooms

PubMed Central

Gaffin, Jonathan M.; Petty, Carter R.; Hauptman, Marissa; Kang, Choong-Min; Wolfson, Jack M.; Awad, Yara Abu; Di, Qian; Lai, Peggy S.; Sheehan, William J.; Baxi, Sachin; Coull, Brent A.; Schwartz, Joel D.; Gold, Diane R.; Koutrakis, Petros; Phipatanakul, Wanda

2016-01-01

Outdoor air pollution penetrates buildings and contributes to total indoor exposures. We investigated the relationship of indoor to outdoor particulate matter in inner-city school classrooms. The School Inner City Asthma Study investigates the effect of classroom-based environmental exposures on students with asthma in the northeast United States. Mixed-effects linear models were used to determine the relationships between indoor PM2.5 and BC and their corresponding outdoor concentrations, and to develop a model for predicting exposures to these pollutants. The indoor-outdoor sulfur ratio was used as an infiltration factor of outdoor fine particles. Weeklong concentrations of PM2.5 and BC in 199 samples from 136 classrooms (30 school buildings) were compared to those measured at a central monitoring site averaged over the same timeframe. Mixed effects regression models found significant random intercept and slope effects, which indicate that: 1) there are important PM2.5 sources in classrooms; 2) the penetration of outdoor PM2.5 particles varies by school, and 3) the site-specific outside PM2.5 levels (inferred by the models) differ from those observed at the central monitor site. Similar results were found for BC except for lack of indoor sources. The fitted predictions from the sulfur-adjusted models were moderately predictive of observed indoor pollutant levels (Out of sample correlations: PM2.5: r2 = 0.68, BC; r2 = 0.61). Our results suggest that PM2.5 has important classroom sources, which vary by school. Furthermore, using these mixed effects models, classroom exposures can be accurately predicted for dates when central site measures are available but indoor measures are not available. PMID:27599884

Evaluation of the Revised Lagrangian Particle Model GRAL Against Wind-Tunnel and Field Observations in the Presence of Obstacles

NASA Astrophysics Data System (ADS)

Oettl, Dietmar

2015-05-01

A revised microscale flow field model has been implemented in the Lagrangian particle model Graz Lagrangian Model (GRAL) for computing flows around obstacles. It is based on the Reynolds-averaged Navier-Stokes equations in three dimensions and the widely used standard turbulence model. Here we focus on evaluating the model regarding computed concentrations by use of a comprehensive wind-tunnel experiment with numerous combinations of building geometries, stack positions, and locations. In addition, two field experiments carried out in Denmark and in the U.S were used to evaluate the model. Further, two different formulations of the standard deviation of wind component fluctuations have also been investigated, but no clear picture could be drawn in this respect. Overall the model is able to capture several of the main features of pollutant dispersion around obstacles, but at least one future model improvement was identified for stack releases within the recirculation zone of buildings. Regulatory applications are the bread-and-butter of most GRAL users nowadays, requiring fast and robust modelling algorithms. Thus, a few simplifications have been introduced to decrease the computational time required. Although predicted concentrations for the two field experiments were found to be in good agreement with observations, shortcomings were identified regarding the extent of computed recirculation zones for the idealized wind-tunnel building geometries, with approaching flows perpendicular to building faces.

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

PubMed Central

Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad

2017-01-01

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635

The impact of solar radiation on the heating and cooling of buildings

NASA Astrophysics Data System (ADS)

Witmer, Lucas

This work focuses on the impact of solar energy on the heating and cooling of buildings. The sun can be the primary driver for building cooling loads as well as a significant source of heat in the winter. Methods are presented for the calculation of solar energy incident on tilted surfaces and the irradiance data source options. A key deficiency in current building energy modeling softwares is reviewed with a demonstration of the impact of calculating for shade on opaque surfaces. Several tools include methods for calculating shade incident on windows, while none do so automatically for opaque surfaces. The resulting calculations for fully irradiated wall surfaces underestimate building energy consumption in the winter and overestimate in the summer by significant margins. A method has been developed for processing and filtering solar irradiance data based on local shading. This method is used to compare situations where a model predictive control system can make poor decisions for building comfort control. An MPC system informed by poor quality solar data will negatively impact comfort in perimeter building zones during the cooling season. The direct component of irradiance is necessary for the calculation of irradiance on a tilted surface. Using graphical analysis and conditional probability distributions, this work demonstrates a proof of concept for estimating direct normal irradiance from a multi-pyranometer array by leveraging inter-surface relationships without directly inverting a sky model.

USE OF TRACER DATA FROM THE MADISON SQUARE GARDEN 2005 FIELD EXPERIMENT TO TEST A SIMPLE URBAN DISPERSION MODEL

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

Hanna, Steven R.; Baja, Emmanuel; Flaherty, Julia E.

2008-01-30

A simple urban dispersion model is tested that is based on the Gaussian plume model and the Briggs’ urban dispersion curves. A key aspect of the model is that an initial dispersion coefficient (sigma) of 40 m is assumed to apply in the x, y, and z directions in built-up downtown areas. This initial sigma accounts for mixing in the local street canyon and/or building wakes. At short distances (i.e., when the release is in the same street canyon as the receptor and there are no obstructions in between), the initial lateral sigma is assumed to be less, 10 m.more » Observations from tracer experiments during the Madison Square Garden 2005 (MSG05) field study are used for model testing. MSG05 took place in a 1 km by 1 km area in Manhattan surrounding Madison Square Garden. Six different perfluorocarbon tracer (PFT) gases were released concurrently from five different locations around MSG, and concentrations in the air were observed by 20 samplers near the surface and seven samplers on building tops. There were two separate continuous 60 minute tracer release periods on each day, beginning at 9 am and at 11:30 am. Releases took place on two separate days (March 10 and 14). The samplers provided 30 minute averaged PFT concentrations from 9 am through 2 pm. This analysis focuses on the maximum 60-minute averaged PFT gas concentration at each sampler location for each PFT for each release period. Stability was assumed to be nearly neutral, because of the moderate winds and the mechanical mixing generated by the buildings. Input wind direction was the average observed building-top wind direction (285° on March 10 and 315° on March 14). Input wind speed was the average street-level observed wind speed (1.5 m/s for both days). To be considered in the evaluation, both the observed and predicted concentration had to exceed the threshold. Concentrations normalized by source release rate, C/Q, were tested. For all PFTs, samplers, and release times, the median observed and predicted C/Q are within 40% of each other, and 43 % of the time the concentration predictions are within a factor of two of the observations. The scatter plots show that the typical error is about the same magnitude as the mean concentration. When only the surface observations are considered, the performance is better, with the median observed and predicted C/Qs within 10 % of each other. The overall 60 minute-averaged maximum C/Q is underpredicted by about 40 % for the surface samplers and is overpredicted by about 25 % for the building-top samplers.« less

IDC Re-Engineering Phase 2 Iteration E2 Use Case Realizations

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

Harris, James M.; Burns, John F.; Hamlet, Benjamin R.

2016-06-01

This architecturally significant use case describes how the System acquires meteorological data to build atmospheric models used in automatic and interactive processing of infrasound data. The System requests the latest available high-resolution global meteorological data from external data centers and puts it into the correct formats for generation of infrasound propagation models. The system moves the meteorological data from Data Acquisition Partition to the Data Processing Partition and stores the meteorological data. The System builds a new atmospheric model based on the meteorological data. This use case is architecturally significant because it describes acquiring meteorological data from various sources andmore » creating dynamic atmospheric transmission model to support the prediction of infrasonic signal detection« less

Melanoma-specific mortality and competing mortality in patients with non-metastatic malignant melanoma: a population-based analysis.

PubMed

Shen, Weidong; Sakamoto, Naoko; Yang, Limin

2016-07-07

The objectives of this study were to evaluate and model the probability of melanoma-specific death and competing causes of death for patients with melanoma by competing risk analysis, and to build competing risk nomograms to provide individualized and accurate predictive tools. Melanoma data were obtained from the Surveillance Epidemiology and End Results program. All patients diagnosed with primary non-metastatic melanoma during the years 2004-2007 were potentially eligible for inclusion. The cumulative incidence function (CIF) was used to describe the probability of melanoma mortality and competing risk mortality. We used Gray's test to compare differences in CIF between groups. The proportional subdistribution hazard approach by Fine and Gray was used to model CIF. We built competing risk nomograms based on the models that we developed. The 5-year cumulative incidence of melanoma death was 7.1 %, and the cumulative incidence of other causes of death was 7.4 %. We identified that variables associated with an elevated probability of melanoma-specific mortality included older age, male sex, thick melanoma, ulcerated cancer, and positive lymph nodes. The nomograms were well calibrated. C-indexes were 0.85 and 0.83 for nomograms predicting the probability of melanoma mortality and competing risk mortality, which suggests good discriminative ability. This large study cohort enabled us to build a reliable competing risk model and nomogram for predicting melanoma prognosis. Model performance proved to be good. This individualized predictive tool can be used in clinical practice to help treatment-related decision making.

Risk prediction of Critical Infrastructures against extreme natural hazards: local and regional scale analysis

NASA Astrophysics Data System (ADS)

Rosato, Vittorio; Hounjet, Micheline; Burzel, Andreas; Di Pietro, Antonio; Tofani, Alberto; Pollino, Maurizio; Giovinazzi, Sonia

2016-04-01

Natural hazard events can induce severe impacts on the built environment; they can hit wide and densely populated areas, where there is a large number of (inter)dependent technological systems whose damages could cause the failure or malfunctioning of further different services, spreading the impacts on wider geographical areas. The EU project CIPRNet (Critical Infrastructures Preparedness and Resilience Research Network) is realizing an unprecedented Decision Support System (DSS) which enables to operationally perform risk prediction on Critical Infrastructures (CI) by predicting the occurrence of natural events (from long term weather to short nowcast predictions, correlating intrinsic vulnerabilities of CI elements with the different events' manifestation strengths, and analysing the resulting Damage Scenario. The Damage Scenario is then transformed into an Impact Scenario, where punctual CI element damages are transformed into micro (local area) or meso (regional) scale Services Outages. At the smaller scale, the DSS simulates detailed city models (where CI dependencies are explicitly accounted for) that are of important input for crisis management organizations whereas, at the regional scale by using approximate System-of-Systems model describing systemic interactions, the focus is on raising awareness. The DSS has allowed to develop a novel simulation framework for predicting earthquakes shake maps originating from a given seismic event, considering the shock wave propagation in inhomogeneous media and the subsequent produced damages by estimating building vulnerabilities on the basis of a phenomenological model [1, 2]. Moreover, in presence of areas containing river basins, when abundant precipitations are expected, the DSS solves the hydrodynamic 1D/2D models of the river basins for predicting the flux runoff and the corresponding flood dynamics. This calculation allows the estimation of the Damage Scenario and triggers the evaluation of the Impact Scenario. The regional output of cascading effects can be used as an input model for more detailed analyses within urban areas for instance. The DSS weights the overall expected Crisis Scenario by also considering, through an appropriate Consequences Analysis, the number of citizens affected by the Service(s) outages, the expected economic losses of the major industrial activities hit by the unavailability of relevant Services (electricity, water, telecommunications etc.) and the influence of outages of the availability of Public Services (hospitals, schools, public offices etc.) [1] S.Giovinazzi, S. Lagomarsino: A macroseismic method for the vulnerability assessment of buildings. 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada (2004) [2] S. Lagomarsino, S.Giovinazzi: Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings. Bull Earthquake Eng., 4:415-443 (2006)

