An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent

OpenAIRE

Bo Sun; Qiang Feng; Songjie Li

2012-01-01

According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules) based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negoti...

Search-based model identification of smart-structure damage

Science.gov (United States)

Glass, B. J.; Macalou, A.

1991-01-01

This paper describes the use of a combined model and parameter identification approach, based on modal analysis and artificial intelligence (AI) techniques, for identifying damage or flaws in a rotating truss structure incorporating embedded piezoceramic sensors. This smart structure example is representative of a class of structures commonly found in aerospace systems and next generation space structures. Artificial intelligence techniques of classification, heuristic search, and an object-oriented knowledge base are used in an AI-based model identification approach. A finite model space is classified into a search tree, over which a variant of best-first search is used to identify the model whose stored response most closely matches that of the input. Newly-encountered models can be incorporated into the model space. This adaptativeness demonstrates the potential for learning control. Following this output-error model identification, numerical parameter identification is used to further refine the identified model. Given the rotating truss example in this paper, noisy data corresponding to various damage configurations are input to both this approach and a conventional parameter identification method. The combination of the AI-based model identification with parameter identification is shown to lead to smaller parameter corrections than required by the use of parameter identification alone.

Multi-Domain Modeling Based on Modelica

Directory of Open Access Journals (Sweden)

Liu Jun

2016-01-01

Full Text Available With the application of simulation technology in large-scale and multi-field problems, multi-domain unified modeling become an effective way to solve these problems. This paper introduces several basic methods and advantages of the multidisciplinary model, and focuses on the simulation based on Modelica language. The Modelica/Mworks is a newly developed simulation software with features of an object-oriented and non-casual language for modeling of the large, multi-domain system, which makes the model easier to grasp, develop and maintain.It This article shows the single degree of freedom mechanical vibration system based on Modelica language special connection mechanism in Mworks. This method that multi-domain modeling has simple and feasible, high reusability. it closer to the physical system, and many other advantages.

Health condition identification of multi-stage planetary gearboxes using a mRVM-based method

Science.gov (United States)

Lei, Yaguo; Liu, Zongyao; Wu, Xionghui; Li, Naipeng; Chen, Wu; Lin, Jing

2015-08-01

Multi-stage planetary gearboxes are widely applied in aerospace, automotive and heavy industries. Their key components, such as gears and bearings, can easily suffer from damage due to tough working environment. Health condition identification of planetary gearboxes aims to prevent accidents and save costs. This paper proposes a method based on multiclass relevance vector machine (mRVM) to identify health condition of multi-stage planetary gearboxes. In this method, a mRVM algorithm is adopted as a classifier, and two features, i.e. accumulative amplitudes of carrier orders (AACO) and energy ratio based on difference spectra (ERDS), are used as the input of the classifier to classify different health conditions of multi-stage planetary gearboxes. To test the proposed method, seven health conditions of a two-stage planetary gearbox are considered and vibration data is acquired from the planetary gearbox under different motor speeds and loading conditions. The results of three tests based on different data show that the proposed method obtains an improved identification performance and robustness compared with the existing method.

HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS

Energy Technology Data Exchange (ETDEWEB)

Gupta, Ravi R.; Kuhlmann, Steve; Kovacs, Eve; Spinka, Harold; Kessler, Richard; Goldstein, Daniel A.; Liotine, Camille; Pomian, Katarzyna; D’Andrea, Chris B.; Sullivan, Mark; Carretero, Jorge; Castander, Francisco J.; Nichol, Robert C.; Finley, David A.; Fischer, John A.; Foley, Ryan J.; Kim, Alex G.; Papadopoulos, Andreas; Sako, Masao; Scolnic, Daniel M.; Smith, Mathew; Tucker, Brad E.; Uddin, Syed; Wolf, Rachel C.; Yuan, Fang; Abbott, Tim M. C.; Abdalla, Filipe B.; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Rosell, Aurelio Carnero; Kind, Matias Carrasco; Cunha, Carlos E.; Costa, Luiz N. da; Desai, Shantanu; Doel, Peter; Eifler, Tim F.; Evrard, August E.; Flaugher, Brenna; Fosalba, Pablo; Gaztañaga, Enrique; Gruen, Daniel; Gruendl, Robert; James, David J.; Kuehn, Kyler; Kuropatkin, Nikolay; Maia, Marcio A. G.; Marshall, Jennifer L.; Miquel, Ramon; Plazas, Andrés A.; Romer, A. Kathy; Sánchez, Eusebio; Schubnell, Michael; Sevilla-Noarbe, Ignacio; Sobreira, Flávia; Suchyta, Eric; Swanson, Molly E. C.; Tarle, Gregory; Walker, Alistair R.; Wester, William

2016-11-08

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS

Energy Technology Data Exchange (ETDEWEB)

Gupta, Ravi R.; Kuhlmann, Steve; Kovacs, Eve; Spinka, Harold; Liotine, Camille; Pomian, Katarzyna [Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439 (United States); Kessler, Richard; Scolnic, Daniel M. [Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637 (United States); Goldstein, Daniel A. [Department of Astronomy, University of California, Berkeley, 501 Campbell Hall #3411, Berkeley, CA 94720 (United States); D’Andrea, Chris B.; Nichol, Robert C.; Papadopoulos, Andreas [Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, PO1 3FX (United Kingdom); Sullivan, Mark [Department of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ (United Kingdom); Carretero, Jorge; Castander, Francisco J. [Institut de Ciències de l’Espai, IEEC-CSIC, Campus UAB, Carrer de Can Magrans, s/n, E-08193 Bellaterra, Barcelona (Spain); Finley, David A. [Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510 (United States); Fischer, John A.; Sako, Masao [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Foley, Ryan J. [Department of Astronomy, University of Illinois, 1002 W. Green Street, Urbana, IL 61801 (United States); Kim, Alex G., E-mail: raviryan@gmail.com [Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720 (United States); and others

2016-12-01

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate “hostless” SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey.

CEAI: CCM-based email authorship identification model

Directory of Open Access Journals (Sweden)

Sarwat Nizamani

2013-11-01

Full Text Available In this paper we present a model for email authorship identification (EAI by employing a Cluster-based Classification (CCM technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature set to include some more interesting and effective features for email authorship identification (e.g., the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell. We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM-based models, as well as the models proposed by Iqbal et al. (2010, 2013 [1,2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors’ constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1,2].

CEAI: CCM based Email Authorship Identification Model

DEFF Research Database (Denmark)

Nizamani, Sarwat; Memon, Nasrullah

2013-01-01

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some...... more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based...... reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10...

Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury.

Science.gov (United States)

Benoussaad, Mourad; Poignet, Philippe; Hayashibe, Mitsuhiro; Azevedo-Coste, Christine; Fattal, Charles; Guiraud, David

2013-06-01

We investigated the parameter identification of a multi-scale physiological model of skeletal muscle, based on Huxley's formulation. We focused particularly on the knee joint controlled by quadriceps muscles under electrical stimulation (ES) in subjects with a complete spinal cord injury. A noninvasive and in vivo identification protocol was thus applied through surface stimulation in nine subjects and through neural stimulation in one ES-implanted subject. The identification protocol included initial identification steps, which are adaptations of existing identification techniques to estimate most of the parameters of our model. Then we applied an original and safer identification protocol in dynamic conditions, which required resolution of a nonlinear programming (NLP) problem to identify the serial element stiffness of quadriceps. Each identification step and cross validation of the estimated model in dynamic condition were evaluated through a quadratic error criterion. The results highlighted good accuracy, the efficiency of the identification protocol and the ability of the estimated model to predict the subject-specific behavior of the musculoskeletal system. From the comparison of parameter values between subjects, we discussed and explored the inter-subject variability of parameters in order to select parameters that have to be identified in each patient.

Multi-cracks identification based on the nonlinear vibration response of beams subjected to moving harmonic load

Directory of Open Access Journals (Sweden)

Chouiyakh H.

2016-01-01

Full Text Available The aim of this work is to investigate the nonlinear forced vibration of beams containing an arbitrary number of cracks and to perform a multi-crack identification procedure based on the obtained signals. Cracks are assumed to be open and modelled trough rotational springs linking two adjacent sub-beams. Forced vibration analysis is performed by a developed time differential quadrature method. The obtained nonlinear vibration responses are analyzed by Huang Hilbert Transform. The instantaneous frequency is used as damage index tool for cracks detection.

Ship Detection in Optical Remote Sensing Images Based on Wavelet Transform and Multi-Level False Alarm Identification

Directory of Open Access Journals (Sweden)

Fang Xu

2017-09-01

Full Text Available Ship detection by Unmanned Airborne Vehicles (UAVs and satellites plays an important role in a spectrum of related military and civil applications. To improve the detection efficiency, accuracy, and speed, a novel ship detection method from coarse to fine is presented. Ship targets are viewed as uncommon regions in the sea background caused by the differences in colors, textures, shapes, or other factors. Inspired by this fact, a global saliency model is constructed based on high-frequency coefficients of the multi-scale and multi-direction wavelet decomposition, which can characterize different feature information from edge to texture of the input image. To further reduce the false alarms, a new and effective multi-level discrimination method is designed based on the improved entropy and pixel distribution, which is robust against the interferences introduced by islands, coastlines, clouds, and shadows. The experimental results on optical remote sensing images validate that the presented saliency model outperforms the comparative models in terms of the area under the receiver operating characteristic curves core and the accuracy in the images with different sizes. After the target identification, the locations and the number of the ships in various sizes and colors can be detected accurately and fast with high robustness.

Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution

International Nuclear Information System (INIS)

Gong, Wenyin; Cai, Zhihua

2013-01-01

Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

Science.gov (United States)

Zhong, Chongquan; Lin, Yaoyao

2017-11-01

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

Geo-Parcel Based Crop Identification by Integrating High Spatial-Temporal Resolution Imagery from Multi-Source Satellite Data

Directory of Open Access Journals (Sweden)

Yingpin Yang

2017-12-01

Full Text Available Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

Rudder Based Roll Control via host-computer of A Robotic Boat

OpenAIRE

Bao, Xinping; Yu, Zhenyu; Nonami, Kenzo

2009-01-01

Rudder based roll control of a small-sized robotic boat is a key technique for the devices on board to achieve good performance. This paper introduces a host-based robotic boat capable of performing basic movement operations. The course keeping and roll reduction are studied via rudder based method in simulations and sea trials. The boat dynamic model is built with the combination of mathematical analysis and system identification technique. A mixed sensitivity H control method design is sele...

Multi-scale Material Parameter Identification Using LS-DYNA® and LS-OPT®

Energy Technology Data Exchange (ETDEWEB)

Stander, Nielen; Basudhar, Anirban; Basu, Ushnish; Gandikota, Imtiaz; Savic, Vesna; Sun, Xin; Choi, Kyoo Sil; Hu, Xiaohua; Pourboghrat, F.; Park, Taejoon; Mapar, Aboozar; Kumar, Shavan; Ghassemi-Armaki, Hassan; Abu-Farha, Fadi

2015-09-14

Test Ban Treaty of 1996 which banned surface testing of nuclear devices [1]. This had the effect that experimental work was reduced from large scale tests to multiscale experiments to provide material models with validation at different length scales. In the subsequent years industry realized that multi-scale modeling and simulation-based design were transferable to the design optimization of any structural system. Horstemeyer [1] lists a number of advantages of the use of multiscale modeling. Among these are: the reduction of product development time by alleviating costly trial-and-error iterations as well as the reduction of product costs through innovations in material, product and process designs. Multi-scale modeling can reduce the number of costly large scale experiments and can increase product quality by providing more accurate predictions. Research tends to be focussed on each particular length scale, which enhances accuracy in the long term. This paper serves as an introduction to the LS-OPT and LS-DYNA methodology for multi-scale modeling. It mainly focuses on an approach to integrate material identification using material models of different length scales. As an example, a multi-scale material identification strategy, consisting of a Crystal Plasticity (CP) material model and a homogenized State Variable (SV) model, is discussed and the parameter identification of the individual material models of different length scales is demonstrated. The paper concludes with thoughts on integrating the multi-scale methodology into the overall vehicle design.

[A model for multi-source feedback in postgraduate medical education based on validation and best practise].

Science.gov (United States)

Eriksen, Gitte Valsted; Malling, Bente

2014-04-14

In Denmark multi-source feedback is used in formative assessment of trainees' performance regarding the roles: communicator, collaborator, professional and manager. A web-based model was developed and evaluated useful, time-effective, acceptable and feasible. The model comprises a validated questionnaire usable in all specialities, personal feedback from an educated feedback facilitator, identification of areas for improvement and a mandatory written plan for the trainees' further professional development. The model is implemented at all hospitals in the Northern Educational Region in Denmark.

A physiologically based nonhomogeneous Poisson counter model of visual identification

DEFF Research Database (Denmark)

Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus

2018-01-01

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are ......A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects...... that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model...

Research on Error Modelling and Identification of 3 Axis NC Machine Tools Based on Cross Grid Encoder Measurement

International Nuclear Information System (INIS)

Du, Z C; Lv, C F; Hong, M S

2006-01-01

A new error modelling and identification method based on the cross grid encoder is proposed in this paper. Generally, there are 21 error components in the geometric error of the 3 axis NC machine tools. However according our theoretical analysis, the squareness error among different guide ways affects not only the translation error component, but also the rotational ones. Therefore, a revised synthetic error model is developed. And the mapping relationship between the error component and radial motion error of round workpiece manufactured on the NC machine tools are deduced. This mapping relationship shows that the radial error of circular motion is the comprehensive function result of all the error components of link, worktable, sliding table and main spindle block. Aiming to overcome the solution singularity shortcoming of traditional error component identification method, a new multi-step identification method of error component by using the Cross Grid Encoder measurement technology is proposed based on the kinematic error model of NC machine tool. Firstly, the 12 translational error components of the NC machine tool are measured and identified by using the least square method (LSM) when the NC machine tools go linear motion in the three orthogonal planes: XOY plane, XOZ plane and YOZ plane. Secondly, the circular error tracks are measured when the NC machine tools go circular motion in the same above orthogonal planes by using the cross grid encoder Heidenhain KGM 182. Therefore 9 rotational errors can be identified by using LSM. Finally the experimental validation of the above modelling theory and identification method is carried out in the 3 axis CNC vertical machining centre Cincinnati 750 Arrow. The entire 21 error components have been successfully measured out by the above method. Research shows the multi-step modelling and identification method is very suitable for 'on machine measurement'

Modeling Multi-wavelength Stellar Astrometry. III. Determination of the Absolute Masses of Exoplanets and Their Host Stars

Science.gov (United States)

Coughlin, J. L.; López-Morales, Mercedes

2012-05-01

Astrometric measurements of stellar systems are becoming significantly more precise and common, with many ground- and space-based instruments and missions approaching 1 μas precision. We examine the multi-wavelength astrometric orbits of exoplanetary systems via both analytical formulae and numerical modeling. Exoplanets have a combination of reflected and thermally emitted light that causes the photocenter of the system to shift increasingly farther away from the host star with increasing wavelength. We find that, if observed at long enough wavelengths, the planet can dominate the astrometric motion of the system, and thus it is possible to directly measure the orbits of both the planet and star, and thus directly determine the physical masses of the star and planet, using multi-wavelength astrometry. In general, this technique works best for, though is certainly not limited to, systems that have large, high-mass stars and large, low-mass planets, which is a unique parameter space not covered by other exoplanet characterization techniques. Exoplanets that happen to transit their host star present unique cases where the physical radii of the planet and star can be directly determined via astrometry alone. Planetary albedos and day-night contrast ratios may also be probed via this technique due to the unique signature they impart on the observed astrometric orbits. We develop a tool to examine the prospects for near-term detection of this effect, and give examples of some exoplanets that appear to be good targets for detection in the K to N infrared observing bands, if the required precision can be achieved.