Integrated analysis of numerical weather prediction and computational fluid dynamics for estimating cross-ventilation effects on inhaled air quality inside a factory

NASA Astrophysics Data System (ADS)

Murga, Alicia; Sano, Yusuke; Kawamoto, Yoichi; Ito, Kazuhide

2017-10-01

Mechanical and passive ventilation strategies directly impact indoor air quality. Passive ventilation has recently become widespread owing to its ability to reduce energy demand in buildings, such as the case of natural or cross ventilation. To understand the effect of natural ventilation on indoor environmental quality, outdoor-indoor flow paths need to be analyzed as functions of urban atmospheric conditions, topology of the built environment, and indoor conditions. Wind-driven natural ventilation (e.g., cross ventilation) can be calculated through the wind pressure coefficient distributions of outdoor wall surfaces and openings of a building, allowing the study of indoor air parameters and airborne contaminant concentrations. Variations in outside parameters will directly impact indoor air quality and residents' health. Numerical modeling can contribute to comprehend these various parameters because it allows full control of boundary conditions and sampling points. In this study, numerical weather prediction modeling was used to calculate wind profiles/distributions at the atmospheric scale, and computational fluid dynamics was used to model detailed urban and indoor flows, which were then integrated into a dynamic downscaling analysis to predict specific urban wind parameters from the atmospheric to built-environment scale. Wind velocity and contaminant concentration distributions inside a factory building were analyzed to assess the quality of the human working environment by using a computer simulated person. The impact of cross ventilation flows and its variations on local average contaminant concentration around a factory worker, and inhaled contaminant dose, were then discussed.

A comparison of wake characteristics of model and prototype buildings in transverse winds

NASA Technical Reports Server (NTRS)

Logan, E., Jr.; Phataraphruk, P.; Chang, J.

1978-01-01

Previously measured mean velocity and turbulence intensity profiles in the wake of a 26.8-m long building 3.2 m high and transverse to the wind direction in an atmospheric boundary layer several hundred meters thick were compared with profiles at corresponding stations downstream of a 1/50-scale model on the floor of a large meteorological wind tunnel in a boundary layer 0.61 m in thickness. The validity of using model wake data to predict full scale data was determined. Preliminary results are presented which indicate that disparities result from differences in relative depth of logarithmic layers, surface roughness, and the proximity of upstream obstacles.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

Window Area and Development Drive Spatial Variation in Bird-Window Collisions in an Urban Landscape

PubMed Central

Hager, Stephen B.; Cosentino, Bradley J.; McKay, Kelly J.; Monson, Cathleen; Zuurdeeg, Walt; Blevins, Brian

2013-01-01

Collisions with windows are an important human-related threat to birds in urban landscapes. However, the proximate drivers of collisions are not well understood, and no study has examined spatial variation in mortality in an urban setting. We hypothesized that the number of fatalities at buildings varies with window area and habitat features that influence avian community structure. In 2010 we documented bird-window collisions (BWCs) and characterized avian community structure at 20 buildings in an urban landscape in northwestern Illinois, USA. For each building and season, we conducted 21 daily surveys for carcasses and nine point count surveys to estimate relative abundance, richness, and diversity. Our sampling design was informed by experimentally estimated carcass persistence times and detection probabilities. We used linear and generalized linear mixed models to evaluate how habitat features influenced community structure and how mortality was affected by window area and factors that correlated with community structure. The most-supported model was consistent for all community indices and included effects of season, development, and distance to vegetated lots. BWCs were related positively to window area and negatively to development. We documented mortalities for 16/72 (22%) species (34 total carcasses) recorded at buildings, and BWCs were greater for juveniles than adults. Based on the most-supported model of BWCs, the median number of annual predicted fatalities at study buildings was 3 (range = 0–52). These results suggest that patchily distributed environmental resources and levels of window area in buildings create spatial variation in BWCs within and among urban areas. Current mortality estimates place little emphasis on spatial variation, which precludes a fundamental understanding of the issue. To focus conservation efforts, we illustrate how knowledge of the structural and environmental factors that influence bird-window collisions can be used to predict fatalities in the broader landscape. PMID:23326420

Window area and development drive spatial variation in bird-window collisions in an urban landscape.

PubMed

Hager, Stephen B; Cosentino, Bradley J; McKay, Kelly J; Monson, Cathleen; Zuurdeeg, Walt; Blevins, Brian

2013-01-01

Collisions with windows are an important human-related threat to birds in urban landscapes. However, the proximate drivers of collisions are not well understood, and no study has examined spatial variation in mortality in an urban setting. We hypothesized that the number of fatalities at buildings varies with window area and habitat features that influence avian community structure. In 2010 we documented bird-window collisions (BWCs) and characterized avian community structure at 20 buildings in an urban landscape in northwestern Illinois, USA. For each building and season, we conducted 21 daily surveys for carcasses and nine point count surveys to estimate relative abundance, richness, and diversity. Our sampling design was informed by experimentally estimated carcass persistence times and detection probabilities. We used linear and generalized linear mixed models to evaluate how habitat features influenced community structure and how mortality was affected by window area and factors that correlated with community structure. The most-supported model was consistent for all community indices and included effects of season, development, and distance to vegetated lots. BWCs were related positively to window area and negatively to development. We documented mortalities for 16/72 (22%) species (34 total carcasses) recorded at buildings, and BWCs were greater for juveniles than adults. Based on the most-supported model of BWCs, the median number of annual predicted fatalities at study buildings was 3 (range = 0-52). These results suggest that patchily distributed environmental resources and levels of window area in buildings create spatial variation in BWCs within and among urban areas. Current mortality estimates place little emphasis on spatial variation, which precludes a fundamental understanding of the issue. To focus conservation efforts, we illustrate how knowledge of the structural and environmental factors that influence bird-window collisions can be used to predict fatalities in the broader landscape.

Modeling the Risk of Radiation-Induced Acute Esophagitis for Combined Washington University and RTOG Trial 93-11 Lung Cancer Patients

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

Huang, Ellen X.; Bradley, Jeffrey D.; El Naqa, Issam

2012-04-01

Purpose: To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non-small-cell lung cancer. Methods and Materials: The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemotherapy, fraction size). A wide range of dose-volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUDmore » (generalized equivalent uniform dose) values. Results: The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior-inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 Asterisk-Operator MED+1.50 Asterisk-Operator ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001). Conclusions: Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.« less

Two models for identification and predicting behaviour of an induction motor system

NASA Astrophysics Data System (ADS)

Kuo, Chien-Hsun

2018-01-01

System identification or modelling is the process of building mathematical models of dynamical systems based on the available input and output data from the systems. This paper introduces system identification by using ARX (Auto Regressive with eXogeneous input) and ARMAX (Auto Regressive Moving Average with eXogeneous input) models. Through the identified system model, the predicted output could be compared with the measured one to help prevent the motor faults from developing into a catastrophic machine failure and avoid unnecessary costs and delays caused by the need to carry out unscheduled repairs. The induction motor system is illustrated as an example. Numerical and experimental results are shown for the identified induction motor system.

Use of a MS-electronic nose for prediction of early fungal spoilage of bakery products.

PubMed

Marín, S; Vinaixa, M; Brezmes, J; Llobet, E; Vilanova, X; Correig, X; Ramos, A J; Sanchis, V

2007-02-28

A MS-based electronic nose was used to detect fungal spoilage (measured as ergosterol concentration) in samples of bakery products. Bakery products were inoculated with different Eurotium, Aspergillus and Penicillium species, incubated in sealed vials and their headspace sampled after 2, 4 and 7 days. Once the headspace was sampled, ergosterol content was determined in each sample. Different electronic nose signals were recorded depending on incubation time. Both the e-nose signals and ergosterol levels were used to build models for prediction of ergosterol content using e-nose measurements. Accuracy on prediction of those models was between 87 and 96%, except for samples inoculated with Penicillium corylophilum where the best predictions only reached 46%.

Improving urban wind flow predictions through data assimilation

NASA Astrophysics Data System (ADS)

Sousa, Jorge; Gorle, Catherine

2017-11-01

Computational fluid dynamic is fundamentally important to several aspects in the design of sustainable and resilient urban environments. The prediction of the flow pattern for example can help to determine pedestrian wind comfort, air quality, optimal building ventilation strategies, and wind loading on buildings. However, the significant variability and uncertainty in the boundary conditions poses a challenge when interpreting results as a basis for design decisions. To improve our understanding of the uncertainties in the models and develop better predictive tools, we started a pilot field measurement campaign on Stanford University's campus combined with a detailed numerical prediction of the wind flow. The experimental data is being used to investigate the potential use of data assimilation and inverse techniques to better characterize the uncertainty in the results and improve the confidence in current wind flow predictions. We consider the incoming wind direction and magnitude as unknown parameters and perform a set of Reynolds-averaged Navier-Stokes simulations to build a polynomial chaos expansion response surface at each sensor location. We subsequently use an inverse ensemble Kalman filter to retrieve an estimate for the probabilistic density function of the inflow parameters. Once these distributions are obtained, the forward analysis is repeated to obtain predictions for the flow field in the entire urban canopy and the results are compared with the experimental data. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR.

Application of Support Vector Machine to Forex Monitoring

NASA Astrophysics Data System (ADS)

Kamruzzaman, Joarder; Sarker, Ruhul A.

Previous studies have demonstrated superior performance of artificial neural network (ANN) based forex forecasting models over traditional regression models. This paper applies support vector machines to build a forecasting model from the historical data using six simple technical indicators and presents a comparison with an ANN based model trained by scaled conjugate gradient (SCG) learning algorithm. The models are evaluated and compared on the basis of five commonly used performance metrics that measure closeness of prediction as well as correctness in directional change. Forecasting results of six different currencies against Australian dollar reveal superior performance of SVM model using simple linear kernel over ANN-SCG model in terms of all the evaluation metrics. The effect of SVM parameter selection on prediction performance is also investigated and analyzed.

Using Deep Learning Model for Meteorological Satellite Cloud Image Prediction

NASA Astrophysics Data System (ADS)

Su, X.

2017-12-01

A satellite cloud image contains much weather information such as precipitation information. Short-time cloud movement forecast is important for precipitation forecast and is the primary means for typhoon monitoring. The traditional methods are mostly using the cloud feature matching and linear extrapolation to predict the cloud movement, which makes that the nonstationary process such as inversion and deformation during the movement of the cloud is basically not considered. It is still a hard task to predict cloud movement timely and correctly. As deep learning model could perform well in learning spatiotemporal features, to meet this challenge, we could regard cloud image prediction as a spatiotemporal sequence forecasting problem and introduce deep learning model to solve this problem. In this research, we use a variant of Gated-Recurrent-Unit(GRU) that has convolutional structures to deal with spatiotemporal features and build an end-to-end model to solve this forecast problem. In this model, both the input and output are spatiotemporal sequences. Compared to Convolutional LSTM(ConvLSTM) model, this model has lower amount of parameters. We imply this model on GOES satellite data and the model perform well.

Optimizing the models for rapid determination of chlorogenic acid, scopoletin and rutin in plant samples by near-infrared diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Mao, Zhiyi; Shan, Ruifeng; Wang, Jiajun; Cai, Wensheng; Shao, Xueguang

2014-07-01

Polyphenols in plant samples have been extensively studied because phenolic compounds are ubiquitous in plants and can be used as antioxidants in promoting human health. A method for rapid determination of three phenolic compounds (chlorogenic acid, scopoletin and rutin) in plant samples using near-infrared diffuse reflectance spectroscopy (NIRDRS) is studied in this work. Partial least squares (PLS) regression was used for building the calibration models, and the effects of spectral preprocessing and variable selection on the models are investigated for optimization of the models. The results show that individual spectral preprocessing and variable selection has no or slight influence on the models, but the combination of the techniques can significantly improve the models. The combination of continuous wavelet transform (CWT) for removing the variant background, multiplicative scatter correction (MSC) for correcting the scattering effect and randomization test (RT) for selecting the informative variables was found to be the best way for building the optimal models. For validation of the models, the polyphenol contents in an independent sample set were predicted. The correlation coefficients between the predicted values and the contents determined by high performance liquid chromatography (HPLC) analysis are as high as 0.964, 0.948 and 0.934 for chlorogenic acid, scopoletin and rutin, respectively.

Local-aggregate modeling for big data via distributed optimization: Applications to neuroimaging.

PubMed

Hu, Yue; Allen, Genevera I

2015-12-01

Technological advances have led to a proliferation of structured big data that have matrix-valued covariates. We are specifically motivated to build predictive models for multi-subject neuroimaging data based on each subject's brain imaging scans. This is an ultra-high-dimensional problem that consists of a matrix of covariates (brain locations by time points) for each subject; few methods currently exist to fit supervised models directly to this tensor data. We propose a novel modeling and algorithmic strategy to apply generalized linear models (GLMs) to this massive tensor data in which one set of variables is associated with locations. Our method begins by fitting GLMs to each location separately, and then builds an ensemble by blending information across locations through regularization with what we term an aggregating penalty. Our so called, Local-Aggregate Model, can be fit in a completely distributed manner over the locations using an Alternating Direction Method of Multipliers (ADMM) strategy, and thus greatly reduces the computational burden. Furthermore, we propose to select the appropriate model through a novel sequence of faster algorithmic solutions that is similar to regularization paths. We will demonstrate both the computational and predictive modeling advantages of our methods via simulations and an EEG classification problem. © 2015, The International Biometric Society.

Modeling of autocatalytic hydrolysis of adefovir dipivoxil in solid formulations.

PubMed

Dong, Ying; Zhang, Yan; Xiang, Bingren; Deng, Haishan; Wu, Jingfang

2011-04-01

The stability and hydrolysis kinetics of a phosphate prodrug, adefovir dipivoxil, in solid formulations were studied. The stability relationship between five solid formulations was explored. An autocatalytic mechanism for hydrolysis could be proposed according to the kinetic behavior which fits the Prout-Tompkins model well. For the classical kinetic models could hardly describe and predict the hydrolysis kinetics of adefovir dipivoxil in solid formulations accurately when the temperature is high, a feedforward multilayer perceptron (MLP) neural network was constructed to model the hydrolysis kinetics. The build-in approaches in Weka, such as lazy classifiers and rule-based learners (IBk, KStar, DecisionTable and M5Rules), were used to verify the performance of MLP. The predictability of the models was evaluated by 10-fold cross-validation and an external test set. It reveals that MLP should be of general applicability proposing an alternative efficient way to model and predict autocatalytic hydrolysis kinetics for phosphate prodrugs.

Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count.

PubMed

Nowosad, Jakub; Stach, Alfred; Kasprzyk, Idalia; Weryszko-Chmielewska, Elżbieta; Piotrowska-Weryszko, Krystyna; Puc, Małgorzata; Grewling, Łukasz; Pędziszewska, Anna; Uruska, Agnieszka; Myszkowska, Dorota; Chłopek, Kazimiera; Majkowska-Wojciechowska, Barbara

The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus , Alnus , and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.

ZY3-02 Laser Altimeter Footprint Geolocation Prediction

PubMed Central

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-01-01

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test. PMID:28934160

ZY3-02 Laser Altimeter Footprint Geolocation Prediction.

PubMed

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-09-21

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

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

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

Development of Tsunami Numerical Model Considering the Disaster Debris such as Cars, Ships and Collapsed Buildings

NASA Astrophysics Data System (ADS)

Kozono, Y.; Takahashi, T.; Sakuraba, M.; Nojima, K.

2016-12-01

A lot of debris by tsunami, such as cars, ships and collapsed buildings were generated in the 2011 Tohoku tsunami. It is useful for rescue and recovery after tsunami disaster to predict the amount and final position of disaster debris. The transport form of disaster debris varies as drifting, rolling and sliding. These transport forms need to be considered comprehensively in tsunami simulation. In this study, we focused on the following three points. Firstly, the numerical model considering various transport forms of disaster debris was developed. The proposed numerical model was compared with the hydraulic experiment by Okubo et al. (2004) in order to verify transport on the bottom surface such as rolling and sliding. Secondly, a numerical experiment considering transporting on the bottom surface and drifting was studied. Finally, the numerical model was applied for Kesennuma city where serious damage occurred by the 2011 Tohoku tsunami. In this model, the influence of disaster debris was considered as tsunami flow energy loss. The hydraulic experiments conducted in a water tank which was 10 m long by 30 cm wide. The gate confined water in a storage tank, and acted as a wave generator. A slope was set at downstream section. The initial position of a block (width: 3.2 cm, density: 1.55 g/cm3) assuming the disaster debris was placed in front of the slope. The proposed numerical model simulated well the maximum transport distance and the final stop position of the block. In the second numerical experiment, the conditions were the same as the hydraulic experiment, except for the density of the block. The density was set to various values (from 0.30 to 4.20 g/cm3). This model was able to estimate various transport forms including drifting and sliding. In the numerical simulation of the 2011 Tohoku tsunami, the condition of buildings was modeled as follows: (i)the resistance on the bottom using Manning roughness coefficient (conventional method), and (ii)structure of buildings with collapsing and washing-away due to tsunami wave pressure. In this calculation, disaster debris of collapsed buildings, cars and ships was considered. As a result, the proposed model showed that it is necessary to take the disaster debris into account in order to predict tsunami inundation accurately.

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

PubMed

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.

A BIM-based system for demolition and renovation waste estimation and planning.

PubMed

Cheng, Jack C P; Ma, Lauren Y H

2013-06-01

Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C&D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D&R) works and the growing amount of D&R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D&R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D&R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Large eddy simulation on buoyant gas diffusion near building

NASA Astrophysics Data System (ADS)

Tominaga, Yoshihide; Murakami, Shuzo; Mochida, Akashi

1992-12-01

Large eddy simulations on turbulent diffusion of buoyant gases near a building model are carried out for three cases in which the densimetric Froude Number (Frd) was specified at - 8.6, zero and 8.6 respectively. The accuracy of these simulations is examined by comparing the numerically predicted results with wind tunnel experiments conducted. Two types of sub-grid scale models, the standard Smagorinsky model (type 1) and the modified Smagorinsky model (type 2) are compared. The former does not take account of the production of subgrid energy by buoyancy force but the latter incorporates this effect. The latter model (type 2) gives more accurate results than those given by the standard Smagorinsky model (type 1) in terms of the distributions of kappa greater than sign C less than sign greater than sign C(sup - 2) less than sign.

NAME Modeling and Climate Process Team

NASA Astrophysics Data System (ADS)

Schemm, J. E.; Williams, L. N.; Gutzler, D. S.

2007-05-01

NAME Climate Process and Modeling Team (CPT) has been established to address the need of linking climate process research to model development and testing activities for warm season climate prediction. The project builds on two existing NAME-related modeling efforts. One major component of this project is the organization and implementation of a second phase of NAMAP, based on the 2004 season. NAMAP2 will re-examine the metrics proposed by NAMAP, extend the NAMAP analysis to transient variability, exploit the extensive observational database provided by NAME 2004 to analyze simulation targets of special interest, and expand participation. Vertical column analysis will bring local NAME observations and model outputs together in a context where key physical processes in the models can be evaluated and improved. The second component builds on the current NAME-related modeling effort focused on the diurnal cycle of precipitation in several global models, including those implemented at NCEP, NASA and GFDL. Our activities will focus on the ability of the operational NCEP Global Forecast System (GFS) to simulate the diurnal and seasonal evolution of warm season precipitation during the NAME 2004 EOP, and on changes to the treatment of deep convection in the complicated terrain of the NAMS domain that are necessary to improve the simulations, and ultimately predictions of warm season precipitation These activities will be strongly tied to NAMAP2 to ensure technology transfer from research to operations. Results based on experiments conducted with the NCEP CFS GCM will be reported at the conference with emphasis on the impact of horizontal resolution in predicting warm season precipitation over North America.

DemQSAR: predicting human volume of distribution and clearance of drugs

NASA Astrophysics Data System (ADS)

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/.

DemQSAR: predicting human volume of distribution and clearance of drugs.

PubMed

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/ .

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Case, Jonathan; Venners, Jason; Schroeder, Richard; Checchi, Milton; Zavodsky, Bradley; Limaye, Ashutosh; O'Brien, Raymond

2015-01-01

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud-computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high-performance computing environments, supporting multi-node systems required for near real-time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on-demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on-demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre-compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF-EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher-resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short-term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity-building applications that extend numerical weather prediction to developing countries are intended to provide near real-time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather-related hazards, or impacts that local weather events may have during the recovery phase.

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

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

Moore, Bryan A.; Rougier, Esteban; O’Malley, Daniel

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path tomore » failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.« less

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

DOE PAGES

Moore, Bryan A.; Rougier, Esteban; O’Malley, Daniel; ...

2018-02-22

We use simulation data from a high delity Finite-Discrete Element Model to build an e cient Machine Learning (ML) approach to predict fracture growth and coalescence. Our goal is for the ML approach to be used as an emulator in place of the computationally intensive high delity models in an uncertainty quanti cation framework where thousands of forward runs are required. The failure of materials with various fracture con gurations (size, orientation and the number of initial cracks) are explored and used as data to train our ML model. This novel approach has shown promise in predicting spatial (path tomore » failure) and temporal (time to failure) aspects of brittle material failure. Predictions of where dominant fracture paths formed within a material were ~85% accurate and the time of material failure deviated from the actual failure time by an average of ~16%. Additionally, the ML model achieves a reduction in computational cost by multiple orders of magnitude.« less

QSAR modeling of acute toxicity on mammals caused by aromatic compounds: the case study using oral LD50 for rats.

PubMed

Rasulev, Bakhtiyor; Kusić, Hrvoje; Leszczynska, Danuta; Leszczynski, Jerzy; Koprivanac, Natalija

2010-05-01

The goal of the study was to predict toxicity in vivo caused by aromatic compounds structured with a single benzene ring and the presence or absence of different substituent groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc., by using QSAR/QSPR tools. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. The most predictive model is shown to be the 3-variable model which also has a good ratio of the number of descriptors and their predictive ability to avoid overfitting. The main contributions to the toxicity were shown to be the polarizability weighted MATS2p and the number of certain groups C-026 descriptors. The GA-MLRA approach showed good results in this study, which allows the building of a simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals.

Modeling chlorophyll-a and dissolved oxygen concentration in tropical floodplain lakes (Paraná River, Brazil).