MODELING MULTI-WAVELENGTH STELLAR ASTROMETRY. III. DETERMINATION OF THE ABSOLUTE MASSES OF EXOPLANETS AND THEIR HOST STARS

International Nuclear Information System (INIS)

Coughlin, J. L.; López-Morales, Mercedes

2012-01-01

Astrometric measurements of stellar systems are becoming significantly more precise and common, with many ground- and space-based instruments and missions approaching 1 μas precision. We examine the multi-wavelength astrometric orbits of exoplanetary systems via both analytical formulae and numerical modeling. Exoplanets have a combination of reflected and thermally emitted light that causes the photocenter of the system to shift increasingly farther away from the host star with increasing wavelength. We find that, if observed at long enough wavelengths, the planet can dominate the astrometric motion of the system, and thus it is possible to directly measure the orbits of both the planet and star, and thus directly determine the physical masses of the star and planet, using multi-wavelength astrometry. In general, this technique works best for, though is certainly not limited to, systems that have large, high-mass stars and large, low-mass planets, which is a unique parameter space not covered by other exoplanet characterization techniques. Exoplanets that happen to transit their host star present unique cases where the physical radii of the planet and star can be directly determined via astrometry alone. Planetary albedos and day-night contrast ratios may also be probed via this technique due to the unique signature they impart on the observed astrometric orbits. We develop a tool to examine the prospects for near-term detection of this effect, and give examples of some exoplanets that appear to be good targets for detection in the K to N infrared observing bands, if the required precision can be achieved.

Damage detection in multi-span beams based on the analysis of frequency changes

International Nuclear Information System (INIS)

Gillich, G R; Ntakpe, J L; Praisach, Z I; Mimis, M C; Abdel Wahab, M

2017-01-01

Crack identification in multi-span beams is performed to determine whether the structure is healthy or not. Among all crack identification methods, these based on measured natural frequency changes present the advantage of simplicity and easy to use in practical engineering. To accurately identify the cracks characteristics for multi-span beam structure, a mathematical model is established, which can predict frequency changes for any boundary conditions, the intermediate supports being hinges. This relation is based on the modal strain energy concept. Since frequency changes are relative small, to obtain natural frequencies with high resolution, a signal processing algorithm based on superposing of numerous spectra is also proposed, which overcomes the disadvantage of Fast Fourier Transform in the aspect of frequency resolution. Based on above-mentioned mathematical model and signal processing algorithm, the method of identifying cracks on multi-span beams is presented. To verify the accuracy of this identification method, experimental examples are conducted on a two-span structure. The results demonstrate that the method proposed in this paper can accurately identify the crack position and depth. (paper)

Research on Multi - Person Parallel Modeling Method Based on Integrated Model Persistent Storage

Science.gov (United States)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper mainly studies the multi-person parallel modeling method based on the integrated model persistence storage. The integrated model refers to a set of MDDT modeling graphics system, which can carry out multi-angle, multi-level and multi-stage description of aerospace general embedded software. Persistent storage refers to converting the data model in memory into a storage model and converting the storage model into a data model in memory, where the data model refers to the object model and the storage model is a binary stream. And multi-person parallel modeling refers to the need for multi-person collaboration, the role of separation, and even real-time remote synchronization modeling.

Pollutant source identification model for water pollution incidents in small straight rivers based on genetic algorithm

Science.gov (United States)

Zhang, Shou-ping; Xin, Xiao-kang

2017-07-01

Identification of pollutant sources for river pollution incidents is an important and difficult task in the emergency rescue, and an intelligent optimization method can effectively compensate for the weakness of traditional methods. An intelligent model for pollutant source identification has been established using the basic genetic algorithm (BGA) as an optimization search tool and applying an analytic solution formula of one-dimensional unsteady water quality equation to construct the objective function. Experimental tests show that the identification model is effective and efficient: the model can accurately figure out the pollutant amounts or positions no matter single pollution source or multiple sources. Especially when the population size of BGA is set as 10, the computing results are sound agree with analytic results for a single source amount and position identification, the relative errors are no more than 5 %. For cases of multi-point sources and multi-variable, there are some errors in computing results for the reasons that there exist many possible combinations of the pollution sources. But, with the help of previous experience to narrow the search scope, the relative errors of the identification results are less than 5 %, which proves the established source identification model can be used to direct emergency responses.

Simulation, identification and statistical variation in cardiovascular analysis (SISCA) - A software framework for multi-compartment lumped modeling.

Science.gov (United States)

Huttary, Rudolf; Goubergrits, Leonid; Schütte, Christof; Bernhard, Stefan

2017-08-01

It has not yet been possible to obtain modeling approaches suitable for covering a wide range of real world scenarios in cardiovascular physiology because many of the system parameters are uncertain or even unknown. Natural variability and statistical variation of cardiovascular system parameters in healthy and diseased conditions are characteristic features for understanding cardiovascular diseases in more detail. This paper presents SISCA, a novel software framework for cardiovascular system modeling and its MATLAB implementation. The framework defines a multi-model statistical ensemble approach for dimension reduced, multi-compartment models and focuses on statistical variation, system identification and patient-specific simulation based on clinical data. We also discuss a data-driven modeling scenario as a use case example. The regarded dataset originated from routine clinical examinations and comprised typical pre and post surgery clinical data from a patient diagnosed with coarctation of aorta. We conducted patient and disease specific pre/post surgery modeling by adapting a validated nominal multi-compartment model with respect to structure and parametrization using metadata and MRI geometry. In both models, the simulation reproduced measured pressures and flows fairly well with respect to stenosis and stent treatment and by pre-treatment cross stenosis phase shift of the pulse wave. However, with post-treatment data showing unrealistic phase shifts and other more obvious inconsistencies within the dataset, the methods and results we present suggest that conditioning and uncertainty management of routine clinical data sets needs significantly more attention to obtain reasonable results in patient-specific cardiovascular modeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

Science.gov (United States)

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Metamodel-based inverse method for parameter identification: elastic-plastic damage model

Science.gov (United States)

Huang, Changwu; El Hami, Abdelkhalak; Radi, Bouchaïb

2017-04-01

This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic-plastic damage model parameter identification. An elastic-plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic-plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.

A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals.

Science.gov (United States)

Herrgård, Markus; Sukumara, Sumesh; Campodonico, Miguel; Zhuang, Kai

2015-12-01

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry. © 2015 Authors; published by Portland Press Limited.

Identification of the host determinant of two prolate-headed phages infecting lactococcus lactis

International Nuclear Information System (INIS)

Stuer-Lauridsen, Birgitte; Janzen, Thomas; Schnabl, Jannie; Johansen, Eric

2003-01-01

A gene responsible for host determination was identified in two prolate-headed bacteriophages of the c2 species infecting strains of Lactococcus lactis. The identification of the host determinant gene was based on low DNA sequence homology in a specific open reading frame (ORF) between prolate-headed phages with different host ranges. When a host carrying this ORF from one phage on a plasmid was infected with another phage, we obtained phages with an altered host range at a frequency of 10 -6 to 10 -7 . Sequencing of phage DNA originating from 10 independent single plaques confirmed that a genetic recombination had taken place at different positions between the ORF on the plasmid and the infecting phage. The adsorption of the recombinant phages to their bacterial hosts had also changed to match the phage origin of the ORF. Consequently, it is concluded that this ORF codes for the host range determinant

Model identification methodology for fluid-based inerters

Science.gov (United States)

Liu, Xiaofu; Jiang, Jason Zheng; Titurus, Branislav; Harrison, Andrew

2018-06-01

Inerter is the mechanical dual of the capacitor via the force-current analogy. It has the property that the force across the terminals is proportional to their relative acceleration. Compared with flywheel-based inerters, fluid-based forms have advantages of improved durability, inherent damping and simplicity of design. In order to improve the understanding of the physical behaviour of this fluid-based device, especially caused by the hydraulic resistance and inertial effects in the external tube, this work proposes a comprehensive model identification methodology. Firstly, a modelling procedure is established, which allows the topological arrangement of the mechanical networks to be obtained by mapping the damping, inertance and stiffness effects directly to their respective hydraulic counterparts. Secondly, an experimental sequence is followed, which separates the identification of friction, stiffness and various damping effects. Furthermore, an experimental set-up is introduced, where two pressure gauges are used to accurately measure the pressure drop across the external tube. The theoretical models with improved confidence are obtained using the proposed methodology for a helical-tube fluid inerter prototype. The sources of remaining discrepancies are further analysed.

Experimental Damage Identification of a Model Reticulated Shell

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Jing Xu

2017-04-01

Full Text Available The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs, the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs were then established and were trained using the Levenberg–Marquardt algorithm (L–M algorithm. The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

Multi-scale Material Parameter Identification Using LS-DYNA® and LS-OPT®

Energy Technology Data Exchange (ETDEWEB)

Stander, Nielen [Livermore Software Technology Corporation, CA (United States); Basudhar, Anirban [Livermore Software Technology Corporation, CA (United States); Basu, Ushnish [Livermore Software Technology Corporation, CA (United States); Gandikota, Imtiaz [Livermore Software Technology Corporation, CA (United States); Savic, Vesna [General Motors, Flint, MI (United States); Sun, Xin [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Hu, XiaoHua [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Pourboghrat, Farhang [The Ohio State Univ., Columbus, OH (United States); Park, Taejoon [The Ohio State Univ., Columbus, OH (United States); Mapar, Aboozar [Michigan State Univ., East Lansing, MI (United States); Kumar, Sharvan [Brown Univ., Providence, RI (United States); Ghassemi-Armaki, Hassan [Brown Univ., Providence, RI (United States); Abu-Farha, Fadi [Clemson Univ., SC (United States)

2015-06-15

Ever-tightening regulations on fuel economy and carbon emissions demand continual innovation in finding ways for reducing vehicle mass. Classical methods for computational mass reduction include sizing, shape and topology optimization. One of the few remaining options for weight reduction can be found in materials engineering and material design optimization. Apart from considering different types of materials by adding material diversity, an appealing option in automotive design is to engineer steel alloys for the purpose of reducing thickness while retaining sufficient strength and ductility required for durability and safety. Such a project was proposed and is currently being executed under the auspices of the United States Automotive Materials Partnership (USAMP) funded by the Department of Energy. Under this program, new steel alloys (Third Generation Advanced High Strength Steel or 3GAHSS) are being designed, tested and integrated with the remaining design variables of a benchmark vehicle Finite Element model. In this project the principal phases identified are (i) material identification, (ii) formability optimization and (iii) multi-disciplinary vehicle optimization. This paper serves as an introduction to the LS-OPT methodology and therefore mainly focuses on the first phase, namely an approach to integrate material identification using material models of different length scales. For this purpose, a multi-scale material identification strategy, consisting of a Crystal Plasticity (CP) material model and a Homogenized State Variable (SV) model, is discussed and demonstrated. The paper concludes with proposals for integrating the multi-scale methodology into the overall vehicle design.

An Intelligent Fleet Condition-Based Maintenance Decision Making Method Based on Multi-Agent

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Bo Sun

2012-01-01

Full Text Available According to the demand for condition-based maintenance online decision making among a mission oriented fleet, an intelligent maintenance decision making method based on Multi-agent and heuristic rules is proposed. The process of condition-based maintenance within an aircraft fleet (each containing one or more Line Replaceable Modules based on multiple maintenance thresholds is analyzed. Then the process is abstracted into a Multi-Agent Model, a 2-layer model structure containing host negotiation and independent negotiation is established, and the heuristic rules applied to global and local maintenance decision making is proposed. Based on Contract Net Protocol and the heuristic rules, the maintenance decision making algorithm is put forward. Finally, a fleet consisting of 10 aircrafts on a 3-wave continuous mission is illustrated to verify this method. Simulation results indicate that this method can improve the availability of the fleet, meet mission demands, rationalize the utilization of support resources and provide support for online maintenance decision making among a mission oriented fleet.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

Science.gov (United States)

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

The design of multi-core DSP parallel model based on message passing and multi-level pipeline

Science.gov (United States)

Niu, Jingyu; Hu, Jian; He, Wenjing; Meng, Fanrong; Li, Chuanrong

2017-10-01

Currently, the design of embedded signal processing system is often based on a specific application, but this idea is not conducive to the rapid development of signal processing technology. In this paper, a parallel processing model architecture based on multi-core DSP platform is designed, and it is mainly suitable for the complex algorithms which are composed of different modules. This model combines the ideas of multi-level pipeline parallelism and message passing, and summarizes the advantages of the mainstream model of multi-core DSP (the Master-Slave model and the Data Flow model), so that it has better performance. This paper uses three-dimensional image generation algorithm to validate the efficiency of the proposed model by comparing with the effectiveness of the Master-Slave and the Data Flow model.

Modal Identification of a Time-Invariant 6-Storey Model Test RC-Frame from Free Decay Tests using Multi-Variate Models

DEFF Research Database (Denmark)

Skjærbæk, P. S.; Nielsen, Søren R. K.; Kirkegaard, Poul Henning

1997-01-01

in the comparison. The data investigated are sampled from a laboratory model of a plane 6-storey, 2-bay RC-frame. The laboratory model is excited at the top storey where two different types of excitation where considered. In the first case the structure was excited in the first mode and in the second case......The scope of the paper is to apply multi-variate time-domain models for identification of eginfrequencies and mode shapes of a time- invariant model test Reinforced Concrete (RC) frame from measured decays. The frequencies and mode shapes of interest are the two lowest ones since they are normally...

Modal Identification of a Time-Invariant 6-Storey Model Test RC-Frame from Free Decay Tests using Multi-Variate Models

DEFF Research Database (Denmark)

Skjærbæk, P. S.; Nielsen, Søren R. K.; Kirkegaard, Poul Henning

in the comparison. The data investigated are sampled from a laboratory model of a plane 6-storey, 2-bay RC-frame. The laboratory model is excited at the top storey where two different types of excitation where considered. In the first case the structure was excited in the first mode and in the second case......The scope of the paper is to apply multi-variate time-domain models for identification of eginfrequencies and mode shapes of a time- invariant model test Reinforced Concrete (RC) frame from measured decays. The frequencies and mode shapes of interest are the two lowest ones since they are normally...

Rudder Based Roll Control via host-computer of A Robotic Boat

Directory of Open Access Journals (Sweden)

Xinping Bao

2009-03-01

Full Text Available Rudder based roll control of a small-sized robotic boat is a key technique for the devices on board to achieve good performance. This paper introduces a host-based robotic boat capable of performing basic movement operations. The course keeping and roll reduction are studied via rudder based method in simulations and sea trials. The boat dynamic model is built with the combination of mathematical analysis and system identification technique. A mixed sensitivity H control method design is selected since yaw and roll motion are posed in different frequency domains. Computer simulations and experiments carried out show that successful results are achieved.

Rudder Based Roll Control via Host-Computer of a Robotic Boat

Directory of Open Access Journals (Sweden)

Xinping Bao

2009-03-01

Full Text Available Rudder based roll control of a small-sized robotic boat is a key technique for the devices on board to achieve good performance. This paper introduces a host-based robotic boat capable of performing basic movement operations. The course keeping and roll reduction are studied via rudder based method in simulations and sea trials. The boat dynamic model is built with the combination of mathematical analysis and system identification technique. A mixed sensitivity H∞ control method design is selected since yaw and roll motion are posed in different frequency domains. Computer simulations and experiments carried out show that successful results are achieved.

Recombination every day: abundant recombination in a virus during a single multi-cellular host infection.

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Remy Froissart

2005-03-01

Full Text Available Viral recombination can dramatically impact evolution and epidemiology. In viruses, the recombination rate depends on the frequency of genetic exchange between different viral genomes within an infected host cell and on the frequency at which such co-infections occur. While the recombination rate has been recently evaluated in experimentally co-infected cell cultures for several viruses, direct quantification at the most biologically significant level, that of a host infection, is still lacking. This study fills this gap using the cauliflower mosaic virus as a model. We distributed four neutral markers along the viral genome, and co-inoculated host plants with marker-containing and wild-type viruses. The frequency of recombinant genomes was evaluated 21 d post-inoculation. On average, over 50% of viral genomes recovered after a single host infection were recombinants, clearly indicating that recombination is very frequent in this virus. Estimates of the recombination rate show that all regions of the genome are equally affected by this process. Assuming that ten viral replication cycles occurred during our experiment-based on data on the timing of coat protein detection-the per base and replication cycle recombination rate was on the order of 2 x 10(-5 to 4 x 10(-5. This first determination of a virus recombination rate during a single multi-cellular host infection indicates that recombination is very frequent in the everyday life of this virus.

Hazard identification based on plant functional modelling

International Nuclear Information System (INIS)

Rasmussen, B.; Whetton, C.