PubMed

Rocha, R R A; Thomaz, S M; Carvalho, P; Gomes, L C

2009-06-01

The need for prediction is widely recognized in limnology. In this study, data from 25 lakes of the Upper Paraná River floodplain were used to build models to predict chlorophyll-a and dissolved oxygen concentrations. Akaike's information criterion (AIC) was used as a criterion for model selection. Models were validated with independent data obtained in the same lakes in 2001. Predictor variables that significantly explained chlorophyll-a concentration were pH, electrical conductivity, total seston (positive correlation) and nitrate (negative correlation). This model explained 52% of chlorophyll variability. Variables that significantly explained dissolved oxygen concentration were pH, lake area and nitrate (all positive correlations); water temperature and electrical conductivity were negatively correlated with oxygen. This model explained 54% of oxygen variability. Validation with independent data showed that both models had the potential to predict algal biomass and dissolved oxygen concentration in these lakes. These findings suggest that multiple regression models are valuable and practical tools for understanding the dynamics of ecosystems and that predictive limnology may still be considered a powerful approach in aquatic ecology.

Modeling the Relationship between Pre-Service Teachers' TPACK and Digital Nativity

ERIC Educational Resources Information Center

Kabakci Yurdakul, Isil

2018-01-01

The purpose of this study is to build a structural equation model that predicts the relationship between Technological Pedagogical Content Knowledge (TPACK) competencies and digital nativity. The data was collected from 1493 Turkish pre-service teachers. Two instruments were used in the data collection; a TPACK-deep scale and a Turkish adaptation…

Validation Methodology to Allow Simulated Peak Reduction and Energy Performance Analysis of Residential Building Envelope with Phase Change Materials: Preprint

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

Tabares-Velasco, P. C.; Christensen, C.; Bianchi, M.

2012-08-01

Phase change materials (PCM) represent a potential technology to reduce peak loads and HVAC energy consumption in residential buildings. This paper summarizes NREL efforts to obtain accurate energy simulations when PCMs are modeled in residential buildings: the overall methodology to verify and validate Conduction Finite Difference (CondFD) and PCM algorithms in EnergyPlus is presented in this study. It also shows preliminary results of three residential building enclosure technologies containing PCM: PCM-enhanced insulation, PCM impregnated drywall and thin PCM layers. The results are compared based on predicted peak reduction and energy savings using two algorithms in EnergyPlus: the PCM and Conductionmore » Finite Difference (CondFD) algorithms.« less

Sirius PSB: a generic system for analysis of biological sequences.

PubMed

Koh, Chuan Hock; Lin, Sharene; Jedd, Gregory; Wong, Limsoon

2009-12-01

Computational tools are essential components of modern biological research. For example, BLAST searches can be used to identify related proteins based on sequence homology, or when a new genome is sequenced, prediction models can be used to annotate functional sites such as transcription start sites, translation initiation sites and polyadenylation sites and to predict protein localization. Here we present Sirius Prediction Systems Builder (PSB), a new computational tool for sequence analysis, classification and searching. Sirius PSB has four main operations: (1) Building a classifier, (2) Deploying a classifier, (3) Search for proteins similar to query proteins, (4) Preliminary and post-prediction analysis. Sirius PSB supports all these operations via a simple and interactive graphical user interface. Besides being a convenient tool, Sirius PSB has also introduced two novelties in sequence analysis. Firstly, genetic algorithm is used to identify interesting features in the feature space. Secondly, instead of the conventional method of searching for similar proteins via sequence similarity, we introduced searching via features' similarity. To demonstrate the capabilities of Sirius PSB, we have built two prediction models - one for the recognition of Arabidopsis polyadenylation sites and another for the subcellular localization of proteins. Both systems are competitive against current state-of-the-art models based on evaluation of public datasets. More notably, the time and effort required to build each model is greatly reduced with the assistance of Sirius PSB. Furthermore, we show that under certain conditions when BLAST is unable to find related proteins, Sirius PSB can identify functionally related proteins based on their biophysical similarities. Sirius PSB and its related supplements are available at: http://compbio.ddns.comp.nus.edu.sg/~sirius.

Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS).

PubMed

Breen, Michael S; Burke, Janet M; Batterman, Stuart A; Vette, Alan F; Godwin, Christopher; Croghan, Carry W; Schultz, Bradley D; Long, Thomas C

2014-11-07

Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.

Using Whole-House Field Tests to Empirically Derive Moisture Buffering Model Inputs

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

Woods, J.; Winkler, J.; Christensen, D.

2014-08-01

Building energy simulations can be used to predict a building's interior conditions, along with the energy use associated with keeping these conditions comfortable. These models simulate the loads on the building (e.g., internal gains, envelope heat transfer), determine the operation of the space conditioning equipment, and then calculate the building's temperature and humidity throughout the year. The indoor temperature and humidity are affected not only by the loads and the space conditioning equipment, but also by the capacitance of the building materials, which buffer changes in temperature and humidity. This research developed an empirical method to extract whole-house model inputsmore » for use with a more accurate moisture capacitance model (the effective moisture penetration depth model). The experimental approach was to subject the materials in the house to a square-wave relative humidity profile, measure all of the moisture transfer terms (e.g., infiltration, air conditioner condensate) and calculate the only unmeasured term: the moisture absorption into the materials. After validating the method with laboratory measurements, we performed the tests in a field house. A least-squares fit of an analytical solution to the measured moisture absorption curves was used to determine the three independent model parameters representing the moisture buffering potential of this house and its furnishings. Follow on tests with realistic latent and sensible loads showed good agreement with the derived parameters, especially compared to the commonly-used effective capacitance approach. These results show that the EMPD model, once the inputs are known, is an accurate moisture buffering model.« less

Transforming BIM to BEM: Generation of Building Geometry for the NASA Ames Sustainability Base BIM

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

O'Donnell, James T.; Maile, Tobias; Rose, Cody

Typical processes of whole Building Energy simulation Model (BEM) generation are subjective, labor intensive, time intensive and error prone. Essentially, these typical processes reproduce already existing data, i.e. building models already created by the architect. Accordingly, Lawrence Berkeley National Laboratory (LBNL) developed a semi-automated process that enables reproducible conversions of Building Information Model (BIM) representations of building geometry into a format required by building energy modeling (BEM) tools. This is a generic process that may be applied to all building energy modeling tools but to date has only been used for EnergyPlus. This report describes and demonstrates each stage inmore » the semi-automated process for building geometry using the recently constructed NASA Ames Sustainability Base throughout. This example uses ArchiCAD (Graphisoft, 2012) as the originating CAD tool and EnergyPlus as the concluding whole building energy simulation tool. It is important to note that the process is also applicable for professionals that use other CAD tools such as Revit (“Revit Architecture,” 2012) and DProfiler (Beck Technology, 2012) and can be extended to provide geometry definitions for BEM tools other than EnergyPlus. Geometry Simplification Tool (GST) was used during the NASA Ames project and was the enabling software that facilitated semi-automated data transformations. GST has now been superseded by Space Boundary Tool (SBT-1) and will be referred to as SBT-1 throughout this report. The benefits of this semi-automated process are fourfold: 1) reduce the amount of time and cost required to develop a whole building energy simulation model, 2) enable rapid generation of design alternatives, 3) improve the accuracy of BEMs and 4) result in significantly better performing buildings with significantly lower energy consumption than those created using the traditional design process, especially if the simulation model was used as a predictive benchmark during operation. Developing BIM based criteria to support the semi-automated process should result in significant reliable improvements and time savings in the development of BEMs. In order to define successful BIMS, CAD export of IFC based BIMs for BEM must adhere to a standard Model View Definition (MVD) for simulation as provided by the concept design BIM MVD (buildingSMART, 2011). In order to ensure wide scale adoption, companies would also need to develop their own material libraries to support automated activities and undertake a pilot project to improve understanding of modeling conventions and design tool features and limitations.« less

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

PubMed Central

Sen-Bhattacharya, Basabdatta; Serrano-Gotarredona, Teresa; Balassa, Lorinc; Bhattacharya, Akash; Stokes, Alan B.; Rowley, Andrew; Sugiarto, Indar; Furber, Steve

2017-01-01

We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of three “nodes,” where each node is the “basic building block” LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W. PMID:28848380

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine.

PubMed

Sen-Bhattacharya, Basabdatta; Serrano-Gotarredona, Teresa; Balassa, Lorinc; Bhattacharya, Akash; Stokes, Alan B; Rowley, Andrew; Sugiarto, Indar; Furber, Steve

2017-01-01

We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a "basic building block" for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)-brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10-50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of three "nodes," where each node is the "basic building block" LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W.

Grammar-Supported 3d Indoor Reconstruction from Point Clouds for As-Built Bim

NASA Astrophysics Data System (ADS)

Becker, S.; Peter, M.; Fritsch, D.

2015-03-01

The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

PubMed

Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

2016-08-01

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

Hybrid Automatic Building Interpretation System

NASA Astrophysics Data System (ADS)

Pakzad, K.; Klink, A.; Müterthies, A.; Gröger, G.; Stroh, V.; Plümer, L.

2011-09-01

HABIS (Hybrid Automatic Building Interpretation System) is a system for an automatic reconstruction of building roofs used in virtual 3D building models. Unlike most of the commercially available systems, HABIS is able to work to a high degree automatically. The hybrid method uses different sources intending to exploit the advantages of the particular sources. 3D point clouds usually provide good height and surface data, whereas spatial high resolution aerial images provide important information for edges and detail information for roof objects like dormers or chimneys. The cadastral data provide important basis information about the building ground plans. The approach used in HABIS works with a multi-stage-process, which starts with a coarse roof classification based on 3D point clouds. After that it continues with an image based verification of these predicted roofs. In a further step a final classification and adjustment of the roofs is done. In addition some roof objects like dormers and chimneys are also extracted based on aerial images and added to the models. In this paper the used methods are described and some results are presented.

Excessive nest building is a unique behavioural phenotype in the deer mouse model of obsessive-compulsive disorder.

PubMed

Wolmarans, De Wet; Stein, Dan J; Harvey, Brian H

2016-09-01

Obsessive-compulsive disorder (OCD) is a phenotypically heterogeneous condition characterised by time-consuming intrusive thoughts and/or compulsions. Irrespective of the symptom type diagnosed, the severity of OCD is characterised by heterogeneity in symptom presentation that complicates diagnosis and treatment. Heterogeneity of symptoms would be invaluable in an animal model. Nest building behaviour forms part of the normal behavioural repertoire of rodents and demonstrates profound between-species differences. However, it has been proposed that within-species differences in nest building behaviour (i.e. aberrant vs. normal nest building) may resemble obsessive-compulsive-like symptoms. In an attempt to investigate whether other obsessive-compulsive-like behaviours are present in an animal model of OCD, or if aberrant nest building behaviour may represent a unique obsessive-compulsive phenotype in such a model, the current study assessed nest building behaviour in high (H, viz obsessive-compulsive) and non (N, viz normal) stereotypical deer mice. Subsequently, 12 N and H animals, respectively, were provided with an excess of cotton wool daily for one week prior to and following four weeks of high-dose oral escitalopram treatment (50 mg/kg/day). Data from the current investigation demonstrate daily nesting activity to be highly variable in deer mice, with stereotypy and nest building being independent behaviours. However, we identified unique aberrant large nest building behaviour in 30% of animals from both cohorts that was attenuated by escitalopram to pre-treatment nesting scores of the larger group. In summary, behavioural and drug-treatment evidence confirms that deer mouse behaviour does indeed resemble symptom heterogeneity related to OCD, and as such expands its face and predictive validity for the disorder. © The Author(s) 2016.

Spatiotemporal Bayesian networks for malaria prediction.

PubMed

Haddawy, Peter; Hasan, A H M Imrul; Kasantikul, Rangwan; Lawpoolsri, Saranath; Sa-Angchai, Patiwat; Kaewkungwal, Jaranit; Singhasivanon, Pratap

2018-01-01

Targeted intervention and resource allocation are essential for effective malaria control, particularly in remote areas, with predictive models providing important information for decision making. While a diversity of modeling technique have been used to create predictive models of malaria, no work has made use of Bayesian networks. Bayes nets are attractive due to their ability to represent uncertainty, model time lagged and nonlinear relations, and provide explanations. This paper explores the use of Bayesian networks to model malaria, demonstrating the approach by creating village level models with weekly temporal resolution for Tha Song Yang district in northern Thailand. The networks are learned using data on cases and environmental covariates. Three types of networks are explored: networks for numeric prediction, networks for outbreak prediction, and networks that incorporate spatial autocorrelation. Evaluation of the numeric prediction network shows that the Bayes net has prediction accuracy in terms of mean absolute error of about 1.4 cases for 1 week prediction and 1.7 cases for 6 week prediction. The network for outbreak prediction has an ROC AUC above 0.9 for all prediction horizons. Comparison of prediction accuracy of both Bayes nets against several traditional modeling approaches shows the Bayes nets to outperform the other models for longer time horizon prediction of high incidence transmission. To model spread of malaria over space, we elaborate the models with links between the village networks. This results in some very large models which would be far too laborious to build by hand. So we represent the models as collections of probability logic rules and automatically generate the networks. Evaluation of the models shows that the autocorrelation links significantly improve prediction accuracy for some villages in regions of high incidence. We conclude that spatiotemporal Bayesian networks are a highly promising modeling alternative for prediction of malaria and other vector-borne diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

Prediction models for successful external cephalic version: a systematic review.

PubMed

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.

Bayesian source term estimation of atmospheric releases in urban areas using LES approach.

PubMed

Xue, Fei; Kikumoto, Hideki; Li, Xiaofeng; Ooka, Ryozo

2018-05-05

The estimation of source information from limited measurements of a sensor network is a challenging inverse problem, which can be viewed as an assimilation process of the observed concentration data and the predicted concentration data. When dealing with releases in built-up areas, the predicted data are generally obtained by the Reynolds-averaged Navier-Stokes (RANS) equations, which yields building-resolving results; however, RANS-based models are outperformed by large-eddy simulation (LES) in the predictions of both airflow and dispersion. Therefore, it is important to explore the possibility of improving the estimation of the source parameters by using the LES approach. In this paper, a novel source term estimation method is proposed based on LES approach using Bayesian inference. The source-receptor relationship is obtained by solving the adjoint equations constructed using the time-averaged flow field simulated by the LES approach based on the gradient diffusion hypothesis. A wind tunnel experiment with a constant point source downwind of a single building model is used to evaluate the performance of the proposed method, which is compared with that of the existing method using a RANS model. The results show that the proposed method reduces the errors of source location and releasing strength by 77% and 28%, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparative analysis of economic models in selected solar energy computer programs

NASA Astrophysics Data System (ADS)

Powell, J. W.; Barnes, K. A.

1982-01-01

The economic evaluation models in five computer programs widely used for analyzing solar energy systems (F-CHART 3.0, F-CHART 4.0, SOLCOST, BLAST, and DOE-2) are compared. Differences in analysis techniques and assumptions among the programs are assessed from the point of view of consistency with the Federal requirements for life cycle costing (10 CFR Part 436), effect on predicted economic performance, and optimal system size, case of use, and general applicability to diverse systems types and building types. The FEDSOL program developed by the National Bureau of Standards specifically to meet the Federal life cycle cost requirements serves as a basis for the comparison. Results of the study are illustrated in test cases of two different types of Federally owned buildings: a single family residence and a low rise office building.

Electric Conduction in Solids: a Pedagogical Approach Supported by Laboratory Measurements and Computer Modelling Environments

NASA Astrophysics Data System (ADS)

Bonura, A.; Capizzo, M. C.; Fazio, C.; Guastella, I.

2008-05-01

In this paper we present a pedagogic approach aimed at modeling electric conduction in semiconductors, built by using NetLogo, a programmable modeling environment for building and exploring multi-agent systems. `Virtual experiments' are implemented to confront predictions of different microscopic models with real measurements of electric properties of matter, such as resistivity. The relations between these electric properties and other physical variables, like temperature, are, then, analyzed.

Learning about individuals' health from aggregate data.

PubMed

Colbaugh, Rich; Glass, Kristin

2017-07-01

There is growing awareness that user-generated social media content contains valuable health-related information and is more convenient to collect than typical health data. For example, Twitter has been employed to predict aggregate-level outcomes, such as regional rates of diabetes and child poverty, and to identify individual cases of depression and food poisoning. Models which make aggregate-level inferences can be induced from aggregate data, and consequently are straightforward to build. In contrast, learning models that produce individual-level (IL) predictions, which are more informative, usually requires a large number of difficult-to-acquire labeled IL examples. This paper presents a new machine learning method which achieves the best of both worlds, enabling IL models to be learned from aggregate labels. The algorithm makes predictions by combining unsupervised feature extraction, aggregate-based modeling, and optimal integration of aggregate-level and IL information. Two case studies illustrate how to learn health-relevant IL prediction models using only aggregate labels, and show that these models perform as well as state-of-the-art models trained on hundreds or thousands of labeled individuals.

Initial and hourly headloss modelling on a tertiary nitrifying wastewater biofiltration plant.

PubMed

Bernier, Jean; Rocher, Vincent; Lessard, Paul

2016-01-01

The headloss prediction capability of a wastewater biofiltration model is evaluated on data from a full-scale tertiary nitrifying biofilter unit located in the Paris conurbation (Achères, France; 6,000,000 population equivalent). The model has been previously calibrated on nutrient conversion and TSS filtration observations. In this paper the mass of extracted biofilm during biofilter backwash and the headloss value at the start of an operation cycle are first calibrated on sludge production estimations and relative pressure measurements over the year 2009. The calibrated model is then used on two one-month periods in 2012 for which hourly headloss measurements were acquired. The observed trends are correctly predicted for 2009 but the model exhibits some heavy daily variation that is not found in measurements. Hourly predictions stay close to observations, although the model error rises slightly when the headloss does not vary much. The global model shows that both nutrient conversion and headloss build-up can be reasonably well predicted at the same time on a full-scale plant.

Computational prediction of chemical reactions: current status and outlook.

PubMed

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Photovoltaic performance models: an evaluation with actual field data

NASA Astrophysics Data System (ADS)

TamizhMani, Govindasamy; Ishioye, John-Paul; Voropayev, Arseniy; Kang, Yi

2008-08-01

Prediction of energy production is crucial to the design and installation of the building integrated photovoltaic systems. This prediction should be attainable based on the commonly available parameters such as system size, orientation and tilt angle. Several commercially available as well as free downloadable software tools exist to predict energy production. Six software models have been evaluated in this study and they are: PV Watts, PVsyst, MAUI, Clean Power Estimator, Solar Advisor Model (SAM) and RETScreen. This evaluation has been done by comparing the monthly, seasonaly and annually predicted data with the actual, field data obtained over a year period on a large number of residential PV systems ranging between 2 and 3 kWdc. All the systems are located in Arizona, within the Phoenix metropolitan area which lies at latitude 33° North, and longitude 112 West, and are all connected to the electrical grid.

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

PubMed Central

2018-01-01

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

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

PubMed

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

2018-01-01

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

Prediction of brain maturity based on cortical thickness at different spatial resolutions.

PubMed

Khundrakpam, Budhachandra S; Tohka, Jussi; Evans, Alan C

2015-05-01

Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. Copyright © 2015 Elsevier Inc. All rights reserved.

Seismic fragility assessment of low-rise stone masonry buildings

NASA Astrophysics Data System (ADS)

Abo-El-Ezz, Ahmad; Nollet, Marie-José; Nastev, Miroslav

2013-03-01

Many historic buildings in old urban centers in Eastern Canada are made of stone masonry reputed to be highly vulnerable to seismic loads. Seismic risk assessment of stone masonry buildings is therefore the first step in the risk mitigation process to provide adequate planning for retrofit and preservation of historical urban centers. This paper focuses on development of analytical displacement-based fragility curves reflecting the characteristics of existing stone masonry buildings in Eastern Canada. The old historic center of Quebec City has been selected as a typical study area. The standard fragility analysis combines the inelastic spectral displacement, a structure-dependent earthquake intensity measure, and the building damage state correlated to the induced building displacement. The proposed procedure consists of a three-step development process: (1) mechanics-based capacity model, (2) displacement-based damage model and (3) seismic demand model. The damage estimation for a uniform hazard scenario of 2% in 50 years probability of exceedance indicates that slight to moderate damage is the most probable damage experienced by these stone masonry buildings. Comparison is also made with fragility curves implicit in the seismic risk assessment tools Hazus and ELER. Hazus shows the highest probability of the occurrence of no to slight damage, whereas the highest probability of extensive and complete damage is predicted with ELER. This comparison shows the importance of the development of fragility curves specific to the generic construction characteristics in the study area and emphasizes the need for critical use of regional risk assessment tools and generated results.

Integrative Approach for Computationally Inferring Interactions between the Alpha and Beta Subunits of the Calcium-Activated Potassium Channel (BK): a Docking Study

PubMed Central

González, Janneth; Gálvez, Angela; Morales, Ludis; Barreto, George E.; Capani, Francisco; Sierra, Omar; Torres, Yolima

2013-01-01

Three-dimensional models of the alpha- and beta-1 subunits of the calcium-activated potassium channel (BK) were predicted by threading modeling. A recursive approach comprising of sequence alignment and model building based on three templates was used to build these models, with the refinement of non-conserved regions carried out using threading techniques. The complex formed by the subunits was studied by means of docking techniques, using 3D models of the two subunits, and an approach based on rigid-body structures. Structural effects of the complex were analyzed with respect to hydrogen-bond interactions and binding-energy calculations. Potential interaction sites of the complex were determined by referencing a study of the difference accessible surface area (DASA) of the protein subunits in the complex. PMID:23492851

Affordable and personalized lighting using inverse modeling and virtual sensors

NASA Astrophysics Data System (ADS)

Basu, Chandrayee; Chen, Benjamin; Richards, Jacob; Dhinakaran, Aparna; Agogino, Alice; Martin, Rodney

2014-03-01

Wireless sensor networks (WSN) have great potential to enable personalized intelligent lighting systems while reducing building energy use by 50%-70%. As a result WSN systems are being increasingly integrated in state-ofart intelligent lighting systems. In the future these systems will enable participation of lighting loads as ancillary services. However, such systems can be expensive to install and lack the plug-and-play quality necessary for user-friendly commissioning. In this paper we present an integrated system of wireless sensor platforms and modeling software to enable affordable and user-friendly intelligent lighting. It requires ⇠ 60% fewer sensor deployments compared to current commercial systems. Reduction in sensor deployments has been achieved by optimally replacing the actual photo-sensors with real-time discrete predictive inverse models. Spatially sparse and clustered sub-hourly photo-sensor data captured by the WSN platforms are used to develop and validate a piece-wise linear regression of indoor light distribution. This deterministic data-driven model accounts for sky conditions and solar position. The optimal placement of photo-sensors is performed iteratively to achieve the best predictability of the light field desired for indoor lighting control. Using two weeks of daylight and artificial light training data acquired at the Sustainability Base at NASA Ames, the model was able to predict the light level at seven monitored workstations with 80%-95% accuracy. We estimate that 10% adoption of this intelligent wireless sensor system in commercial buildings could save 0.2-0.25 quads BTU of energy nationwide.