1993-10-01

A major objective of the present work is to provide means for representing a process plant as a socio-technical system, so as to allow hazard identification at a high level. The method includes technical, human and organisational aspects and is intended to be used for plant level hazard identification so as to identify critical areas and the need for further analysis using existing methods. The first part of the method is the preparation of a plant functional model where a set of plant functions link together hardware, software, operations, work organisation and other safety related aspects of the plant. The basic principle of the functional modelling is that any aspect of the plant can be represented by an object (in the sense that this term is used in computer science) based upon an Intent (or goal); associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. The Methods and Constraints can themselves be treated as objects and decomposed into lower-level Intents (hence the procedure is known as functional decomposition) so giving rise to a hierarchical, object-oriented structure. The plant level hazard identification is carried out on the plant functional model using the Concept Hazard Analysis method. In this, the user will be supported by checklists and keywords and the analysis is structured by pre-defined worksheets. The preparation of the plant functional model and the performance of the hazard identification can be carried out manually or with computer support. (au) (4 tabs., 10 ills., 7 refs.)

Genetic Algorithm-Based Identification of Fractional-Order Systems

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Shengxi Zhou

2013-05-01

Full Text Available Fractional calculus has become an increasingly popular tool for modeling the complex behaviors of physical systems from diverse domains. One of the key issues to apply fractional calculus to engineering problems is to achieve the parameter identification of fractional-order systems. A time-domain identification algorithm based on a genetic algorithm (GA is proposed in this paper. The multi-variable parameter identification is converted into a parameter optimization by applying GA to the identification of fractional-order systems. To evaluate the identification accuracy and stability, the time-domain output error considering the condition variation is designed as the fitness function for parameter optimization. The identification process is established under various noise levels and excitation levels. The effects of external excitation and the noise level on the identification accuracy are analyzed in detail. The simulation results show that the proposed method could identify the parameters of both commensurate rate and non-commensurate rate fractional-order systems from the data with noise. It is also observed that excitation signal is an important factor influencing the identification accuracy of fractional-order systems.

System Identification of a Non-Uniformly Sampled Multi-Rate System in Aluminium Electrolysis Cells

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Håkon Viumdal

2014-07-01

Full Text Available Standard system identification algorithms are usually designed to generate mathematical models with equidistant sampling instants, that are equal for both input variables and output variables. Unfortunately, real industrial data sets are often disrupted by missing samples, variations of sampling rates in the different variables (also known as multi-rate systems, and intermittent measurements. In industries with varying events based maintenance or manual operational measures, intermittent measurements are performed leading to uneven sampling rates. Such is the case with aluminium smelters, where in addition the materials fed into the cell create even more irregularity in sampling. Both measurements and feeding are mostly manually controlled. A simplified simulation of the metal level in an aluminium electrolysis cell is performed based on mass balance considerations. System identification methods based on Prediction Error Methods (PEM such as Ordinary Least Squares (OLS, and the sub-space method combined Deterministic and Stochastic system identification and Realization (DSR, and its variants are applied to the model of a single electrolysis cell as found in the aluminium smelters. Aliasing phenomena due to large sampling intervals can be crucial in avoiding unsuitable models, but with knowledge about the system dynamics, it is easier to optimize the sampling performance, and hence achieve successful models. The results based on the simulation studies of molten aluminium height in the cells using the various algorithms give results which tally well with the synthetic data sets used. System identification on a smaller data set from a real plant is also implemented in this work. Finally, some concrete suggestions are made for using these models in the smelters.

Multi-fidelity wake modelling based on Co-Kriging method

DEFF Research Database (Denmark)

Wang, Y. M.; Réthoré, Pierre-Elouan; van der Laan, Paul

2016-01-01

models, respectively. Both the univariate and multivariate based surrogate models are established by taking the local wind speed and wind direction as variables of the wind farm power efficiency function. Various multi-fidelity surrogate models are compared and different sampling schemes are discussed...

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

Science.gov (United States)

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Statin Selection in Qatar Based on Multi-indication Pharmacotherapeutic Multi-criteria Scoring Model, and Clinician Preference.

Science.gov (United States)

Al-Badriyeh, Daoud; Fahey, Michael; Alabbadi, Ibrahim; Al-Khal, Abdullatif; Zaidan, Manal

2015-12-01

Statin selection for the largest hospital formulary in Qatar is not systematic, not comparative, and does not consider the multi-indication nature of statins. There are no reports in the literature of multi-indication-based comparative scoring models of statins or of statin selection criteria weights that are based primarily on local clinicians' preferences and experiences. This study sought to comparatively evaluate statins for first-line therapy in Qatar, and to quantify the economic impact of this. An evidence-based, multi-indication, multi-criteria pharmacotherapeutic model was developed for the scoring of statins from the perspective of the main health care provider in Qatar. The literature and an expert panel informed the selection criteria of statins. Relative weighting of selection criteria was based on the input of the relevant local clinician population. Statins were comparatively scored based on literature evidence, with those exceeding a defined scoring threshold being recommended for use. With 95% CI and 5% margin of error, the scoring model was successfully developed. Selection criteria comprised 28 subcriteria under the following main criteria: clinical efficacy, best publish evidence and experience, adverse effects, drug interaction, dosing time, and fixed dose combination availability. Outcome measures for multiple indications were related to effects on LDL cholesterol, HDL cholesterol, triglyceride, total cholesterol, and C-reactive protein. Atorvastatin, pravastatin, and rosuvastatin exceeded defined pharmacotherapeutic thresholds. Atorvastatin and pravastatin were recommended as first-line use and rosuvastatin as a nonformulary alternative. It was estimated that this would produce a 17.6% cost savings in statins expenditure. Sensitivity analyses confirmed the robustness of the evaluation's outcomes against input uncertainties. Incorporating a comparative evaluation of statins in Qatari practices based on a locally developed, transparent, multi

Asymmetric multi-fractality in the U.S. stock indices using index-based model of A-MFDFA

International Nuclear Information System (INIS)

Lee, Minhyuk; Song, Jae Wook; Park, Ji Hwan; Chang, Woojin

2017-01-01

Highlights: • ‘Index-based A-MFDFA’ model is proposed to assess the asymmetric multi-fractality. • The asymmetric multi-fractality in the U.S. stock indices are investigated using ‘Index-based’ and ‘Return-based’ A-MFDFA. • The asymmetric feature is more significantly identified by ‘Index-based’ model than ‘return-based’ model. • Source of multi-fractality and time-varying features are analyzed. - Abstract: We detect the asymmetric multi-fractality in the U.S. stock indices based on the asymmetric multi-fractal detrended fluctuation analysis (A-MFDFA). Instead using the conventional return-based approach, we propose the index-based model of A-MFDFA where the trend based on the evolution of stock index rather than stock price return plays a role for evaluating the asymmetric scaling behaviors. The results show that the multi-fractal behaviors of the U.S. stock indices are asymmetric and the index-based model detects the asymmetric multi-fractality better than return-based model. We also discuss the source of multi-fractality and its asymmetry and observe that the multi-fractal asymmetry in the U.S. stock indices has a time-varying feature where the degree of multi-fractality and asymmetry increase during the financial crisis.

Model-based framework for multi-axial real-time hybrid simulation testing

Science.gov (United States)

Fermandois, Gaston A.; Spencer, Billie F.

2017-10-01

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six

Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model

Directory of Open Access Journals (Sweden)

Yazid Edwar

2014-07-01

Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.

Multi-Collinearity Based Model Selection for Landslide Susceptibility Mapping: A Case Study from Ulus District of Karabuk, Turkey

Science.gov (United States)

Sahin, E. K.; Colkesen, I., , Dr; Kavzoglu, T.

2017-12-01

Identification of localities prone to landslide areas plays an important role for emergency planning, disaster management and recovery planning. Due to its great importance for disaster management, producing accurate and up-to-date landslide susceptibility maps is essential for hazard mitigation purpose and regional planning. The main objective of the present study was to apply multi-collinearity based model selection approach for the production of a landslide susceptibility map of Ulus district of Karabuk, Turkey. It is a fact that data do not contain enough information to describe the problem under consideration when the factors are highly correlated with each other. In such cases, choosing a subset of the original features will often lead to better performance. This paper presents multi-collinearity based model selection approach to deal with the high correlation within the dataset. Two collinearity diagnostic factors (Tolerance (TOL) and the Variance Inflation Factor (VIF)) are commonly used to identify multi-collinearity. Values of VIF that exceed 10.0 and TOL values less than 1.0 are often regarded as indicating multi-collinearity. Five causative factors (slope length, curvature, plan curvature, profile curvature and topographical roughness index) were found highly correlated with each other among 15 factors available for the study area. As a result, the five correlated factors were removed from the model estimation, and performances of the models including the remaining 10 factors (aspect, drainage density, elevation, lithology, land use/land cover, NDVI, slope, sediment transport index, topographical position index and topographical wetness index) were evaluated using logistic regression. The performance of prediction model constructed with 10 factors was compared to that of 15-factor model. The prediction performance of two susceptibility maps was evaluated by overall accuracy and the area under the ROC curve (AUC) values. Results showed that overall

Identification and Reconfigurable Control of Impaired Multi-Rotor Drones

Science.gov (United States)

Stepanyan, Vahram; Krishnakumar, Kalmanje; Bencomo, Alfredo

2016-01-01

The paper presents an algorithm for control and safe landing of impaired multi-rotor drones when one or more motors fail simultaneously or in any sequence. It includes three main components: an identification block, a reconfigurable control block, and a decisions making block. The identification block monitors each motor load characteristics and the current drawn, based on which the failures are detected. The control block generates the required total thrust and three axis torques for the altitude, horizontal position and/or orientation control of the drone based on the time scale separation and nonlinear dynamic inversion. The horizontal displacement is controlled by modulating the roll and pitch angles. The decision making algorithm maps the total thrust and three torques into the individual motor thrusts based on the information provided by the identification block. The drone continues the mission execution as long as the number of functioning motors provide controllability of it. Otherwise, the controller is switched to the safe mode, which gives up the yaw control, commands a safe landing spot and descent rate while maintaining the horizontal attitude.

Variable cycle control model for intersection based on multi-source information

Science.gov (United States)

Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan

2018-05-01

In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

[Crop geometry identification based on inversion of semiempirical BRDF models].

Science.gov (United States)

Zhao, Chun-jiang; Huang, Wen-jiang; Mu, Xu-han; Wang, Jin-diz; Wang, Ji-hua

2009-09-01

With the rapid development of remote sensing technology, the application of remote sensing has extended from single view angle to multi-view angles. It was studied for the qualitative and quantitative effect of average leaf angle (ALA) on crop canopy reflected spectrum. Effect of ALA on canopy reflected spectrum can not be ignored with inversion of leaf area index (LAI) and monitoring of crop growth condition by remote sensing technology. Investigations of the effect of erective and horizontal varieties were conducted by bidirectional canopy reflected spectrum and semiempirical bidirectional reflectance distribution function (BRDF) models. The sensitive analysis was done based on the weight for the volumetric kernel (fvol), the weight for the geometric kernel (fgeo), and the weight for constant corresponding to isotropic reflectance (fiso) at red band (680 nm) and near infrared band (800 nm). By combining the weights of the red and near-infrared bands, the semiempirical models can obtain structural information by retrieving biophysical parameters from the physical BRDF model and a number of bidirectional observations. So, it will allow an on-site and non-sampling mode of crop ALA identification, which is useful for using remote sensing for crop growth monitoring and for improving the LAI inversion accuracy, and it will help the farmers in guiding the fertilizer and irrigation management in the farmland without a priori knowledge.

Physics Model-Based Scatter Correction in Multi-Source Interior Computed Tomography.

Science.gov (United States)

Gong, Hao; Li, Bin; Jia, Xun; Cao, Guohua

2018-02-01

Multi-source interior computed tomography (CT) has a great potential to provide ultra-fast and organ-oriented imaging at low radiation dose. However, X-ray cross scattering from multiple simultaneously activated X-ray imaging chains compromises imaging quality. Previously, we published two hardware-based scatter correction methods for multi-source interior CT. Here, we propose a software-based scatter correction method, with the benefit of no need for hardware modifications. The new method is based on a physics model and an iterative framework. The physics model was derived analytically, and was used to calculate X-ray scattering signals in both forward direction and cross directions in multi-source interior CT. The physics model was integrated to an iterative scatter correction framework to reduce scatter artifacts. The method was applied to phantom data from both Monte Carlo simulations and physical experimentation that were designed to emulate the image acquisition in a multi-source interior CT architecture recently proposed by our team. The proposed scatter correction method reduced scatter artifacts significantly, even with only one iteration. Within a few iterations, the reconstructed images fast converged toward the "scatter-free" reference images. After applying the scatter correction method, the maximum CT number error at the region-of-interests (ROIs) was reduced to 46 HU in numerical phantom dataset and 48 HU in physical phantom dataset respectively, and the contrast-noise-ratio at those ROIs increased by up to 44.3% and up to 19.7%, respectively. The proposed physics model-based iterative scatter correction method could be useful for scatter correction in dual-source or multi-source CT.

A physiologically based nonhomogeneous Poisson counter model of visual identification.

Science.gov (United States)

Christensen, Jeppe H; Markussen, Bo; Bundesen, Claus; Kyllingsbæk, Søren

2018-04-30

A physiologically based nonhomogeneous Poisson counter model of visual identification is presented. The model was developed in the framework of a Theory of Visual Attention (Bundesen, 1990; Kyllingsbæk, Markussen, & Bundesen, 2012) and meant for modeling visual identification of objects that are mutually confusable and hard to see. The model assumes that the visual system's initial sensory response consists in tentative visual categorizations, which are accumulated by leaky integration of both transient and sustained components comparable with those found in spike density patterns of early sensory neurons. The sensory response (tentative categorizations) feeds independent Poisson counters, each of which accumulates tentative object categorizations of a particular type to guide overt identification performance. We tested the model's ability to predict the effect of stimulus duration on observed distributions of responses in a nonspeeded (pure accuracy) identification task with eight response alternatives. The time courses of correct and erroneous categorizations were well accounted for when the event-rates of competing Poisson counters were allowed to vary independently over time in a way that mimicked the dynamics of receptive field selectivity as found in neurophysiological studies. Furthermore, the initial sensory response yielded theoretical hazard rate functions that closely resembled empirically estimated ones. Finally, supplied with a Naka-Rushton type contrast gain control, the model provided an explanation for Bloch's law. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A spatial model of mosquito host-seeking behavior.

Directory of Open Access Journals (Sweden)

Bree Cummins

Full Text Available Mosquito host-seeking behavior and heterogeneity in host distribution are important factors in predicting the transmission dynamics of mosquito-borne infections such as dengue fever, malaria, chikungunya, and West Nile virus. We develop and analyze a new mathematical model to describe the effect of spatial heterogeneity on the contact rate between mosquito vectors and hosts. The model includes odor plumes generated by spatially distributed hosts, wind velocity, and mosquito behavior based on both the prevailing wind and the odor plume. On a spatial scale of meters and a time scale of minutes, we compare the effectiveness of different plume-finding and plume-tracking strategies that mosquitoes could use to locate a host. The results show that two different models of chemotaxis are capable of producing comparable results given appropriate parameter choices and that host finding is optimized by a strategy of flying across the wind until the odor plume is intercepted. We also assess the impact of changing the level of host aggregation on mosquito host-finding success near the end of the host-seeking flight. When clusters of hosts are more tightly associated on smaller patches, the odor plume is narrower and the biting rate per host is decreased. For two host groups of unequal number but equal spatial density, the biting rate per host is lower in the group with more individuals, indicative of an attack abatement effect of host aggregation. We discuss how this approach could assist parameter choices in compartmental models that do not explicitly model the spatial arrangement of individuals and how the model could address larger spatial scales and other probability models for mosquito behavior, such as Lévy distributions.