Predictive Finite Rate Model for Oxygen-Carbon Interactions at High Temperature

NASA Astrophysics Data System (ADS)

Poovathingal, Savio

An oxidation model for carbon surfaces is developed to predict ablation rates for carbon heat shields used in hypersonic vehicles. Unlike existing empirical models, the approach used here was to probe gas-surface interactions individually and then based on an understanding of the relevant fundamental processes, build a predictive model that would be accurate over a wide range of pressures and temperatures, and even microstructures. Initially, molecular dynamics was used to understand the oxidation processes on the surface. The molecular dynamics simulations were compared to molecular beam experiments and good qualitative agreement was observed. The simulations reproduced cylindrical pitting observed in the experiments where oxidation was rapid and primarily occurred around a defect. However, the studies were limited to small systems at low temperatures and could simulate time scales only of the order of nanoseconds. Molecular beam experiments at high surface temperature indicated that a majority of surface reaction products were produced through thermal mechanisms. Since the reactions were thermal, they occurred over long time scales which were computationally prohibitive for molecular dynamics to simulate. The experiments provided detailed dynamical data on the scattering of O, O2, CO, and CO2 and it was found that the data from molecular beam experiments could be used directly to build a model. The data was initially used to deduce surface reaction probabilities at 800 K. The reaction probabilities were then incorporated into the direct simulation Monte Carlo (DSMC) method. Simulations were performed where the microstructure was resolved and dissociated oxygen convected and diffused towards it. For a gas-surface temperature of 800 K, it was found that despite CO being the dominant surface reaction product, a gas-phase reaction forms significant CO2 within the microstructure region. It was also found that surface area did not play any role in concentration of reaction products because the reaction probabilities were in the diffusion dominant regime. The molecular beam data at different surface temperatures was then used to build a finite rate model. Each reaction mechanism and all rate parameters of the new model were determined individually based on the molecular beam data. Despite the experiments being performed at near vacuum conditions, the finite rate model developed using the data could be used at pressures and temperatures relevant to hypersonic conditions. The new model was implemented in a computational fluid dynamics (CFD) solver and flow over a hypersonic vehicle was simulated. The new model predicted similar overall mass loss rates compared to existing models, however, the individual species production rates were completely different. The most notable difference was that the new model (based on molecular beam data) predicts CO as the oxidation reaction product with virtually no CO2 production, whereas existing models predict the exact opposite trend. CO being the dominant oxidation product is consistent with recent high enthalpy wind tunnel experiments. The discovery that measurements taken in molecular beam facilities are able to determine individual reaction mechanisms, including dependence on surface coverage, opens up an entirely new way of constructing ablation models.

Verification of models for ballistic movement time and endpoint variability.

PubMed

Lin, Ray F; Drury, Colin G

2013-01-01

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.

Tools for Evaluating Fault Detection and Diagnostic Methods for HVAC Secondary Systems

NASA Astrophysics Data System (ADS)

Pourarian, Shokouh

Although modern buildings are using increasingly sophisticated energy management and control systems that have tremendous control and monitoring capabilities, building systems routinely fail to perform as designed. More advanced building control, operation, and automated fault detection and diagnosis (AFDD) technologies are needed to achieve the goal of net-zero energy commercial buildings. Much effort has been devoted to develop such technologies for primary heating ventilating and air conditioning (HVAC) systems, and some secondary systems. However, secondary systems, such as fan coil units and dual duct systems, although widely used in commercial, industrial, and multifamily residential buildings, have received very little attention. This research study aims at developing tools that could provide simulation capabilities to develop and evaluate advanced control, operation, and AFDD technologies for these less studied secondary systems. In this study, HVACSIM+ is selected as the simulation environment. Besides developing dynamic models for the above-mentioned secondary systems, two other issues related to the HVACSIM+ environment are also investigated. One issue is the nonlinear equation solver used in HVACSIM+ (Powell's Hybrid method in subroutine SNSQ). It has been found from several previous research projects (ASRHAE RP 825 and 1312) that SNSQ is especially unstable at the beginning of a simulation and sometimes unable to converge to a solution. Another issue is related to the zone model in the HVACSIM+ library of components. Dynamic simulation of secondary HVAC systems unavoidably requires an interacting zone model which is systematically and dynamically interacting with building surrounding. Therefore, the accuracy and reliability of the building zone model affects operational data generated by the developed dynamic tool to predict HVAC secondary systems function. The available model does not simulate the impact of direct solar radiation that enters a zone through glazing and the study of zone model is conducted in this direction to modify the existing zone model. In this research project, the following tasks are completed and summarized in this report: 1. Develop dynamic simulation models in the HVACSIM+ environment for common fan coil unit and dual duct system configurations. The developed simulation models are able to produce both fault-free and faulty operational data under a wide variety of faults and severity levels for advanced control, operation, and AFDD technology development and evaluation purposes; 2. Develop a model structure, which includes the grouping of blocks and superblocks, treatment of state variables, initial and boundary conditions, and selection of equation solver, that can simulate a dual duct system efficiently with satisfactory stability; 3. Design and conduct a comprehensive and systematic validation procedure using collected experimental data to validate the developed simulation models under both fault-free and faulty operational conditions; 4. Conduct a numerical study to compare two solution techniques: Powell's Hybrid (PH) and Levenberg-Marquardt (LM) in terms of their robustness and accuracy. 5. Modification of the thermal state of the existing building zone model in HVACSIM+ library of component. This component is revised to consider the transmitted heat through glazing as a heat source for transient building zone load prediction In this report, literature, including existing HVAC dynamic modeling environment and models, HVAC model validation methodologies, and fault modeling and validation methodologies, are reviewed. The overall methodologies used for fault free and fault model development and validation are introduced. Detailed model development and validation results for the two secondary systems, i.e., fan coil unit and dual duct system are summarized. Experimental data mostly from the Iowa Energy Center Energy Resource Station are used to validate the models developed in this project. Satisfactory model performance in both fault free and fault simulation studies is observed for all studied systems.

Building and validation of a prognostic model for predicting extracorporeal circuit clotting in patients with continuous renal replacement therapy.

PubMed

Fu, Xia; Liang, Xinling; Song, Li; Huang, Huigen; Wang, Jing; Chen, Yuanhan; Zhang, Li; Quan, Zilin; Shi, Wei

2014-04-01

To develop a predictive model for circuit clotting in patients with continuous renal replacement therapy (CRRT). A total of 425 cases were selected. 302 cases were used to develop a predictive model of extracorporeal circuit life span during CRRT without citrate anticoagulation in 24 h, and 123 cases were used to validate the model. The prediction formula was developed using multivariate Cox proportional-hazards regression analysis, from which a risk score was assigned. The mean survival time of the circuit was 15.0 ± 1.3 h, and the rate of circuit clotting was 66.6 % during 24 h of CRRT. Five significant variables were assigned a predicting score according to the regression coefficient: insufficient blood flow, no anticoagulation, hematocrit ≥0.37, lactic acid of arterial blood gas analysis ≤3 mmol/L and APTT < 44.2 s. The Hosmer-Lemeshow test showed no significant difference between the predicted and actual circuit clotting (R (2) = 0.232; P = 0.301). A risk score that includes the five above-mentioned variables can be used to predict the likelihood of extracorporeal circuit clotting in patients undergoing CRRT.

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares

PubMed Central

Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future. PMID:29410849

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

PubMed

Li, Xu; Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

Development of constraint-based system-level models of microbial metabolism.

PubMed

Navid, Ali

2012-01-01

Genome-scale models of metabolism are valuable tools for using genomic information to predict microbial phenotypes. System-level mathematical models of metabolic networks have been developed for a number of microbes and have been used to gain new insights into the biochemical conversions that occur within organisms and permit their survival and proliferation. Utilizing these models, computational biologists can (1) examine network structures, (2) predict metabolic capabilities and resolve unexplained experimental observations, (3) generate and test new hypotheses, (4) assess the nutritional requirements of the organism and approximate its environmental niche, (5) identify missing enzymatic functions in the annotated genome, and (6) engineer desired metabolic capabilities in model organisms. This chapter details the protocol for developing genome-scale models of metabolism in microbes as well as tips for accelerating the model building process.

SU-D-204-06: Integration of Machine Learning and Bioinformatics Methods to Analyze Genome-Wide Association Study Data for Rectal Bleeding and Erectile Dysfunction Following Radiotherapy in Prostate Cancer

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

Oh, J; Deasy, J; Kerns, S

Purpose: We investigated whether integration of machine learning and bioinformatics techniques on genome-wide association study (GWAS) data can improve the performance of predictive models in predicting the risk of developing radiation-induced late rectal bleeding and erectile dysfunction in prostate cancer patients. Methods: We analyzed a GWAS dataset generated from 385 prostate cancer patients treated with radiotherapy. Using genotype information from these patients, we designed a machine learning-based predictive model of late radiation-induced toxicities: rectal bleeding and erectile dysfunction. The model building process was performed using 2/3 of samples (training) and the predictive model was tested with 1/3 of samples (validation).more » To identify important single nucleotide polymorphisms (SNPs), we computed the SNP importance score, resulting from our random forest regression model. We performed gene ontology (GO) enrichment analysis for nearby genes of the important SNPs. Results: After univariate analysis on the training dataset, we filtered out many SNPs with p>0.001, resulting in 749 and 367 SNPs that were used in the model building process for rectal bleeding and erectile dysfunction, respectively. On the validation dataset, our random forest regression model achieved the area under the curve (AUC)=0.70 and 0.62 for rectal bleeding and erectile dysfunction, respectively. We performed GO enrichment analysis for the top 25%, 50%, 75%, and 100% SNPs out of the select SNPs in the univariate analysis. When we used the top 50% SNPs, more plausible biological processes were obtained for both toxicities. An additional test with the top 50% SNPs improved predictive power with AUC=0.71 and 0.65 for rectal bleeding and erectile dysfunction. A better performance was achieved with AUC=0.67 when age and androgen deprivation therapy were added to the model for erectile dysfunction. Conclusion: Our approach that combines machine learning and bioinformatics techniques enabled designing better models and identifying more plausible biological processes associated with the outcomes.« less

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

NASA Prediction of Worldwide Energy Resource High Resolution Meteorology Data For Sustainable Building Design

NASA Technical Reports Server (NTRS)

Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping; Stackhouse, Paul W., Jr.