Approaching Incast Congestion with Multi-host Ethernet Controllers

CERN Document Server

Jereczek, Grzegorz Edmund; The ATLAS collaboration

2018-01-01

The bursty many-to-one communication pattern, typical for data acquisition systems, but also present in datacenter networks, is particularly demanding for commodity TCP/IP and Ethernet technologies. We expand our study of building incast-resistant networks based on software switches running on commercial-off-the-shelf servers. In this paper we provide the estimates for costs and physical area required to build such a network. Our estimates indicate that our proposed design offers significant cost advantage over traditional solutions, but higher space utilisation. Next, we show how the latter can be improved with multi-host Ethernet controllers, as an alternative to typical network interface cards. This can also make software switching easier to adapt in datacenter as a solution for incast congestion. We confirm the capabilities for incast-avoidance by evaluating the performance of a reference platform.

Approaching Incast Congestion with Multi-host Ethernet Controllers

CERN Document Server

AUTHOR|(SzGeCERN)698154; The ATLAS collaboration; Lehmann Miotto, Giovanna; Malone, David; Walukiewicz, Miroslaw

2017-01-01

The bursty many-to-one communication pattern, typical for data acquisition systems, but also present in datacenter networks, is particularly demanding for commodity TCP/IP and Ethernet technologies. We expand our study of building incast-resistant networks based on software switches running on commercial-off-the-shelf servers. In this paper we provide the estimates for costs and physical area required to build such a network. Our estimates indicate that our proposed design offers significant cost advantage over traditional solutions, but higher space utilisation. Next, we show how the latter can be improved with multi-host Ethernet controllers, as an alternative to typical network interface cards. This can also make software switching easier to adapt in datacenter as a solution for incast congestion. We confirm the capabilities for incast-avoidance by evaluating the performance of a reference platform.

Analysis of Offshore Knuckle Boom Crane - Part One: Modeling and Parameter Identification

Directory of Open Access Journals (Sweden)

Morten K. Bak

2013-10-01

Full Text Available This paper presents an extensive model of a knuckle boom crane used for pipe handling on offshore drilling rigs. The mechanical system is modeled as a multi-body system and includes the structural flexibility and damping. The motion control system model includes the main components of the crane's electro-hydraulic actuation system. For this a novel black-box model for counterbalance valves is presented, which uses two different pressure ratios to compute the flow through the valve. Experimental data and parameter identification, based on both numerical optimization and manual tuning, are used to verify the crane model. The demonstrated modeling and parameter identification techniques target the system engineer and takes into account the limited access to component data normally encountered by engineers working with design of hydraulic systems.

Host range, host ecology, and distribution of more than 11800 fish parasite species

Science.gov (United States)

Strona, Giovanni; Palomares, Maria Lourdes D.; Bailly, Nicholas; Galli, Paolo; Lafferty, Kevin D.

2013-01-01

Our data set includes 38 008 fish parasite records (for Acanthocephala, Cestoda, Monogenea, Nematoda, Trematoda) compiled from the scientific literature, Internet databases, and museum collections paired to the corresponding host ecological, biogeographical, and phylogenetic traits (maximum length, growth rate, life span, age at maturity, trophic level, habitat preference, geographical range size, taxonomy). The data focus on host features, because specific parasite traits are not consistently available across records. For this reason, the data set is intended as a flexible resource able to extend the principles of ecological niche modeling to the host–parasite system, providing researchers with the data to model parasite niches based on their distribution in host species and the associated host features. In this sense, the database offers a framework for testing general ecological, biogeographical, and phylogenetic hypotheses based on the identification of hosts as parasite habitat. Potential applications of the data set are, for example, the investigation of species–area relationships or the taxonomic distribution of host-specificity. The provided host–parasite list is that currently used by Fish Parasite Ecology Software Tool (FishPEST, http://purl.oclc.org/fishpest), which is a website that allows researchers to model several aspects of the relationships between fish parasites and their hosts. The database is intended for researchers who wish to have more freedom to analyze the database than currently possible with FishPEST. However, for readers who have not seen FishPEST, we recommend using this as a starting point for interacting with the database.

The Drosophila melanogaster host model

Science.gov (United States)

Igboin, Christina O.; Griffen, Ann L.; Leys, Eugene J.

2012-01-01

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed. PMID:22368770

The Drosophila melanogaster host model

Directory of Open Access Journals (Sweden)

Christina O. Igboin

2012-02-01

Full Text Available The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen–host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial–host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis–host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed.

The Drosophila melanogaster host model.

Science.gov (United States)

Igboin, Christina O; Griffen, Ann L; Leys, Eugene J

2012-01-01

The deleterious and sometimes fatal outcomes of bacterial infectious diseases are the net result of the interactions between the pathogen and the host, and the genetically tractable fruit fly, Drosophila melanogaster, has emerged as a valuable tool for modeling the pathogen-host interactions of a wide variety of bacteria. These studies have revealed that there is a remarkable conservation of bacterial pathogenesis and host defence mechanisms between higher host organisms and Drosophila. This review presents an in-depth discussion of the Drosophila immune response, the Drosophila killing model, and the use of the model to examine bacterial-host interactions. The recent introduction of the Drosophila model into the oral microbiology field is discussed, specifically the use of the model to examine Porphyromonas gingivalis-host interactions, and finally the potential uses of this powerful model system to further elucidate oral bacterial-host interactions are addressed.

Diagnosis and Model Based Identification of a Coupling Misalignment

Directory of Open Access Journals (Sweden)

P. Pennacchi

2005-01-01

Full Text Available This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

Directory of Open Access Journals (Sweden)

S. Arivazhagan

2011-11-01

Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Magnetic hysteresis at the domain scale of a multi-scale material model for magneto-elastic behaviour

Energy Technology Data Exchange (ETDEWEB)

Vanoost, D., E-mail: dries.vanoost@kuleuven-kulak.be [KU Leuven Technology Campus Ostend, ReMI Research Group, Oostende B-8400 (Belgium); KU Leuven Kulak, Wave Propagation and Signal Processing Research Group, Kortrijk B-8500 (Belgium); Steentjes, S. [Institute of Electrical Machines, RWTH Aachen University, Aachen D-52062 (Germany); Peuteman, J. [KU Leuven Technology Campus Ostend, ReMI Research Group, Oostende B-8400 (Belgium); KU Leuven, Department of Electrical Engineering, Electrical Energy and Computer Architecture, Heverlee B-3001 (Belgium); Gielen, G. [KU Leuven, Department of Electrical Engineering, Microelectronics and Sensors, Heverlee B-3001 (Belgium); De Gersem, H. [KU Leuven Kulak, Wave Propagation and Signal Processing Research Group, Kortrijk B-8500 (Belgium); TU Darmstadt, Institut für Theorie Elektromagnetischer Felder, Darmstadt D-64289 (Germany); Pissoort, D. [KU Leuven Technology Campus Ostend, ReMI Research Group, Oostende B-8400 (Belgium); KU Leuven, Department of Electrical Engineering, Microelectronics and Sensors, Heverlee B-3001 (Belgium); Hameyer, K. [Institute of Electrical Machines, RWTH Aachen University, Aachen D-52062 (Germany)

2016-09-15

This paper proposes a multi-scale energy-based material model for poly-crystalline materials. Describing the behaviour of poly-crystalline materials at three spatial scales of dominating physical mechanisms allows accounting for the heterogeneity and multi-axiality of the material behaviour. The three spatial scales are the poly-crystalline, grain and domain scale. Together with appropriate scale transitions rules and models for local magnetic behaviour at each scale, the model is able to describe the magneto-elastic behaviour (magnetostriction and hysteresis) at the macroscale, although the data input is merely based on a set of physical constants. Introducing a new energy density function that describes the demagnetisation field, the anhysteretic multi-scale energy-based material model is extended to the hysteretic case. The hysteresis behaviour is included at the domain scale according to the micro-magnetic domain theory while preserving a valid description for the magneto-elastic coupling. The model is verified using existing measurement data for different mechanical stress levels. - Highlights: • A ferromagnetic hysteretic energy-based multi-scale material model is proposed. • The hysteresis is obtained by new proposed hysteresis energy density function. • Avoids tedious parameter identification.

Genetic variability and identification of the intermediate snail hosts of Schistosoma mansoni

Directory of Open Access Journals (Sweden)

Teofânia HDA Vidigal

1998-01-01

Full Text Available Studies based on shell or reproductive organ morphology and genetic considerations suggest extensive intraspecific variation in Biomphalaria snails. The high variability at the morphological and genetic levels, as well as the small size of some specimens and similarities between species complicate the correct identification of these snails. Here we review our work using methods based on polymerase chain reaction (PCR amplification for analysis of genetic variation and identification of Biomphalaria snails from Brazil, Argentina, Uruguay and Paraguay. Arbitrarily primed-PCR revealed that the genome of B. glabrata exihibits a remarkable degree of intraespecific polymorphism. Low stringency-PCR using primers for 18S rRNA permited the identification of B. glabrata, B. tenagophila and B. occidentalis. The study of individuals obtained from geographically distinct populations exhibits significant intraspecific DNA polymorphism, however specimens from the same species, exhibit some species specific LSPs. We also showed that PCR-restriction fragment of length polymorphism of the internal transcribed spacer region of Biomphalaria rDNA, using DdeI permits the differentiation of the three intermediate hosts of Schistosoma mansoni. The molecular biological techniques used in our studies are very useful for the generation of new knowledge concerning the systematics and population genetics of Biomphalaria snails.

Use of model plant hosts to identify Pseudomonas aeruginosa virulence factors

Science.gov (United States)

Rahme, Laurence G.; Tan, Man-Wah; Le, Long; Wong, Sandy M.; Tompkins, Ronald G.; Calderwood, Stephen B.; Ausubel, Frederick M.

1997-01-01

We used plants as an in vivo pathogenesis model for the identification of virulence factors of the human opportunistic pathogen Pseudomonas aeruginosa. Nine of nine TnphoA mutant derivatives of P. aeruginosa strain UCBPP-PA14 that were identified in a plant leaf assay for less pathogenic mutants also exhibited significantly reduced pathogenicity in a burned mouse pathogenicity model, suggesting that P. aeruginosa utilizes common strategies to infect both hosts. Seven of these nine mutants contain TnphoA insertions in previously unknown genes. These results demonstrate that an alternative nonvertebrate host of a human bacterial pathogen can be used in an in vivo high throughput screen to identify novel bacterial virulence factors involved in mammalian pathogenesis. PMID:9371831

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

System Identification Based Proxy Model of a Reservoir under Water Injection

Directory of Open Access Journals (Sweden)

Berihun M. Negash

2017-01-01

Full Text Available Simulation of numerical reservoir models with thousands and millions of grid blocks may consume a significant amount of time and effort, even when high performance processors are used. In cases where the simulation runs are required for sensitivity analysis, dynamic control, and optimization, the act needs to be repeated several times by continuously changing parameters. This makes it even more time-consuming. Currently, proxy models that are based on response surface are being used to lessen the time required for running simulations during sensitivity analysis and optimization. Proxy models are lighter mathematical models that run faster and perform in place of heavier models that require large computations. Nevertheless, to acquire data for modeling and validation and develop the proxy model itself, hundreds of simulation runs are required. In this paper, a system identification based proxy model that requires only a single simulation run and a properly designed excitation signal was proposed and evaluated using a benchmark case study. The results show that, with proper design of excitation signal and proper selection of model structure, system identification based proxy models are found to be practical and efficient alternatives for mimicking the performance of numerical reservoir models. The resulting proxy models have potential applications for dynamic well control and optimization.

Multi-objective decision-making model based on CBM for an aircraft fleet

Science.gov (United States)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

PHI-base: a new interface and further additions for the multi-species pathogen–host interactions database

Science.gov (United States)

Urban, Martin; Cuzick, Alayne; Rutherford, Kim; Irvine, Alistair; Pedro, Helder; Pant, Rashmi; Sadanadan, Vidyendra; Khamari, Lokanath; Billal, Santoshkumar; Mohanty, Sagar; Hammond-Kosack, Kim E.

2017-01-01

The pathogen–host interactions database (PHI-base) is available at www.phi-base.org. PHI-base contains expertly curated molecular and biological information on genes proven to affect the outcome of pathogen–host interactions reported in peer reviewed research articles. In addition, literature that indicates specific gene alterations that did not affect the disease interaction phenotype are curated to provide complete datasets for comparative purposes. Viruses are not included. Here we describe a revised PHI-base Version 4 data platform with improved search, filtering and extended data display functions. A PHIB-BLAST search function is provided and a link to PHI-Canto, a tool for authors to directly curate their own published data into PHI-base. The new release of PHI-base Version 4.2 (October 2016) has an increased data content containing information from 2219 manually curated references. The data provide information on 4460 genes from 264 pathogens tested on 176 hosts in 8046 interactions. Prokaryotic and eukaryotic pathogens are represented in almost equal numbers. Host species belong ∼70% to plants and 30% to other species of medical and/or environmental importance. Additional data types included into PHI-base 4 are the direct targets of pathogen effector proteins in experimental and natural host organisms. The curation problems encountered and the future directions of the PHI-base project are briefly discussed. PMID:27915230

Identification of physical models

DEFF Research Database (Denmark)

Melgaard, Henrik

1994-01-01

of the model with the available prior knowledge. The methods for identification of physical models have been applied in two different case studies. One case is the identification of thermal dynamics of building components. The work is related to a CEC research project called PASSYS (Passive Solar Components......The problem of identification of physical models is considered within the frame of stochastic differential equations. Methods for estimation of parameters of these continuous time models based on descrete time measurements are discussed. The important algorithms of a computer program for ML or MAP...... design of experiments, which is for instance the design of an input signal that are optimal according to a criterion based on the information provided by the experiment. Also model validation is discussed. An important verification of a physical model is to compare the physical characteristics...

Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

Directory of Open Access Journals (Sweden)

Tae-Hyoung Kim

2017-01-01

Full Text Available This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

Distributed model based control of multi unit evaporation systems

International Nuclear Information System (INIS)

Yudi Samyudia

2006-01-01

In this paper, we present a new approach to the analysis and design of distributed control systems for multi-unit plants. The approach is established after treating the effect of recycled dynamics as a gap metric uncertainty from which a distributed controller can be designed sequentially for each unit to tackle the uncertainty. We then use a single effect multi-unit evaporation system to illustrate how the proposed method is used to analyze different control strategies and to systematically achieve a better closed-loop performance using a distributed model-based controller

Cluster-based analysis of multi-model climate ensembles

Science.gov (United States)

Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

2018-06-01

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and

Robust multi-model predictive control of multi-zone thermal plate system

Directory of Open Access Journals (Sweden)

Poom Jatunitanon

2018-02-01

Full Text Available A modern controller was designed by using the mathematical model of a multi–zone thermal plate system. An important requirement for this type of controller is that it must be able to keep the temperature set-point of each thermal zone. The mathematical model used in the design was determined through a system identification process. The results showed that when the operating condition is changed, the performance of the controller may be reduced as a result of the system parameter uncertainties. This paper proposes a weighting technique of combining the robust model predictive controller for each operating condition into a single robust multi-model predictive control. Simulation and experimental results showed that the proposed method performed better than the conventional multi-model predictive control in rise time of transient response, when used in a system designed to work over a wide range of operating conditions.

PHI-base: a new interface and further additions for the multi-species pathogen-host interactions database.

Science.gov (United States)

Urban, Martin; Cuzick, Alayne; Rutherford, Kim; Irvine, Alistair; Pedro, Helder; Pant, Rashmi; Sadanadan, Vidyendra; Khamari, Lokanath; Billal, Santoshkumar; Mohanty, Sagar; Hammond-Kosack, Kim E

2017-01-04

The pathogen-host interactions database (PHI-base) is available at www.phi-base.org PHI-base contains expertly curated molecular and biological information on genes proven to affect the outcome of pathogen-host interactions reported in peer reviewed research articles. In addition, literature that indicates specific gene alterations that did not affect the disease interaction phenotype are curated to provide complete datasets for comparative purposes. Viruses are not included. Here we describe a revised PHI-base Version 4 data platform with improved search, filtering and extended data display functions. A PHIB-BLAST search function is provided and a link to PHI-Canto, a tool for authors to directly curate their own published data into PHI-base. The new release of PHI-base Version 4.2 (October 2016) has an increased data content containing information from 2219 manually curated references. The data provide information on 4460 genes from 264 pathogens tested on 176 hosts in 8046 interactions. Prokaryotic and eukaryotic pathogens are represented in almost equal numbers. Host species belong ∼70% to plants and 30% to other species of medical and/or environmental importance. Additional data types included into PHI-base 4 are the direct targets of pathogen effector proteins in experimental and natural host organisms. The curation problems encountered and the future directions of the PHI-base project are briefly discussed. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Multi-Site Calibration of Linear Reservoir Based Geomorphologic Rainfall-Runoff Models

Directory of Open Access Journals (Sweden)

Bahram Saeidifarzad

2014-09-01

Full Text Available Multi-site optimization of two adapted event-based geomorphologic rainfall-runoff models was presented using Non-dominated Sorting Genetic Algorithm (NSGA-II method for the South Fork Eel River watershed, California. The first model was developed based on Unequal Cascade of Reservoirs (UECR and the second model was presented as a modified version of Geomorphological Unit Hydrograph based on Nash’s model (GUHN. Two calibration strategies were considered as semi-lumped and semi-distributed for imposing (or unimposing the geomorphology relations in the models. The results of models were compared with Nash’s model. Obtained results using the observed data of two stations in the multi-site optimization framework showed reasonable efficiency values in both the calibration and the verification steps. The outcomes also showed that semi-distributed calibration of the modified GUHN model slightly outperformed other models in both upstream and downstream stations during calibration. Both calibration strategies for the developed UECR model during the verification phase showed slightly better performance in the downstream station, but in the upstream station, the modified GUHN model in the semi-lumped strategy slightly outperformed the other models. The semi-lumped calibration strategy could lead to logical lag time parameters related to the basin geomorphology and may be more suitable for data-based statistical analyses of the rainfall-runoff process.

Identification of walking human model using agent-based modelling

Science.gov (United States)

Shahabpoor, Erfan; Pavic, Aleksandar; Racic, Vitomir

2018-03-01

The interaction of walking people with large vibrating structures, such as footbridges and floors, in the vertical direction is an important yet challenging phenomenon to describe mathematically. Several different models have been proposed in the literature to simulate interaction of stationary people with vibrating structures. However, the research on moving (walking) human models, explicitly identified for vibration serviceability assessment of civil structures, is still sparse. In this study, the results of a comprehensive set of FRF-based modal tests were used, in which, over a hundred test subjects walked in different group sizes and walking patterns on a test structure. An agent-based model was used to simulate discrete traffic-structure interactions. The occupied structure modal parameters found in tests were used to identify the parameters of the walking individual's single-degree-of-freedom (SDOF) mass-spring-damper model using 'reverse engineering' methodology. The analysis of the results suggested that the normal distribution with the average of μ = 2.85Hz and standard deviation of σ = 0.34Hz can describe human SDOF model natural frequency. Similarly, the normal distribution with μ = 0.295 and σ = 0.047 can describe the human model damping ratio. Compared to the previous studies, the agent-based modelling methodology proposed in this paper offers significant flexibility in simulating multi-pedestrian walking traffics, external forces and simulating different mechanisms of human-structure and human-environment interaction at the same time.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Study on integrated evaluation of sandstone-hosted uranium metallogenic potential in southern Yili basin

International Nuclear Information System (INIS)

Han Shaoyang; Ke Dan; Xu Jianguo; Zheng Enjiu; Li Shengxiang

2008-01-01

Plenty of geological data have been accumulated during mineral resource survey in China; under the guidance of new metallogenic theories, it is an important task of how to use these data most effectively for the new cycle uranium survey. In this paper, the flow of establishing the integrated mineral deposits prospecting model for sandstone-hosted uranium deposits is put forward. Based on studying geologic, hydrogeologic and regional geophysical field characteristics of representative uranium deposits No. 512 in southern Yili basin, its multi-source information descriptive model has been established, from which 512-type integrated prospecting models of sandstone-hosted uranium orefield and deposits are summarized. According to the established integrated prospecting models, the metallogenic information extraction of sandstone-hosted uranium deposits has completed in the study area. Finally, the integrated quantitative evaluation of sandstone-hosted uranium metallogenic potential is performed by using the evidence weighing method to integrate middle scale multi-source metallogenic information in the southern Yili basin, and good prediction effect is obtained. (authors)

Simulation-optimization framework for multi-site multi-season hybrid stochastic streamflow modeling

Science.gov (United States)

Srivastav, Roshan; Srinivasan, K.; Sudheer, K. P.

2016-11-01

A simulation-optimization (S-O) framework is developed for the hybrid stochastic modeling of multi-site multi-season streamflows. The multi-objective optimization model formulated is the driver and the multi-site, multi-season hybrid matched block bootstrap model (MHMABB) is the simulation engine within this framework. The multi-site multi-season simulation model is the extension of the existing single-site multi-season simulation model. A robust and efficient evolutionary search based technique, namely, non-dominated sorting based genetic algorithm (NSGA - II) is employed as the solution technique for the multi-objective optimization within the S-O framework. The objective functions employed are related to the preservation of the multi-site critical deficit run sum and the constraints introduced are concerned with the hybrid model parameter space, and the preservation of certain statistics (such as inter-annual dependence and/or skewness of aggregated annual flows). The efficacy of the proposed S-O framework is brought out through a case example from the Colorado River basin. The proposed multi-site multi-season model AMHMABB (whose parameters are obtained from the proposed S-O framework) preserves the temporal as well as the spatial statistics of the historical flows. Also, the other multi-site deficit run characteristics namely, the number of runs, the maximum run length, the mean run sum and the mean run length are well preserved by the AMHMABB model. Overall, the proposed AMHMABB model is able to show better streamflow modeling performance when compared with the simulation based SMHMABB model, plausibly due to the significant role played by: (i) the objective functions related to the preservation of multi-site critical deficit run sum; (ii) the huge hybrid model parameter space available for the evolutionary search and (iii) the constraint on the preservation of the inter-annual dependence. Split-sample validation results indicate that the AMHMABB model is

Multi-model-based Access Control in Construction Projects

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Frank Hilbert

2012-04-01

Full Text Available During the execution of large scale construction projects performed by Virtual Organizations (VO, relatively complex technical models have to be exchanged between the VO members. For linking the trade and transfer of these models, a so-called multi-model container format was developed. Considering the different skills and tasks of the involved partners, it is not necessary for them to know all the models in every technical detailing. Furthermore, the model size can lead to a delay in communication. In this paper an approach is presented for defining model cut-outs according to the current project context. Dynamic dependencies to the project context as well as static dependencies on the organizational structure are mapped in a context-sensitive rule. As a result, an approach for dynamic filtering of multi-models is obtained which ensures, together with a filtering service, that the involved VO members get a simplified view of complex multi-models as well as sufficient permissions depending on their tasks.

Identification of host blood-meal sources and Borrelia in field-collected Ixodes ricinus ticks in north-western Poland

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Beata Wodecka

2015-12-01

Full Text Available Forest animals play fundamental roles in the maintenance of [i]Ixodes ricinus[/i] and [i]Borrelia[/i] species in the forest biotope. To identify the forest vertebrate species that are host for I. ricinus and for the recognition of the reservoirs of [i]Borrelia[/i] species, the blood-meal of 325 [i]I. ricinus[/i] ticks collected at two forest sites in north-western Poland were analysed. Nested PCR was used to detect polymorphisms in a fragment of the mitochondrial 12S rRNA gene for the identification of the hosts species. The products were digested with the restriction enzymes, a combination that allows the identification of 60 vertebrate species, comprising 17 bird, 4 reptile and 39 mammalian species. Host DNA was detected in 244 (75%[i] I. ricinus[/i] individuals, with the species being detected and classified for 210 (86% samples. The restriction patterns resulted in the identification of 14 vertebrate species, including 2 species of birds, lizard, badger, rabbit, deer; most of the samples contained DNA from wild boar ([i]Sus scrofa[/i], red fox ([i]Vulpes vulpes[/i], red deer ([i]Cervus elaphus[/i] and roe deer ([i]Capreolus capreolus[/i]. Identification of Borrelia species was based on the flaB gene using nested PCR coupled to RFLP. This method allows the identification of all [i]Borrelia[/i] species transmitted by [i]I. ricinus [/i]in Europe, including [i]B. miyamotoi[/i] and 3 genetic variants of [i]B. garinii[/i]. In the studied isolates, 2 species belonging to [i]B. burgdorferi[/i] sensu lato were identified – B. [i]garinii [/i]and B. [i]afzelii[/i], and B. [i]miyamotoi,[/i] which are related to relapsing fever borreliae.

A multi-layered mechanistic modelling approach to understand how effector genes extend beyond phytoplasma to modulate plant hosts, insect vectors and the environment.

Science.gov (United States)

Tomkins, Melissa; Kliot, Adi; Marée, Athanasius Fm; Hogenhout, Saskia A

2018-03-13

Members of the Candidatus genus Phytoplasma are small bacterial pathogens that hijack their plant hosts via the secretion of virulence proteins (effectors) leading to a fascinating array of plant phenotypes, such as witch's brooms (stem proliferations) and phyllody (retrograde development of flowers into vegetative tissues). Phytoplasma depend on insect vectors for transmission, and interestingly, these insect vectors were found to be (in)directly attracted to plants with these phenotypes. Therefore, phytoplasma effectors appear to reprogram plant development and defence to lure insect vectors, similarly to social engineering malware, which employs tricks to lure people to infected computers and webpages. A multi-layered mechanistic modelling approach will enable a better understanding of how phytoplasma effector-mediated modulations of plant host development and insect vector behaviour contribute to phytoplasma spread, and ultimately to predict the long reach of phytoplasma effector genes. Copyright © 2018. Published by Elsevier Ltd.

A Multi-Agent Traffic Control Model Based on Distributed System

Directory of Open Access Journals (Sweden)

Qian WU

2014-06-01

Full Text Available With the development of urbanization construction, urban travel has become a quite thorny and imminent problem. Some previous researches on the large urban traffic systems easily change into NPC problems. We purpose a multi-agent inductive control model based on the distributed approach. To describe the real traffic scene, this model designs four different types of intelligent agents, i.e. we regard each lane, route, intersection and traffic region as different types of intelligent agents. Each agent can achieve the real-time traffic data from its neighbor agents, and decision-making agents establish real-time traffic signal plans through the communication between local agents and their neighbor agents. To evaluate the traffic system, this paper takes the average delay, the stopped time and the average speed as performance parameters. Finally, the distributed multi-agent is simulated on the VISSIM simulation platform, the simulation results show that the multi-agent system is more effective than the adaptive control system in solving the traffic congestion.

A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer's Disease.

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Wenjia Wang

Full Text Available Results from Genome-Wide Association Studies (GWAS have shown that the genetic basis of complex traits often include many genetic variants with small to moderate effects whose identification remains a challenging problem. In this context multi-marker analysis at the gene and pathway level can complement traditional point-wise approaches that treat the genetic markers individually. In this paper we propose a novel statistical approach for multi-marker analysis based on the Rasch model. The method summarizes the categorical genotypes of SNPs by a generalized logistic function into a genetic score that can be used for association analysis. Through different sets of simulations, the false-positive rate and power of the proposed approach are compared to a set of existing methods, and shows good performances. The application of the Rasch model on Alzheimer's Disease (AD ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.

An Agent Based Modelling Approach for Multi-Stakeholder Analysis of City Logistics Solutions

NARCIS (Netherlands)

Anand, N.

2015-01-01

This thesis presents a comprehensive framework for multi-stakeholder analysis of city logistics solutions using agent based modeling. The framework describes different stages for the systematic development of an agent based model for the city logistics domain. The framework includes a

Efficient Hybrid Genetic Based Multi Dimensional Host Load Aware Algorithm for Scheduling and Optimization of Virtual Machines

OpenAIRE

Thiruvenkadam, T; Karthikeyani, V

2014-01-01

Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by...

Multi-objective optimization for generating a weighted multi-model ensemble

Science.gov (United States)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

A GIS-based multi-source and multi-box modeling approach (GMSMB) for air pollution assessment--a North American case study.

Science.gov (United States)

Wang, Bao-Zhen; Chen, Zhi

2013-01-01

This article presents a GIS-based multi-source and multi-box modeling approach (GMSMB) to predict the spatial concentration distributions of airborne pollutant on local and regional scales. In this method, an extended multi-box model combined with a multi-source and multi-grid Gaussian model are developed within the GIS framework to examine the contributions from both point- and area-source emissions. By using GIS, a large amount of data including emission sources, air quality monitoring, meteorological data, and spatial location information required for air quality modeling are brought into an integrated modeling environment. It helps more details of spatial variation in source distribution and meteorological condition to be quantitatively analyzed. The developed modeling approach has been examined to predict the spatial concentration distribution of four air pollutants (CO, NO(2), SO(2) and PM(2.5)) for the State of California. The modeling results are compared with the monitoring data. Good agreement is acquired which demonstrated that the developed modeling approach could deliver an effective air pollution assessment on both regional and local scales to support air pollution control and management planning.

Host Galaxy Properties and Black Hole Mass of Swift J164449.3+573451 from Multi-wavelength Long-term Monitoring and HST Data

Science.gov (United States)

Yoon, Yongmin; Im, Myungshin; Jeon, Yiseul; Lee, Seong-Kook; Choi, Philip; Gehrels, Neil; Pak, Soojong; Sakamoto, Takanori; Urata, Yuji

2015-07-01

We study the host galaxy properties of the tidal disruption object Swift J164449.3+573451 using long-term optical to near-infrared (NIR) data. First, we decompose the galaxy surface brightness distribution and analyze the morphology of the host galaxy using high-resolution Hubble Space Telescope WFC3 images. We conclude that the host galaxy is bulge-dominant and well described by a single Sérsic model with Sérsic index n=3.43+/- 0.05. Adding a disk component, the bulge to total host galaxy flux ratio (B/ T) is 0.83 ± 0.03, which still indicates a bulge-dominant galaxy. Second, we estimate multi-band fluxes of the host galaxy through long-term light curves. Our long-term NIR light curves reveal the pure host galaxy fluxes ˜500 days after the burst. We fit spectral energy distribution models to the multi-band fluxes from the optical to NIR of the host galaxy and determine its properties. The stellar mass, the star formation rate, and the age of the stellar population are {log}({M}\\star /{M}⊙ )={9.14}-0.10+0.13, {0.03}-0.03+0.28 {M}⊙ yr-1, and {0.63}-0.43+0.95 Gyr. Finally, we estimate the mass of the central super massive black hole which is responsible for the tidal disruption event. The black hole mass is estimated to be {10}6.7+/- 0.4 {M}⊙ from {M}{BH}-{M}\\star ,{bul} and {M}{BH}-{L}{bul} relations for the K band, although a smaller value of ˜ {10}5 {M}⊙ cannot be excluded convincingly if the host galaxy harbors a pseudobulge.

A case for multi-model and multi-approach based event attribution: The 2015 European drought

Science.gov (United States)

Hauser, Mathias; Gudmundsson, Lukas; Orth, René; Jézéquel, Aglaé; Haustein, Karsten; Seneviratne, Sonia Isabelle

2017-04-01

Science on the role of anthropogenic influence on extreme weather events such as heat waves or droughts has evolved rapidly over the past years. The approach of "event attribution" compares the occurrence probability of an event in the present, factual world with the probability of the same event in a hypothetical, counterfactual world without human-induced climate change. Every such analysis necessarily faces multiple methodological choices including, but not limited to: the event definition, climate model configuration, and the design of the counterfactual world. Here, we explore the role of such choices for an attribution analysis of the 2015 European summer drought (Hauser et al., in preparation). While some GCMs suggest that anthropogenic forcing made the 2015 drought more likely, others suggest no impact, or even a decrease in the event probability. These results additionally differ for single GCMs, depending on the reference used for the counterfactual world. Observational results do not suggest a historical tendency towards more drying, but the record may be too short to provide robust assessments because of the large interannual variability of drought occurrence. These results highlight the need for a multi-model and multi-approach framework in event attribution research. This is especially important for events with low signal to noise ratio and high model dependency such as regional droughts. Hauser, M., L. Gudmundsson, R. Orth, A. Jézéquel, K. Haustein, S.I. Seneviratne, in preparation. A case for multi-model and multi-approach based event attribution: The 2015 European drought.