2013-01-01

A primary objective of NASA's Prediction of Worldwide Energy Resource (POWER) project is to adapt and infuse NASA's solar and meteorological data into the energy, agricultural, and architectural industries. Improvements are continuously incorporated when higher resolution and longer-term data inputs become available. Climatological data previously provided via POWER web applications were three-hourly and 1x1 degree latitude/longitude. The NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) data set provides higher resolution data products (hourly and 1/2x1/2 degree) covering the entire globe. Currently POWER solar and meteorological data are available for more than 30 years on hourly (meteorological only), daily, monthly and annual time scales. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings. A recent focus has been working with ASHRAE to assess complementing weather station data with MERRA data. ASHRAE building design parameters being investigated include heating/cooling degree days and climate zones.

Generalized role for the cerebellum in encoding internal models: evidence from semantic processing.

PubMed

Moberget, Torgeir; Gullesen, Eva Hilland; Andersson, Stein; Ivry, Richard B; Endestad, Tor

2014-02-19

The striking homogeneity of cerebellar microanatomy is strongly suggestive of a corresponding uniformity of function. Consequently, theoretical models of the cerebellum's role in motor control should offer important clues regarding cerebellar contributions to cognition. One such influential theory holds that the cerebellum encodes internal models, neural representations of the context-specific dynamic properties of an object, to facilitate predictive control when manipulating the object. The present study examined whether this theoretical construct can shed light on the contribution of the cerebellum to language processing. We reasoned that the cerebellum might perform a similar coordinative function when the context provided by the initial part of a sentence can be highly predictive of the end of the sentence. Using functional MRI in humans we tested two predictions derived from this hypothesis, building on previous neuroimaging studies of internal models in motor control. First, focal cerebellar activation-reflecting the operation of acquired internal models-should be enhanced when the linguistic context leads terminal words to be predictable. Second, more widespread activation should be observed when such predictions are violated, reflecting the processing of error signals that can be used to update internal models. Both predictions were confirmed, with predictability and prediction violations associated with increased blood oxygenation level-dependent signal in the posterior cerebellum (Crus I/II). Our results provide further evidence for cerebellar involvement in predictive language processing and suggest that the notion of cerebellar internal models may be extended to the language domain.

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Developing a Model for Identifying Students at Risk of Failure in a First Year Accounting Unit

ERIC Educational Resources Information Center

Smith, Malcolm; Therry, Len; Whale, Jacqui

2012-01-01

This paper reports on the process involved in attempting to build a predictive model capable of identifying students at risk of failure in a first year accounting unit in an Australian university. Identifying attributes that contribute to students being at risk can lead to the development of appropriate intervention strategies and support…

Predicting the Performance of Radiant Technologies in Attics: Reducing the Discrepancies Between Attic Specific and Whole-Building Energy Models

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

Merket, Noel D; DeGraw, Jason W; Lee, Edwin S

The use of radiant technology in attics aims to reduce the radiation component of heat transfer between the attic floor and roof decks, gables, and eaves. Recently, it has been shown that EnergyPlus underestimates the savings using radiant technologies in attic spaces. The aim of this study is to understand why EnergyPlus underestimates the performance of radiant technologies and provide a solution strategy that works within the current capabilities of EnergyPlus. The analysis uses three attic energy models as a baseline for comparison for EnergyPlus. Potential reasons for the discrepancies between the attic specific energy models and EnergyPlus are isolatedmore » and individually tested. A solution strategy is proposed using the Energy Management System (EMS) capabilities within EnergyPlus. This solution strategy produces similar results to the other attic specific energy models. This paper shows that the current capabilities of EnergyPlus are sufficient to simulate radiant technologies in attics. The methodology showcased in this paper serves as a guide for engineers and researchers who would like to predict the performance radiant technology in attics using the whole building energy software, EnergyPlus.« less

The benefit of 3D laser scanning technology in the generation and calibration of FEM models for health assessment of concrete structures.

PubMed

Yang, Hao; Xu, Xiangyang; Neumann, Ingo

2014-11-19

Terrestrial laser scanning technology (TLS) is a new technique for quickly getting three-dimensional information. In this paper we research the health assessment of concrete structures with a Finite Element Method (FEM) model based on TLS. The goal focuses on the benefits of 3D TLS in the generation and calibration of FEM models, in order to build a convenient, efficient and intelligent model which can be widely used for the detection and assessment of bridges, buildings, subways and other objects. After comparing the finite element simulation with surface-based measurement data from TLS, the FEM model is determined to be acceptable with an error of less than 5%. The benefit of TLS lies mainly in the possibility of a surface-based validation of results predicted by the FEM model.

Uncertainty Estimation Improves Energy Measurement and Verification Procedures

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

Walter, Travis; Price, Phillip N.; Sohn, Michael D.

2014-05-14

Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits.more » Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.« less

Confronting uncertainty in flood damage predictions

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno

2015-04-01

Reliable flood damage models are a prerequisite for the practical usefulness of the model results. Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005 and 2006, in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The reliability of the probabilistic predictions within validation runs decreases only slightly and achieves a very good coverage of observations within the predictive interval. Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Predicting wetland plant community responses to proposed water-level-regulation plans for Lake Ontario: GIS-based modeling

USGS Publications Warehouse

Wilcox, D.A.; Xie, Y.

2007-01-01

Integrated, GIS-based, wetland predictive models were constructed to assist in predicting the responses of wetland plant communities to proposed new water-level regulation plans for Lake Ontario. The modeling exercise consisted of four major components: 1) building individual site wetland geometric models; 2) constructing generalized wetland geometric models representing specific types of wetlands (rectangle model for drowned river mouth wetlands, half ring model for open embayment wetlands, half ellipse model for protected embayment wetlands, and ellipse model for barrier beach wetlands); 3) assigning wetland plant profiles to the generalized wetland geometric models that identify associations between past flooding / dewatering events and the regulated water-level changes of a proposed water-level-regulation plan; and 4) predicting relevant proportions of wetland plant communities and the time durations during which they would be affected under proposed regulation plans. Based on this conceptual foundation, the predictive models were constructed using bathymetric and topographic wetland models and technical procedures operating on the platform of ArcGIS. An example of the model processes and outputs for the drowned river mouth wetland model using a test regulation plan illustrates the four components and, when compared against other test regulation plans, provided results that met ecological expectations. The model results were also compared to independent data collected by photointerpretation. Although data collections were not directly comparable, the predicted extent of meadow marsh in years in which photographs were taken was significantly correlated with extent of mapped meadow marsh in all but barrier beach wetlands. The predictive model for wetland plant communities provided valuable input into International Joint Commission deliberations on new regulation plans and was also incorporated into faunal predictive models used for that purpose.

Multi-scale modeling of tsunami flows and tsunami-induced forces

NASA Astrophysics Data System (ADS)

Qin, X.; Motley, M. R.; LeVeque, R. J.; Gonzalez, F. I.

2016-12-01

The modeling of tsunami flows and tsunami-induced forces in coastal communities with the incorporation of the constructed environment is challenging for many numerical modelers because of the scale and complexity of the physical problem. A two-dimensional (2D) depth-averaged model can be efficient for modeling of waves offshore but may not be accurate enough to predict the complex flow with transient variance in vertical direction around constructed environments on land. On the other hand, using a more complex three-dimensional model is much more computational expensive and can become impractical due to the size of the problem and the meshing requirements near the built environment. In this study, a 2D depth-integrated model and a 3D Reynolds Averaged Navier-Stokes (RANS) model are built to model a 1:50 model-scale, idealized community, representative of Seaside, OR, USA, for which existing experimental data is available for comparison. Numerical results from the two numerical models are compared with each other as well as experimental measurement. Both models predict the flow parameters (water level, velocity, and momentum flux in the vicinity of the buildings) accurately, in general, except for time period near the initial impact, where the depth-averaged models can fail to capture the complexities in the flow. Forces predicted using direct integration of predicted pressure on structural surfaces from the 3D model and using momentum flux from the 2D model with constructed environment are compared, which indicates that force prediction from the 2D model is not always reliable in such a complicated case. Force predictions from integration of the pressure are also compared with forces predicted from bare earth momentum flux calculations to reveal the importance of incorporating the constructed environment in force prediction models.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Data Assimilation and Propagation of Uncertainty in Multiscale Cardiovascular Simulation

NASA Astrophysics Data System (ADS)

Schiavazzi, Daniele; Marsden, Alison

2015-11-01

Cardiovascular modeling is the application of computational tools to predict hemodynamics. State-of-the-art techniques couple a 3D incompressible Navier-Stokes solver with a boundary circulation model and can predict local and peripheral hemodynamics, analyze the post-operative performance of surgical designs and complement clinical data collection minimizing invasive and risky measurement practices. The ability of these tools to make useful predictions is directly related to their accuracy in representing measured physiologies. Tuning of model parameters is therefore a topic of paramount importance and should include clinical data uncertainty, revealing how this uncertainty will affect the predictions. We propose a fully Bayesian, multi-level approach to data assimilation of uncertain clinical data in multiscale circulation models. To reduce the computational cost, we use a stable, condensed approximation of the 3D model build by linear sparse regression of the pressure/flow rate relationship at the outlets. Finally, we consider the problem of non-invasively propagating the uncertainty in model parameters to the resulting hemodynamics and compare Monte Carlo simulation with Stochastic Collocation approaches based on Polynomial or Multi-resolution Chaos expansions.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy.

PubMed

Payne, Courtney E; Wolfrum, Edward J

2015-01-01

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. It is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

A Study on Active Disaster Management System for Standardized Emergency Action Plan using BIM and Flood Damage Estimation Techniques

NASA Astrophysics Data System (ADS)

Jeong, C.; Om, J.; Hwang, J.; Joo, K.; Heo, J.

2013-12-01

In recent, the frequency of extreme flood has been increasing due to climate change and global warming. Highly flood damages are mainly caused by the collapse of flood control structures such as dam and dike. In order to reduce these disasters, the disaster management system (DMS) through flood forecasting, inundation mapping, EAP (Emergency Action Plan) has been studied. The estimation of inundation damage and practical EAP are especially crucial to the DMS. However, it is difficult to predict inundation and take a proper action through DMS in real emergency situation because several techniques for inundation damage estimation are not integrated and EAP is supplied in the form of a document in Korea. In this study, the integrated simulation system including rainfall frequency analysis, rainfall-runoff modeling, inundation prediction, surface runoff analysis, and inland flood analysis was developed. Using this system coupled with standard GIS data, inundation damage can be estimated comprehensively and automatically. The standard EAP based on BIM (Building Information Modeling) was also established in this system. It is, therefore, expected that the inundation damages through this study over the entire area including buildings can be predicted and managed.