Identification of novel esterase-active enzymes from hot environments by use of the host bacterium Thermus thermophilus

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Benedikt eLeis

2015-04-01

Full Text Available Functional metagenomic screening strategies, which are independent of known sequence information, can lead to the identification of truly novel genes and enzymes. Since E. coli has been used exhaustively for this purpose as a host, it is important to establish alternative expression hosts and to use them for functional metagenomic screening for new enzymes. In this study we show that Thermus thermophilus HB27 is an excellent screening host and can be used as an alternative provider of truly novel biocatalysts. In a previous study we constructed the mutant strain BL03 that was no longer able to grow on defined minimal medium supplemented with tributyrin as the sole carbon source and could be used as a host to screen for metagenomic DNA fragments that could complement growth on tributyrin. Several thousand single fosmid clones from thermophilic metagenomic libraries from heated compost and hot spring water samples were subjected to a comparative screening for esterase activity in both T. thermophilus strain BL03 and E. coli EPI300. We scored a greater number of active clones in the thermophilic bacterium than in the mesophilic E. coli. From all clones functionally screened in E. coli, only two thermostable α/β-fold hydrolase enzymes with high amino acid sequence similarity to already characterized enzymes were identifiable. In contrast, five further fosmids were found that conferred lipolytic activities in T. thermophilus. Four open reading frames (ORFs were found which did not share significant similarity to known esterase enzymes. Two of the genes were expressed in both hosts and the novel thermophilic esterases, which based on their primary structures could not be assigned to known esterase or lipase families, were purified and preliminarily characterized. Our work underscores the benefit of using additional screening hosts other than E. coli for the identification of novel biocatalysts with industrial relevance.

Design and implementation of space physics multi-model application integration based on web

Science.gov (United States)

Jiang, Wenping; Zou, Ziming

With the development of research on space environment and space science, how to develop network online computing environment of space weather, space environment and space physics models for Chinese scientific community is becoming more and more important in recent years. Currently, There are two software modes on space physics multi-model application integrated system (SPMAIS) such as C/S and B/S. the C/S mode which is traditional and stand-alone, demands a team or workshop from many disciplines and specialties to build their own multi-model application integrated system, that requires the client must be deployed in different physical regions when user visits the integrated system. Thus, this requirement brings two shortcomings: reducing the efficiency of researchers who use the models to compute; inconvenience of accessing the data. Therefore, it is necessary to create a shared network resource access environment which could help users to visit the computing resources of space physics models through the terminal quickly for conducting space science research and forecasting spatial environment. The SPMAIS develops high-performance, first-principles in B/S mode based on computational models of the space environment and uses these models to predict "Space Weather", to understand space mission data and to further our understanding of the solar system. the main goal of space physics multi-model application integration system (SPMAIS) is to provide an easily and convenient user-driven online models operating environment. up to now, the SPMAIS have contained dozens of space environment models , including international AP8/AE8 IGRF T96 models and solar proton prediction model geomagnetic transmission model etc. which are developed by Chinese scientists. another function of SPMAIS is to integrate space observation data sets which offers input data for models online high-speed computing. In this paper, service-oriented architecture (SOA) concept that divides system into

Modeling and simulation of virtual human's coordination based on multi-agent systems

Science.gov (United States)

Zhang, Mei; Wen, Jing-Hua; Zhang, Zu-Xuan; Zhang, Jian-Qing

2006-10-01

The difficulties and hotspots researched in current virtual geographic environment (VGE) are sharing space and multiusers operation, distributed coordination and group decision-making. The theories and technologies of MAS provide a brand-new environment for analysis, design and realization of distributed opening system. This paper takes cooperation among virtual human in VGE which multi-user participate in as main researched object. First we describe theory foundation truss of VGE, and present the formalization description of Multi-Agent System (MAS). Then we detailed analyze and research arithmetic of collectivity operating behavior learning of virtual human based on best held Genetic Algorithm(GA), and establish dynamics action model which Multi-Agents and object interact dynamically and colony movement strategy. Finally we design a example which shows how 3 evolutional Agents cooperate to complete the task of colony pushing column box, and design a virtual world prototype of virtual human pushing box collectively based on V-Realm Builder 2.0, moreover we make modeling and dynamic simulation with Simulink 6.

Dynamics of Practical Premixed Flames, Part I: Model Structure and Identification

Directory of Open Access Journals (Sweden)

A. Huber

2009-06-01

Full Text Available For the analysis of thermoacoustic instabilities it is most important to determine the dynamic flame response to acoustic disturbances. Premixed flames are often modelled as single-input single-output system, where the “output” (the overall rate of heat release responds to a single “input” variable (often the velocity at the exit of the burner nozzle. However, for practical premixed flames, where perturbations of pressure or velocity at the fuel injector will modulate the fuel equivalence ratio, the heat release rate will respond to fluctuations of equivalence ratio as well as nozzle mass flow rate. In this case, a multiple-input, single-output (MISO model structure for the flame is appropriate. Such a model structure is developed in the present paper. Staged fuel injection as well as fuel line impedances can be taken into account, the integration with low-order or finite-element based models for stability analysis is straightforward. In order to determine unit impulse and frequency response functions for such a model structure, an identification scheme based on unsteady CFD calculation with broadband excitation followed by correlation analysis is proposed and validated successfully. Identification of MISO model coefficients is a challenging task, especially in the presence of noise. Therefore criteria are introduced which allow to ascertain a posteriori how well the identified model represents the true system dynamics. Using these criteria, it is investigated how excitation signal type, time series length and signal-to-noise ratio influence the results of the identification process. Consequences for passive design strategies based on multi-stage fuel injection and experimental work on practical premixed flame dynamics are discussed.

Learning Natural Selection in 4th Grade with Multi-Agent-Based Computational Models

Science.gov (United States)

Dickes, Amanda Catherine; Sengupta, Pratim

2013-01-01

In this paper, we investigate how elementary school students develop multi-level explanations of population dynamics in a simple predator-prey ecosystem, through scaffolded interactions with a multi-agent-based computational model (MABM). The term "agent" in an MABM indicates individual computational objects or actors (e.g., cars), and these…

Line impedance estimation using model based identification technique

DEFF Research Database (Denmark)

Ciobotaru, Mihai; Agelidis, Vassilios; Teodorescu, Remus

2011-01-01

The estimation of the line impedance can be used by the control of numerous grid-connected systems, such as active filters, islanding detection techniques, non-linear current controllers, detection of the on/off grid operation mode. Therefore, estimating the line impedance can add extra functions...... into the operation of the grid-connected power converters. This paper describes a quasi passive method for estimating the line impedance of the distribution electricity network. The method uses the model based identification technique to obtain the resistive and inductive parts of the line impedance. The quasi...

Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)

International Nuclear Information System (INIS)

Gao, Hao; Osher, Stanley; Yu, Hengyong; Wang, Ge

2011-01-01

We propose a compressive sensing approach for multi-energy computed tomography (CT), namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi-energy image for allowing the reconstruction with fewer CT data and less radiation dose, the PRISM models a multi-energy image as the superposition of a low-rank matrix and a sparse matrix (with row dimension in space and column dimension in energy), where the low-rank matrix corresponds to the stationary background over energy that has a low matrix rank, and the sparse matrix represents the rest of distinct spectral features that are often sparse. Distinct from previous methods, the PRISM utilizes the generalized rank, e.g., the matrix rank of tight-frame transform of a multi-energy image, which offers a way to characterize the multi-level and multi-filtered image coherence across the energy spectrum. Besides, the energy-dependent intensity information can be incorporated into the PRISM in terms of the spectral curves for base materials, with which the restoration of the multi-energy image becomes the reconstruction of the energy-independent material composition matrix. In other words, the PRISM utilizes prior knowledge on the generalized rank and sparsity of a multi-energy image, and intensity/spectral characteristics of base materials. Furthermore, we develop an accurate and fast split Bregman method for the PRISM and demonstrate the superior performance of the PRISM relative to several competing methods in simulations. (papers)

A TaqMan real-time PCR-based assay for the identification of Fasciola spp.

Science.gov (United States)

Alasaad, Samer; Soriguer, Ramón C; Abu-Madi, Marawan; El Behairy, Ahmed; Jowers, Michael J; Baños, Pablo Díez; Píriz, Ana; Fickel, Joerns; Zhu, Xing-Quan

2011-06-30

Real time quantitative PCR (qPCR) is one of the key technologies of the post-genome era, with clear advantages compared to normal end-point PCR. In this paper, we report the first qPCR-based assay for the identification of Fasciola spp. Based on sequences of the second internal transcribed spacers (ITS-2) of the ribosomal rRNA gene, we used a set of genus-specific primers for Fasciola ITS-2 amplification, and we designed species-specific internal TaqMan probes to identify F. hepatica and F. gigantica, as well as the hybrid 'intermediate'Fasciola. These primers and probes were used for the highly specific, sensitive, and simple identification of Fasciola species collected from different animal host from China, Spain, Niger and Egypt. The novel qPCR-based technique for the identification of Fasciola spp. may provide a useful tool for the epidemiological investigation of Fasciola infection, including their intermediate snail hosts. Copyright © 2011 Elsevier B.V. All rights reserved.

Feasibility Study on Tension Estimation Technique for Hanger Cables Using the FE Model-Based System Identification Method

Directory of Open Access Journals (Sweden)

Kyu-Sik Park

2015-01-01

Full Text Available Hanger cables in suspension bridges are partly constrained by horizontal clamps. So, existing tension estimation methods based on a single cable model are prone to higher errors as the cable gets shorter, making it more sensitive to flexural rigidity. Therefore, inverse analysis and system identification methods based on finite element models are suggested recently. In this paper, the applicability of system identification methods is investigated using the hanger cables of Gwang-An bridge. The test results show that the inverse analysis and systemic identification methods based on finite element models are more reliable than the existing string theory and linear regression method for calculating the tension in terms of natural frequency errors. However, the estimation error of tension can be varied according to the accuracy of finite element model in model based methods. In particular, the boundary conditions affect the results more profoundly when the cable gets shorter. Therefore, it is important to identify the boundary conditions through experiment if it is possible. The FE model-based tension estimation method using system identification method can take various boundary conditions into account. Also, since it is not sensitive to the number of natural frequency inputs, the availability of this system is high.

Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm

International Nuclear Information System (INIS)

Sun, Zhe; Wang, Ning; Bi, Yunrui; Srinivasan, Dipti

2015-01-01

In this paper, a HADE (hybrid adaptive differential evolution) algorithm is proposed for the identification problem of PEMFC (proton exchange membrane fuel cell). Inspired by biological genetic strategy, a novel adaptive scaling factor and a dynamic crossover probability are presented to improve the adaptive and dynamic performance of differential evolution algorithm. Moreover, two kinds of neighborhood search operations based on the bee colony foraging mechanism are introduced for enhancing local search efficiency. Through testing the benchmark functions, the proposed algorithm exhibits better performance in convergent accuracy and speed. Finally, the HADE algorithm is applied to identify the nonlinear parameters of PEMFC stack model. Through experimental comparison with other identified methods, the PEMFC model based on the HADE algorithm shows better performance. - Highlights: • We propose a hybrid adaptive differential evolution algorithm (HADE). • The search efficiency is enhanced in low and high dimension search space. • The effectiveness is confirmed by testing benchmark functions. • The identification of the PEMFC model is conducted by adopting HADE.

Identification of nonlinear anelastic models

International Nuclear Information System (INIS)

Draganescu, G E; Bereteu, L; Ercuta, A

2008-01-01

A useful nonlinear identification technique applied to the anelastic and rheologic models is presented in this paper. First introduced by Feldman, the method is based on the Hilbert transform, and is currently used for identification of the nonlinear vibrations

Identification and analysis of multi-protein complexes in placenta.

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Fuqiang Wang

Full Text Available Placental malfunction induces pregnancy disorders which contribute to life-threatening complications for both the mother and the fetus. Identification and characterization of placental multi-protein complexes is an important step to integratedly understand the protein-protein interaction networks in placenta which determine placental function. In this study, blue native/sodium dodecyl sulfate polyacrylamide gel electrophoresis (BN/SDS-PAGE and Liquid chromatography-tandem mass spectrometry (LC-MS/MS were used to screen the multi-protein complexes in placenta. 733 unique proteins and 34 known and novel heterooligomeric multi-protein complexes including mitochondrial respiratory chain complexes, integrin complexes, proteasome complexes, histone complex, and heat shock protein complexes were identified. A novel protein complex, which involves clathrin and small conductance calcium-activated potassium (SK channel protein 2, was identified and validated by antibody based gel shift assay, co-immunoprecipitation and immunofluorescence staining. These results suggest that BN/SDS-PAGE, when integrated with LC-MS/MS, is a very powerful and versatile tool for the investigation of placental protein complexes. This work paves the way for deeper functional characterization of the placental protein complexes associated with pregnancy disorders.

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

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Caiping Zhang

2013-05-01

Full Text Available Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters and a nonparametric part [involving the open-circuit voltage (OCV]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

Sediment-Hosted Zinc-Lead Deposits of the World - Database and Grade and Tonnage Models

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Singer, Donald A.; Berger, Vladimir I.; Moring, Barry C.

2009-01-01

This report provides information on sediment-hosted zinc-lead mineral deposits based on the geologic settings that are observed on regional geologic maps. The foundation of mineral-deposit models is information about known deposits. The purpose of this publication is to make this kind of information available in digital form for sediment-hosted zinc-lead deposits. Mineral-deposit models are important in exploration planning and quantitative resource assessments: Grades and tonnages among deposit types are significantly different, and many types occur in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Too few thoroughly explored mineral deposits are available in most local areas for reliable identification of the important geoscience variables, or for robust estimation of undiscovered deposits - thus, we need mineral-deposit models. Globally based deposit models allow recognition of important features because the global models demonstrate how common different features are. Well-designed and -constructed deposit models allow geologists to know from observed geologic environments the possible mineral-deposit types that might exist, and allow economists to determine the possible economic viability of these resources in the region. Thus, mineral-deposit models play the central role in transforming geoscience information to a form useful to policy makers. This publication contains a computer file of information on sediment-hosted zinc-lead deposits from around the world. It also presents new grade and tonnage models for nine types of these deposits and a file allowing locations of all deposits to be plotted in Google Earth. The data are presented in FileMaker Pro, Excel and text files to make the information available to as many as possible. The

A Physiologically Based, Multi-Scale Model of Skeletal Muscle Structure and Function

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Röhrle, O.; Davidson, J. B.; Pullan, A. J.

2012-01-01

Models of skeletal muscle can be classified as phenomenological or biophysical. Phenomenological models predict the muscle’s response to a specified input based on experimental measurements. Prominent phenomenological models are the Hill-type muscle models, which have been incorporated into rigid-body modeling frameworks, and three-dimensional continuum-mechanical models. Biophysically based models attempt to predict the muscle’s response as emerging from the underlying physiology of the system. In this contribution, the conventional biophysically based modeling methodology is extended to include several structural and functional characteristics of skeletal muscle. The result is a physiologically based, multi-scale skeletal muscle finite element model that is capable of representing detailed, geometrical descriptions of skeletal muscle fibers and their grouping. Together with a well-established model of motor-unit recruitment, the electro-physiological behavior of single muscle fibers within motor units is computed and linked to a continuum-mechanical constitutive law. The bridging between the cellular level and the organ level has been achieved via a multi-scale constitutive law and homogenization. The effect of homogenization has been investigated by varying the number of embedded skeletal muscle fibers and/or motor units and computing the resulting exerted muscle forces while applying the same excitatory input. All simulations were conducted using an anatomically realistic finite element model of the tibialis anterior muscle. Given the fact that the underlying electro-physiological cellular muscle model is capable of modeling metabolic fatigue effects such as potassium accumulation in the T-tubular space and inorganic phosphate build-up, the proposed framework provides a novel simulation-based way to investigate muscle behavior ranging from motor-unit recruitment to force generation and fatigue. PMID:22993509

A physiologically based, multi-scale model of skeletal muscle structure and function

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Oliver eRöhrle

2012-09-01

Full Text Available Models of skeletal muscle can be classified as phenomenological or biophysical. Phenomenological models predict the muscle's response to a specified input based on experimental measurements. Prominent phenomenological models are the Hill-type muscle models, which have been incorporated into rigid-body modelling frameworks, and three-dimensional continuum-mechanical models. Biophysically based models attempt to predict the muscle's response as emerging from the underlying physiology of the system. In this contribution, the conventional biophysically based modelling methodology is extended to include several structural and functional characteristics of skeletal muscle. The result is a physiologically based, multi-scale skeletal muscle finite element model that is capable of representing detailed, geometrical descriptions of skeletal muscle fibres and their grouping. Together with a well-established model of motor unit recruitment, the electro-physiological behaviour of single muscle fibres within motor units is computed and linked to a continuum-mechanical constitutive law. The bridging between the cellular level and the organ level has been achieved via a multi-scale constitutive law and homogenisation. The effect of homogenisation has been investigated by varying the number of embedded skeletal muscle fibres and/or motor units and computing the resulting exerted muscle forces while applying the same excitatory input. All simulations were conducted using an anatomically realistic finite element model of the Tibialis Anterior muscle. Given the fact that the underlying electro-physiological cellular muscle model is capable of modelling metabolic fatigue effects such as potassium accumulation in the T-tubular space and inorganic phosphate build-up, the proposed framework provides a novel simulation-based way to investigate muscle behaviour ranging from motor unit recruitment to force generation and fatigue.