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

PubMed Central

Crayton, Elise; Wolfe, Charles; Douiri, Abdel

2018-01-01

Objective We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke. Methods Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers. The discrimination of the prediction models was measured by the area under the curve receiver operating characteristic curve or c-statistic in random effects meta-analysis. Heterogeneity was measured using I2. Appropriate appraisal tools and reporting guidelines were used in this review. Results 31395 references were screened, of which 109 articles were included in the review. These articles described 66 different predictive risk models. Appraisal identified poor methodological quality and a high risk of bias for most models. However, all models precede the development of reporting guidelines for prediction modelling studies. Generalisability of models could be improved, less than half of the included models have been externally validated(n = 27/66). 152 predictors of mortality and 192 predictors and functional outcome were identified. No studies assessing ability to improve patient outcome (model impact studies) were identified. Conclusions Further external validation and model impact studies to confirm the utility of existing models in supporting decision-making is required. Existing models have much potential. Those wishing to predict stroke outcome are advised to build on previous work, to update and adapt validated models to their specific contexts opposed to designing new ones. PMID:29377923

Locally Weighted Learning Methods for Predicting Dose-Dependent Toxicity with Application to the Human Maximum Recommended Daily Dose

DTIC Science & Technology

2012-09-10

Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States ABSTRACT: Toxicological ...species. Thus, it is more advantageous to predict the toxicological effects of a compound on humans directly from the human toxicological data of related...compounds. However, many popular quantitative structure−activity relationship ( QSAR ) methods that build a single global model by fitting all training

The Earth System Model

NASA Technical Reports Server (NTRS)

Schoeberl, Mark; Rood, Richard B.; Hildebrand, Peter; Raymond, Carol

2003-01-01

The Earth System Model is the natural evolution of current climate models and will be the ultimate embodiment of our geophysical understanding of the planet. These models are constructed from components - atmosphere, ocean, ice, land, chemistry, solid earth, etc. models and merged together through a coupling program which is responsible for the exchange of data from the components. Climate models and future earth system models will have standardized modules, and these standards are now being developed by the ESMF project funded by NASA. The Earth System Model will have a variety of uses beyond climate prediction. The model can be used to build climate data records making it the core of an assimilation system, and it can be used in OSSE experiments to evaluate. The computing and storage requirements for the ESM appear to be daunting. However, the Japanese ES theoretical computing capability is already within 20% of the minimum requirements needed for some 2010 climate model applications. Thus it seems very possible that a focused effort to build an Earth System Model will achieve succcss.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Physics-based Space Weather Forecasting in the Project for Solar-Terrestrial Environment Prediction (PSTEP) in Japan

NASA Astrophysics Data System (ADS)

Kusano, K.

2016-12-01

Project for Solar-Terrestrial Environment Prediction (PSTEP) is a Japanese nation-wide research collaboration, which was recently launched. PSTEP aims to develop a synergistic interaction between predictive and scientific studies of the solar-terrestrial environment and to establish the basis for next-generation space weather forecasting using the state-of-the-art observation systems and the physics-based models. For this project, we coordinate the four research groups, which develop (1) the integration of space weather forecast system, (2) the physics-based solar storm prediction, (3) the predictive models of magnetosphere and ionosphere dynamics, and (4) the model of solar cycle activity and its impact on climate, respectively. In this project, we will build the coordinated physics-based model to answer the fundamental questions concerning the onset of solar eruptions and the mechanism for radiation belt dynamics in the Earth's magnetosphere. In this paper, we will show the strategy of PSTEP, and discuss about the role and prospect of the physics-based space weather forecasting system being developed by PSTEP.

Sensitivity of Hypoxia Predictions for the Northern Gulf of Mexico to Sediment Oxygen Consumption and Model Nesting

NASA Astrophysics Data System (ADS)

Fennel, Katja; Hu, Jiatang; Laurent, Arnaud; Marta-Almeida, Martinho; Hetland, Robert

2014-05-01

Interannual variations of the hypoxic area that develops every summer over the Texas-Louisiana Shelf are large. The 2008 Action Plan put forth by an alliance of multiple state and federal agencies and tribes calls for a decrease of the hypoxic area through nutrient management in the watershed. Realistic models help build mechanistic understanding of the processes underlying hypoxia formation and are thus indispensable for devising efficient nutrient reduction strategies. Here we present such a model, evaluate its hypoxia predictions against monitoring observations and assess the sensitivity of hypoxia predictions to model resolution, variations in sediment oxygen consumption and choice of physical horizontal boundary conditions. We find that hypoxia predictions on the shelf are very sensitive to the parameterization of sediment oxygen consumption, a result of the fact that hypoxic conditions are restricted to a relatively thin layer above the bottom over most of the shelf. We also show that the strength of vertical stratification is an important predictor of oxygen concentration in bottom waters and that modification of physical horizontal boundary conditions can have a large effect on hypoxia predictions.

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

Electron-Ion Dynamics with Time-Dependent Density Functional Theory: Towards Predictive Solar Cell Modeling: Final Technical Report

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

Maitra, Neepa

2016-07-14

This project investigates the accuracy of currently-used functionals in time-dependent density functional theory, which is today routinely used to predict and design materials and computationally model processes in solar energy conversion. The rigorously-based electron-ion dynamics method developed here sheds light on traditional methods and overcomes challenges those methods have. The fundamental research undertaken here is important for building reliable and practical methods for materials discovery. The ultimate goal is to use these tools for the computational design of new materials for solar cell devices of high efficiency.

Application of the Software as a Service Model to the Control of Complex Building Systems

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

Stadler, Michael; Donadee, Jonathan; Marnay, Chris

2011-03-17

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building.more » The SaaS interface, known as WebOpt, consists of two major parts: a) the investment& planning and b) the operations module, which builds on the investment& planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analysed.« less

Application of the Software as a Service Model to the Control of Complex Building Systems

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

Stadler, Michael; Donadee, Jon; Marnay, Chris

2011-03-18

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building.more » The SaaS interface, known as WebOpt, consists of two major parts: a) the investment& planning and b) the operations module, which builds on the investment& planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analyzed.« less

Emergence of intercolonial variation in termite shelter tube patterns and prediction of its underlying mechanism

PubMed Central

2015-01-01

Building behaviours occur in various organisms from bacteria to humans. Social insects build various structures such as large nests and underground galleries, achieved by self-organization. Structures built by social insects have recently been demonstrated to vary widely in size and shape within a species, even under the same environmental conditions. However, little is known about how intraspecific variation in structures emerges from collective behaviours. Here we show that the colony variation of structures can be generated by simply changing two behavioural parameters of group members, even with the same building algorithm. Our laboratory experiment of termite shelter tube construction demonstrated clear intercolonial variation, and a two-dimensional lattice model showed that it can be attributed to the extent of positive feedback and the number of individuals engaged in building. This study contributes to explaining the great diversity of structures emerging from collective building in social insects. PMID:26715997

Sonic-boom-induced building structure responses including damage.

NASA Technical Reports Server (NTRS)

Clarkson, B. L.; Mayes, W. H.

1972-01-01

Concepts of sonic-boom pressure loading of building structures and the associated responses are reviewed, and results of pertinent theoretical and experimental research programs are summarized. The significance of sonic-boom load time histories, including waveshape effects, are illustrated with the aid of simple structural elements such as beams and plates. Also included are discussions of the significance of such other phenomena as three-dimensional loading effects, air cavity coupling, multimodal responses, and structural nonlinearities. Measured deflection, acceleration, and strain data from laboratory models and full-scale building tests are summarized, and these data are compared, where possible, with predicted values. Damage complaint and claim experience due both to controlled and uncontrolled supersonic flights over communities are summarized with particular reference to residential, commercial, and historic buildings. Sonic-boom-induced building responses are compared with those from other impulsive loadings due to natural and cultural events and from laboratory simulation tests.

The prediction of engineering cost for green buildings based on information entropy

NASA Astrophysics Data System (ADS)

Liang, Guoqiang; Huang, Jinglian

2018-03-01

Green building is the developing trend in the world building industry. Additionally, construction costs are an essential consideration in building constructions. Therefore, it is necessary to investigate the problems of cost prediction in green building. On the basis of analyzing the cost of green building, this paper proposes the forecasting method of actual cost in green building based on information entropy and provides the forecasting working procedure. Using the probability density obtained from statistical data, such as labor costs, material costs, machinery costs, administration costs, profits, risk costs a unit project quotation and etc., situations can be predicted which lead to cost variations between budgeted cost and actual cost in constructions, through estimating the information entropy of budgeted cost and actual cost. The research results of this article have a practical significance in cost control of green building. Additionally, the method proposed in this article can be generalized and applied to a variety of other aspects in building management.

Modeling of fire smoke movement in multizone garments building using two open source platforms

NASA Astrophysics Data System (ADS)

Khandoker, Md. Arifur Rahman; Galib, Musanna; Islam, Adnan; Rahman, Md. Ashiqur

2017-06-01

Casualty of garment factory workers from factory fire in Bangladesh is a recurring tragedy. Smoke, which is more fatal than fire itself, often propagates through different pathways from lower to upper floors during building fire. Among the toxic gases produced from a building fire, carbon monoxide (CO) can be deadly, even in small amounts. This paper models the propagation and transportation of fire induced smoke (CO) that resulted from the burning of synthetic polyester fibers using two open source platforms, CONTAM and Fire Dynamics Simulator (FDS). Smoke migration in a generic multistoried garment factory building in Bangladesh is modeled using CONTAM where each floor is compartmentalized by different zones. The elevator and stairway shafts are modeled by phantom zones to simulate contaminant (CO) transport from one floor to upper floors. FDS analysis involves burning of two different stacks of polyester jacket of six feet height and with a maximum heat release rate per unit area of 1500kw/m2 over a storage area 50m2 and 150m2, respectively. The resulting CO generation and removal rates from FDS are used in CONTAM to predict fire-borne CO propagation in different zones of the garment building. Findings of the study exhibit that the contaminant flow rate is a strong function of the position of building geometry, location of initiation of fire, amount of burnt material, presence of AHU and contaminant generation and removal rate of CO from the source location etc. The transport of fire-smoke in the building Hallways, stairways and lifts are also investigated in detail to examine the safe egress of the occupants in case of fire.

Quantitative structure-activity relationship models that stand the test of time.

PubMed

Davis, Andrew M; Wood, David J

2013-04-01

The pharmaceutical industry is in a period of intense change. While this has many drivers, attrition through the development process continues to be an important pressure. The emerging definitions of "compound quality" that are based on retrospective analyses of developmental attrition have highlighted a new direction for medicinal chemistry and the paradigm of "quality at the point of design". The time has come for retrospective analyses to catalyze prospective action. Quality at the point of design places pressure on the quality of our predictive models. Empirical QSAR models when built with care provide true predictive control, but their accuracy and precision can be improved. Here we describe AstraZeneca's experience of automation in QSAR model building and validation, and how an informatics system can provide a step-change in predictive power to project design teams, if they choose to use it.

Chill Down Process of Hydrogen Transport Pipelines

NASA Technical Reports Server (NTRS)

Mei, Renwei; Klausner, James

2006-01-01

A pseudo-steady model has been developed to predict the chilldown history of pipe wall temperature in the horizontal transport pipeline for cryogenic fluids. A new film boiling heat transfer model is developed by incorporating the stratified flow structure for cryogenic chilldown. A modified nucleate boiling heat transfer correlation for cryogenic chilldown process inside a horizontal pipe is proposed. The efficacy of the correlations is assessed by comparing the model predictions with measured values of wall temperature in several azimuthal positions in a well controlled experiment by Chung et al. (2004). The computed pipe wall temperature histories match well with the measured results. The present model captures important features of thermal interaction between the pipe wall and the cryogenic fluid, provides a simple and robust platform for predicting pipe wall chilldown history in long horizontal pipe at relatively low computational cost, and builds a foundation to incorporate the two-phase hydrodynamic interaction in the chilldown process.

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C.

PubMed

Kurosaki, Masayuki; Hiramatsu, Naoki; Sakamoto, Minoru; Suzuki, Yoshiyuki; Iwasaki, Manabu; Tamori, Akihiro; Matsuura, Kentaro; Kakinuma, Sei; Sugauchi, Fuminaka; Sakamoto, Naoya; Nakagawa, Mina; Izumi, Namiki

2012-03-01

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC. Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy). On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040). The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC. Copyright © 2011 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.