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

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Hu, Hai-Feng

2018-01-01

As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM). First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM) to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes. PMID:29621175

Biochemical component identification by plasmonic improved whispering gallery mode optical resonance based sensor

Science.gov (United States)

Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas

2014-05-01

Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.

System identification and the modeling of sailing yachts

Science.gov (United States)

Legursky, Katrina

This research represents an exploration of sailing yacht dynamics with full-scale sailing motion data, physics-based models, and system identification techniques. The goal is to provide a method of obtaining and validating suitable physics-based dynamics models for use in control system design on autonomous sailing platforms, which have the capacity to serve as mobile, long range, high endurance autonomous ocean sensing platforms. The primary contributions of this study to the state-of-the-art are the formulation of a five degree-of-freedom (DOF) linear multi-input multi-output (MIMO) state space model of sailing yacht dynamics, the process for identification of this model from full-scale data, a description of the maneuvers performed during on-water tests, and an analysis method to validate estimated models. The techniques and results described herein can be directly applied to and tested on existing autonomous sailing platforms. A full-scale experiment on a 23ft monohull sailing yacht is developed to collect motion data for physics-based model identification. Measurements include 3 axes of accelerations, velocities, angular rates, and attitude angles in addition to apparent wind speed and direction. The sailing yacht herein is treated as a dynamic system with two control inputs, the rudder angle, deltaR, and the mainsail angle, delta B, which are also measured. Over 20 hours of full scale sailing motion data is collected, representing three sail configurations corresponding to a range of wind speeds: the Full Main and Genoa (abbrev. Genoa) for lower wind speeds, the Full Main and Jib (abbrev. Jib) for mid-range wind speeds, and the Reefed Main and Jib (abbrev. Reef) for the highest wind speeds. The data also covers true wind angles from upwind through a beam reach. A physics-based non-linear model to describe sailing yacht motion is outlined, including descriptions of methods to model the aerodynamics and hydrodynamics of a sailing yacht in surge, sway, roll, and

A method for crack profiles identification in eddy current testing by the multi-directional scan

International Nuclear Information System (INIS)

Kojima, Fumio; Ikeda, Takuya; Nguyen, Doung

2006-01-01

This paper is concerned with a method for identification of crack shape in conducting materials. Multi-directional scanning strategies using Eddy Current Testing is performed for sizing complex natural crackings. Two dimensional measurements by means of multi-directional scan are used in a output least square identifications. (author)

Models of microbiome evolution incorporating host and microbial selection.

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Zeng, Qinglong; Wu, Steven; Sukumaran, Jeet; Rodrigo, Allen

2017-09-25

Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong

Model Optimization Identification Method Based on Closed-loop Operation Data and Process Characteristics Parameters

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Zhiqiang GENG

2014-01-01

Full Text Available Output noise is strongly related to input in closed-loop control system, which makes model identification of closed-loop difficult, even unidentified in practice. The forward channel model is chosen to isolate disturbance from the output noise to input, and identified by optimization the dynamic characteristics of the process based on closed-loop operation data. The characteristics parameters of the process, such as dead time and time constant, are calculated and estimated based on the PI/PID controller parameters and closed-loop process input/output data. And those characteristics parameters are adopted to define the search space of the optimization identification algorithm. PSO-SQP optimization algorithm is applied to integrate the global search ability of PSO with the local search ability of SQP to identify the model parameters of forward channel. The validity of proposed method has been verified by the simulation. The practicability is checked with the PI/PID controller parameter turning based on identified forward channel model.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

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Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block

Prediction of hourly solar radiation with multi-model framework

International Nuclear Information System (INIS)

Wu, Ji; Chan, Chee Keong

2013-01-01

Highlights: • A novel approach to predict solar radiation through the use of clustering paradigms. • Development of prediction models based on the intrinsic pattern observed in each cluster. • Prediction based on proper clustering and selection of model on current time provides better results than other methods. • Experiments were conducted on actual solar radiation data obtained from a weather station in Singapore. - Abstract: In this paper, a novel multi-model prediction framework for prediction of solar radiation is proposed. The framework started with the assumption that there are several patterns embedded in the solar radiation series. To extract the underlying pattern, the solar radiation series is first segmented into smaller subsequences, and the subsequences are further grouped into different clusters. For each cluster, an appropriate prediction model is trained. Hence a procedure for pattern identification is developed to identify the proper pattern that fits the current period. Based on this pattern, the corresponding prediction model is applied to obtain the prediction value. The prediction result of the proposed framework is then compared to other techniques. It is shown that the proposed framework provides superior performance as compared to others

Multi-fractal measures of city-size distributions based on the three-parameter Zipf model

International Nuclear Information System (INIS)

Chen Yanguang; Zhou Yixing

2004-01-01

A multi-fractal framework of urban hierarchies is presented to address the rank-size distribution of cities. The three-parameter Zipf model based on a pair of exponential-type scaling laws is generalized to multi-scale fractal measures. Then according to the equivalent relationship between Zipf's law and Pareto distribution, a set of multi-fractal equations are derived using dual conversion and the Legendre transform. The US city population data coming from the 2000 census are employed to verify the multi-fractal models and the results are satisfying. The multi-fractal measures reveal some strange symmetry regularity of urban systems. While explaining partially the remains of the hierarchical step-like frequency distribution of city sizes suggested by central place theory, the mathematical framework can be interpreted with the entropy-maximizing principle and some related ideas from self-organization

SIMULATING AN EVOLUTIONARY MULTI-AGENT BASED MODEL OF THE STOCK MARKET

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Diana MARICA

2015-08-01

Full Text Available The paper focuses on artificial stock market simulations using a multi-agent model incorporating 2,000 heterogeneous agents interacting on the artificial market. The agents interaction is due to trading activity on the market through a call auction trading mechanism. The multi-agent model uses evolutionary techniques such as genetic programming in order to generate an adaptive and evolving population of agents. Each artificial agent is endowed with wealth and a genetic programming induced trading strategy. The trading strategy evolves and adapts to the new market conditions through a process called breeding, which implies that at each simulation step, new agents with better trading strategies are generated by the model, from recombining the best performing trading strategies and replacing the agents which have the worst performing trading strategies. The simulation model was build with the help of the simulation software Altreva Adaptive Modeler which offers a suitable platform for financial market simulations of evolutionary agent based models, the S&P500 composite index being used as a benchmark for the simulation results.

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

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Suk-Ju Kang

2016-12-01

Full Text Available This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.

The path to host extinction can lead to loss of generalist parasites.

Science.gov (United States)

Farrell, Maxwell J; Stephens, Patrick R; Berrang-Ford, Lea; Gittleman, John L; Davies, T Jonathan

2015-07-01

Host extinction can alter disease transmission dynamics, influence parasite extinction and ultimately change the nature of host-parasite systems. While theory predicts that single-host parasites are among the parasite species most susceptible to extinction following declines in their hosts, documented parasite extinctions are rare. Using a comparative approach, we investigate how the richness of single-host and multi-host parasites is influenced by extinction risk among ungulate and carnivore hosts. Host-parasite associations for free-living carnivores (order Carnivora) and terrestrial ungulates (orders Perissodactyla + Cetartiodactyla minus cetaceans) were merged with host trait data and IUCN Red List status to explore the distribution of single-host and multi-host parasites among threatened and non-threatened hosts. We find that threatened ungulates harbour a higher proportion of single-host parasites compared to non-threatened ungulates, which is explained by decreases in the richness of multi-host parasites. However, among carnivores threat status is not a significant predictor of the proportion of single-host parasites, or the richness of single-host or multi-host parasites. The loss of multi-host parasites from threatened ungulates may be explained by decreased cross-species contact as hosts decline and habitats become fragmented. Among carnivores, threat status may not be important in predicting patterns of parasite specificity because host decline results in equal losses of both single-host parasites and multi-host parasites through reduction in average population density and frequency of cross-species contact. Our results contrast with current models of parasite coextinction and highlight the need for updated theories that are applicable across host groups and account for both inter- and intraspecific contact. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Host-to-host variation of ecological interactions in polymicrobial infections

Science.gov (United States)

Mukherjee, Sayak; Weimer, Kristin E.; Seok, Sang-Cheol; Ray, Will C.; Jayaprakash, C.; Vieland, Veronica J.; Swords, W. Edward; Das, Jayajit

2015-02-01

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

Host-to-host variation of ecological interactions in polymicrobial infections.

Science.gov (United States)

Mukherjee, Sayak; Weimer, Kristin E; Seok, Sang-Cheol; Ray, Will C; Jayaprakash, C; Vieland, Veronica J; Swords, W Edward; Das, Jayajit

2014-12-04

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

Subcutaneous injection of water-soluble multi-walled carbon nanotubes in tumor-bearing mice boosts the host immune activity

International Nuclear Information System (INIS)

Meng Jie; Yang Man; Jia Fumin; Kong Hua; Zhang Weiqi; Xu Haiyan; Wang Chaoying; Xie Sishen; Xing Jianmin

2010-01-01

The immunological responses induced by oxidized water-soluble multi-walled carbon nanotubes on a hepatocarcinoma tumor-bearing mice model via a local administration of subcutaneous injection were investigated. Experimental results show that the subcutaneously injected carbon nanotubes induced significant activation of the complement system, promoted inflammatory cytokines' production and stimulated macrophages' phagocytosis and activation. All of these responses increased the general activity of the host immune system and inhibited the progression of tumor growth.

Subcutaneous injection of water-soluble multi-walled carbon nanotubes in tumor-bearing mice boosts the host immune activity

Science.gov (United States)

Meng, Jie; Yang, Man; Jia, Fumin; Kong, Hua; Zhang, Weiqi; Wang, Chaoying; Xing, Jianmin; Xie, Sishen; Xu, Haiyan

2010-04-01

The immunological responses induced by oxidized water-soluble multi-walled carbon nanotubes on a hepatocarcinoma tumor-bearing mice model via a local administration of subcutaneous injection were investigated. Experimental results show that the subcutaneously injected carbon nanotubes induced significant activation of the complement system, promoted inflammatory cytokines' production and stimulated macrophages' phagocytosis and activation. All of these responses increased the general activity of the host immune system and inhibited the progression of tumor growth.

Subcutaneous injection of water-soluble multi-walled carbon nanotubes in tumor-bearing mice boosts the host immune activity

Energy Technology Data Exchange (ETDEWEB)

Jie, Meng; Man, Yang; Fumin, Jia; Hua, Kong; Weiqi, Zhang; Haiyan, Xu [Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, 5 Dong Dan San Tiao, Beijing 100005 (China); Chaoying, Wang; Sishen, Xie [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 8 Nan San Jie, Zhongguancun, Beijing100080 (China); Xing Jianmin, E-mail: xuhy@pumc.edu.cn [Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, 11 Bei San Huan Dong Lu, Beijing 100029 (China)

2010-04-09

The immunological responses induced by oxidized water-soluble multi-walled carbon nanotubes on a hepatocarcinoma tumor-bearing mice model via a local administration of subcutaneous injection were investigated. Experimental results show that the subcutaneously injected carbon nanotubes induced significant activation of the complement system, promoted inflammatory cytokines' production and stimulated macrophages' phagocytosis and activation. All of these responses increased the general activity of the host immune system and inhibited the progression of tumor growth.

Model Predictive Control Based on System Re-Identification (MPC-SRI) to Control Bio-H2 Production from Biomass

Science.gov (United States)

Wahid, A.; Taqwallah, H. M. H.

2018-03-01

Compressors and a steam reformer are the important units in biohydrogen from biomass plant. The compressors are useful for achieving high-pressure operating conditions while the steam reformer is the main process to produce H2 gas. To control them, in this research used a model predictive control (MPC) expected to have better controller performance than conventional controllers. Because of the explicit model empowerment in MPC, obtaining a better model is the main objective before employing MPC. The common way to get the empirical model is through the identification system, so that obtained a first-order plus dead-time (FOPDT) model. This study has already improved that way since used the system re-identification (SRI) based on closed loop mode. Based on this method the results of the compressor pressure control and temperature control of steam reformer were that MPC based on system re-identification (MPC-SRI) has better performance than MPC without system re-identification (MPCWSRI) and the proportional-integral (PI) controller, by % improvement of 73% against MPCWSRI and 75% against the PI controller.

A proof-of-concept model for the identification of the key events in the infection process with specific reference to Pseudomonas aeruginosa in corneal infections

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Ilias Soumpasis

2015-11-01

Full Text Available Background: It is a common medical practice to characterise an infection based on the causative agent and to adopt therapeutic and prevention strategies targeting the agent itself. However, from an epidemiological perspective, exposure to a microbe can be harmless to a host as a result of low-level exposure or due to host immune response, with opportunistic infection only occurring as a result of changes in the host, pathogen, or surrounding environment. Methods: We have attempted to review systematically the key host, pathogen, and environmental factors that may significantly impact clinical outcomes of exposure to a pathogen, using Pseudomonas aeruginosa eye infection as a case study. Results and discussion: Extended contact lens wearing and compromised hygiene may predispose users to microbial keratitis, which can be a severe and vision-threatening infection. P. aeruginosa has a wide array of virulence-associated genes and sensing systems to initiate and maintain cell populations at the corneal surface and beyond. We have adapted the well-known concept of the epidemiological triangle in combination with the classic risk assessment framework (hazard identification, characterisation, and exposure to develop a conceptual pathway-based model that demonstrates the overlapping relationships between the host, the pathogen, and the environment; and to illustrate the key events in P. aeruginosa eye infection. Conclusion: This strategy differs from traditional approaches that consider potential risk factors in isolation, and hopefully will aid the identification of data and models to inform preventive and therapeutic measures in addition to risk assessment. Furthermore, this may facilitate the identification of knowledge gaps to direct research in areas of greatest impact to avert or mitigate adverse outcomes of infection.

Host-to-host variation of ecological interactions in polymicrobial infections

International Nuclear Information System (INIS)

Mukherjee, Sayak; Seok, Sang-Cheol; Ray, Will C; Jayaprakash, C; Vieland, Veronica J; Das, Jayajit; Weimer, Kristin E; Swords, W Edward

2015-01-01

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host–microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species. (paper)

Appearance-Based Multimodal Human Tracking and Identification for Healthcare in the Digital Home

Directory of Open Access Journals (Sweden)

Mau-Tsuen Yang

2014-08-01

Full Text Available There is an urgent need for intelligent home surveillance systems to provide home security, monitor health conditions, and detect emergencies of family members. One of the fundamental problems to realize the power of these intelligent services is how to detect, track, and identify people at home. Compared to RFID tags that need to be worn all the time, vision-based sensors provide a natural and nonintrusive solution. Observing that body appearance and body build, as well as face, provide valuable cues for human identification, we model and record multi-view faces, full-body colors and shapes of family members in an appearance database by using two Kinects located at a home’s entrance. Then the Kinects and another set of color cameras installed in other parts of the house are used to detect, track, and identify people by matching the captured color images with the registered templates in the appearance database. People are detected and tracked by multisensor fusion (Kinects and color cameras using a Kalman filter that can handle duplicate or partial measurements. People are identified by multimodal fusion (face, body appearance, and silhouette using a track-based majority voting. Moreover, the appearance-based human detection, tracking, and identification modules can cooperate seamlessly and benefit from each other. Experimental results show the effectiveness of the human tracking across multiple sensors and human identification considering the information of multi-view faces, full-body clothes, and silhouettes. The proposed home surveillance system can be applied to domestic applications in digital home security and intelligent healthcare.

Multi-Dimensional Optimization for Cloud Based Multi-Tier Applications

Science.gov (United States)

Jung, Gueyoung

2010-01-01

Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these…

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

Host-pathogen interactions: A cholera surveillance system

Energy Technology Data Exchange (ETDEWEB)

Wright, Aaron T.

2016-02-22

Bacterial pathogen-secreted proteases may play a key role in inhibiting a potentially widespread host-pathogen interaction. Activity-based protein profiling enabled the identification of a major Vibrio cholerae serine protease that limits the ability of a host-derived intestinal lectin to bind to the bacterial pathogen in vivo.

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

Science.gov (United States)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations

A deep learning-based multi-model ensemble method for cancer prediction.

Science.gov (United States)

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling Multi-Mobile Agents System Based on Coalition Signature Mechanism Using UML

Institute of Scientific and Technical Information of China (English)

SUNZhixin; HUANGHaiping; WANGRuchuan

2004-01-01

With the development of electronic commerce and agent techniques, multi-mobile agents cooperation can not only improve the efficiency of electronic business trade, but more importantly, it has a comprehensive applicative value in solving the security issues of mobile agent system. This paper firstly describes the mechanism of multi-mobile agents coalition signature aiming at the system security. Subsequently it brings forward a basic architecture of Multi-mobile agents system (MMAS) based on the design pattern of multi-mobile agents. The paper uses the diagrs_rn of UML, such as use case diagram, class diagram and sequence diagram to build the detailed model of the coalition signature and multi-mobile agents cooperation results. Through security analysis, we find that multimobile agents cooperation and interaction can solve some security problems of mobile agents in transfer, and also it can improve the efficiency of business trade. These results indicate that MMAS has a high security performance and can be widely used in E-commerce trade.

A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena

Directory of Open Access Journals (Sweden)

Jianxun Zhang

2017-10-01

Full Text Available A lithium-Ion battery is a typical degradation product, and its performance will deteriorate over time. In its degradation process, regeneration phenomena have been frequently encountered, which affect both the degradation state and rate. In this paper, we focus on how to build the degradation model and estimate the lifetime. Toward this end, we first propose a multi-phase stochastic degradation model with random jumps based on the Wiener process, where the multi-phase model and random jumps at the changing point are used to describe the variation of degradation rate and state caused by regeneration phenomena accordingly. Owing to the complex structure and random variables, the traditional Maximum Likelihood Estimation (MLE is not suitable for the proposed model. In this case, we treat these random variables as latent parameters, and then develop an approach for model identification based on expectation conditional maximum (ECM algorithm. Moreover, depending on the proposed model, how to estimate the lifetime with fixed changing point is presented via the time-space transformation technique, and the approximate analytical solution is derived. Finally, a numerical simulation and a practical case are provided for illustration.

Multi-granularity immunization strategy based on SIRS model in scale-free network

Science.gov (United States)

Nian, Fuzhong; Wang, Ke

2015-04-01

In this paper, a new immunization strategy was established to prevent the epidemic spreading based on the principle of "Multi-granularity" and "Pre-warning Mechanism", which send different pre-warning signal with the risk rank of the susceptible node to be infected. The pre-warning means there is a higher risk that the susceptible node is more likely to be infected. The multi-granularity means the susceptible node is linked with multi-infected nodes. In our model, the effect of the different situation of the multi-granularity immunizations is compared and different spreading rates are adopted to describe the epidemic behavior of nodes. In addition the threshold value of epidemic outbreak is investigated, which makes the result more convincing. The theoretical analysis and the simulations indicate that the proposed immunization strategy is effective and it is also economic and feasible.

Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production

DEFF Research Database (Denmark)

Zhuang, Kai; Herrgard, Markus

2015-01-01

factories. To address this issue, we have developed a comprehensive Multi-scale framework for modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes and economic impact assessment. We...... investment in a new bio-based chemical industry, there is a need for assessing the technological, economic, and environmental potentials of combinations of biomass feedstocks, biochemical products, bioprocess technologies, and metabolic engineering approaches in the early phase of development of cell...... demonstrate the use of the MuSIC framework in a case study where two major polymer precursors (1,3-propanediol and 3-hydroxypropionic acid) are produced from two biomass feedstocks (corn-based glucose and soy-based glycerol) through 66 proposed biosynthetic pathways in two host organisms (Escherichia coli...

Evaluation for Bearing Wear States Based on Online Oil Multi-Parameters Monitoring

Directory of Open Access Journals (Sweden)

Si-Yuan Wang

2018-04-01

Full Text Available As bearings are critical components of a mechanical system, it is important to characterize their wear states and evaluate health conditions. In this paper, a novel approach for analyzing the relationship between online oil multi-parameter monitoring samples and bearing wear states has been proposed based on an improved gray k-means clustering model (G-KCM. First, an online monitoring system with multiple sensors for bearings is established, obtaining oil multi-parameter data and vibration signals for bearings through the whole lifetime. Secondly, a gray correlation degree distance matrix is generated using a gray correlation model (GCM to express the relationship of oil monitoring samples at different times and then a KCM is applied to cluster the matrix. Analysis and experimental results show that there is an obvious correspondence that state changing coincides basically in time between the lubricants’ multi-parameters and the bearings’ wear states. It also has shown that online oil samples with multi-parameters have early wear failure prediction ability for bearings superior to vibration signals. It is expected to realize online oil monitoring and evaluation for bearing health condition and to provide a novel approach for early identification of bearing-related failure modes.

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Directory of Open Access Journals (Sweden)

Yanning Zhang

2015-04-01

Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Directory of Open Access Journals (Sweden)

Ayedh Alqahtani

2016-01-01

Full Text Available Solar photovoltaic (PV energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

The elaboration of a manufacturing flow connectivity model, based on Multi Agent System

Directory of Open Access Journals (Sweden)

Fahhama Lamyae

2017-01-01

The aim of this paper was to establish a model of the industrial flow connectivity; Afterward, we’ve detailed a network configuration model based on the multi-agents systems, to study the interactions between all the actors and give a more realistic vision onto manufacturing coordination in the supply chain.

Systems integration of biodefense omics data for analysis of pathogen-host interactions and identification of potential targets.

Directory of Open Access Journals (Sweden)

Peter B McGarvey

2009-09-01

Full Text Available The NIAID (National Institute for Allergy and Infectious Diseases Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1 The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells infected by different bacterial (Bacillus anthracis and Salmonella typhimurium and viral (orthopox pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2 The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3 Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Science.gov (United States)

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes.

Directory of Open Access Journals (Sweden)

Samuel Sunghwan Cho

Full Text Available Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs. However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods

A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model

International Nuclear Information System (INIS)

Wei, Zhongbao; Zhao, Jiyun; Ji, Dongxu; Tseng, King Jet

2017-01-01

Highlights: •SOC and capacity are dually estimated with online adapted battery model. •Model identification and state dual estimate are fully decoupled. •Multiple timescales are used to improve estimation accuracy and stability. •The proposed method is verified with lab-scale experiments. •The proposed method is applicable to different battery chemistries. -- Abstract: Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application.

A Multi-Model Approach for System Diagnosis

DEFF Research Database (Denmark)

Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Bækgaard, Mikkel Ask Buur

2007-01-01

A multi-model approach for system diagnosis is presented in this paper. The relation with fault diagnosis as well as performance validation is considered. The approach is based on testing a number of pre-described models and find which one is the best. It is based on an active approach......,i.e. an auxiliary input to the system is applied. The multi-model approach is applied on a wind turbine system....

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

OpenAIRE

Alexandridis, Konstantinos T.; Pijanowski, Bryan C.

2002-01-01

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving g...

A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery

Science.gov (United States)

Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

2017-11-01

State of energy (SOE) is an important index for the electrochemical energy storage system in electric vehicles. In this paper, a robust state of energy estimation method in combination with a physical model parameter identification method is proposed to achieve accurate battery state estimation at different operating conditions and different aging stages. A physics-based fractional order model with variable solid-state diffusivity (FOM-VSSD) is used to characterize the dynamic performance of a LiFePO4/graphite battery. In order to update the model parameter automatically at different aging stages, a multi-step model parameter identification method based on the lexicographic optimization is especially designed for the electric vehicle operating conditions. As the battery available energy changes with different applied load current profiles, the relationship between the remaining energy loss and the state of charge, the average current as well as the average squared current is modeled. The SOE with different operating conditions and different aging stages are estimated based on an adaptive fractional order extended Kalman filter (AFEKF). Validation results show that the overall SOE estimation error is within ±5%. The proposed method is suitable for the electric vehicle online applications.

The Time Division Multi-Channel Communication Model and the Correlative Protocol Based on Quantum Time Division Multi-Channel Communication

International Nuclear Information System (INIS)

Liu Xiao-Hui; Pei Chang-Xing; Nie Min

2010-01-01

Based on the classical time division multi-channel communication theory, we present a scheme of quantum time-division multi-channel communication (QTDMC). Moreover, the model of quantum time division switch (QTDS) and correlative protocol of QTDMC are proposed. The quantum bit error rate (QBER) is analyzed and the QBER simulation test is performed. The scheme shows that the QTDS can carry out multi-user communication through quantum channel, the QBER can also reach the reliability requirement of communication, and the protocol of QTDMC has high practicability and transplantable. The scheme of QTDS may play an important role in the establishment of quantum communication in a large scale in the future. (general)

Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models

Science.gov (United States)

Wen, Xian-Huan; Gómez-Hernández, J. Jaime

1998-03-01

The macrodispersion of an inert solute in a 2-D heterogeneous porous media is estimated numerically in a series of fields of varying heterogeneity. Four different random function (RF) models are used to model log-transmissivity (ln T) spatial variability, and for each of these models, ln T variance is varied from 0.1 to 2.0. The four RF models share the same univariate Gaussian histogram and the same isotropic covariance, but differ from one another in terms of the spatial connectivity patterns at extreme transmissivity values. More specifically, model A is a multivariate Gaussian model for which, by definition, extreme values (both high and low) are spatially uncorrelated. The other three models are non-multi-Gaussian: model B with high connectivity of high extreme values, model C with high connectivity of low extreme values, and model D with high connectivities of both high and low extreme values. Residence time distributions (RTDs) and macrodispersivities (longitudinal and transverse) are computed on ln T fields corresponding to the different RF models, for two different flow directions and at several scales. They are compared with each other, as well as with predicted values based on first-order analytical results. Numerically derived RTDs and macrodispersivities for the multi-Gaussian model are in good agreement with analytically derived values using first-order theories for log-transmissivity variance up to 2.0. The results from the non-multi-Gaussian models differ from each other and deviate largely from the multi-Gaussian results even when ln T variance is small. RTDs in non-multi-Gaussian realizations with high connectivity at high extreme values display earlier breakthrough than in multi-Gaussian realizations, whereas later breakthrough and longer tails are observed for RTDs from non-multi-Gaussian realizations with high connectivity at low extreme values. Longitudinal macrodispersivities in the non-multi-Gaussian realizations are, in general, larger than

Combining Host-based and network-based intrusion detection system

African Journals Online (AJOL)

These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

It's the parameters, stupid! Moving beyond multi-model and multi-physics approaches to characterize and reduce predictive uncertainty in process-based hydrological models

Science.gov (United States)

Clark, Martyn; Samaniego, Luis; Freer, Jim

2014-05-01

Multi-model and multi-physics approaches are a popular tool in environmental modelling, with many studies focusing on optimally combining output from multiple model simulations to reduce predictive errors and better characterize predictive uncertainty. However, a careful and systematic analysis of different hydrological models reveals that individual models are simply small permutations of a master modeling template, and inter-model differences are overwhelmed by uncertainty in the choice of the parameter values in the model equations. Furthermore, inter-model differences do not explicitly represent the uncertainty in modeling a given process, leading to many situations where different models provide the wrong results for the same reasons. In other cases, the available morphological data does not support the very fine spatial discretization of the landscape that typifies many modern applications of process-based models. To make the uncertainty characterization problem worse, the uncertain parameter values in process-based models are often fixed (hard-coded), and the models lack the agility necessary to represent the tremendous heterogeneity in natural systems. This presentation summarizes results from a systematic analysis of uncertainty in process-based hydrological models, where we explicitly analyze the myriad of subjective decisions made throughout both the model development and parameter estimation process. Results show that much of the uncertainty is aleatory in nature - given a "complete" representation of dominant hydrologic processes, uncertainty in process parameterizations can be represented using an ensemble of model parameters. Epistemic uncertainty associated with process interactions and scaling behavior is still important, and these uncertainties can be represented using an ensemble of different spatial configurations. Finally, uncertainty in forcing data can be represented using ensemble methods for spatial meteorological analysis. Our systematic

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

Science.gov (United States)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A

Climate forcing of an emerging pathogenic fungus across a montane multi-host community.

Science.gov (United States)

Clare, Frances C; Halder, Julia B; Daniel, Olivia; Bielby, Jon; Semenov, Mikhail A; Jombart, Thibaut; Loyau, Adeline; Schmeller, Dirk S; Cunningham, Andrew A; Rowcliffe, Marcus; Garner, Trenton W J; Bosch, Jaime; Fisher, Matthew C

2016-12-05

Changes in the timings of seasonality as a result of anthropogenic climate change are predicted to occur over the coming decades. While this is expected to have widespread impacts on the dynamics of infectious disease through environmental forcing, empirical data are lacking. Here, we investigated whether seasonality, specifically the timing of spring ice-thaw, affected susceptibility to infection by the emerging pathogenic fungus Batrachochytrium dendrobatidis (Bd) across a montane community of amphibians that are suffering declines and extirpations as a consequence of this infection. We found a robust temporal association between the timing of the spring thaw and Bd infection in two host species, where we show that an early onset of spring forced high prevalences of infection. A third highly susceptible species (the midwife toad, Alytes obstetricans) maintained a high prevalence of infection independent of time of spring thaw. Our data show that perennially overwintering midwife toad larvae may act as a year-round reservoir of infection with variation in time of spring thaw determining the extent to which infection spills over into sympatric species. We used future temperature projections based on global climate models to demonstrate that the timing of spring thaw in this region will advance markedly by the 2050s, indicating that climate change will further force the severity of infection. Our findings on the effect of annual variability on multi-host infection dynamics show that the community-level impact of fungal infectious disease on biodiversity will need to be re-evaluated in the face of climate change.This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'. © 2016 The Authors.

Tip-tilt disturbance model identification based on non-linear least squares fitting for Linear Quadratic Gaussian control

Science.gov (United States)

Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing

2018-05-01

We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.

A multi-resolution envelope-power based model for speech intelligibility

DEFF Research Database (Denmark)

Jørgensen, Søren; Ewert, Stephan D.; Dau, Torsten

2013-01-01

The speech-based envelope power spectrum model (sEPSM) presented by Jørgensen and Dau [(2011). J. Acoust. Soc. Am. 130, 1475-1487] estimates the envelope power signal-to-noise ratio (SNRenv) after modulation-frequency selective processing. Changes in this metric were shown to account well...... to conditions with stationary interferers, due to the long-term integration of the envelope power, and cannot account for increased intelligibility typically obtained with fluctuating maskers. Here, a multi-resolution version of the sEPSM is presented where the SNRenv is estimated in temporal segments...... with a modulation-filter dependent duration. The multi-resolution sEPSM is demonstrated to account for intelligibility obtained in conditions with stationary and fluctuating interferers, and noisy speech distorted by reverberation or spectral subtraction. The results support the hypothesis that the SNRenv...

Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis

Science.gov (United States)

Cao, Pei; Qi, Shuai; Tang, J.

2018-03-01

The impedance/admittance measurements of a piezoelectric transducer bonded to or embedded in a host structure can be used as damage indicator. When a credible model of the healthy structure, such as the finite element model, is available, using the impedance/admittance change information as input, it is possible to identify both the location and severity of damage. The inverse analysis, however, may be under-determined as the number of unknowns in high-frequency analysis is usually large while available input information is limited. The fundamental challenge thus is how to find a small set of solutions that cover the true damage scenario. In this research we cast the damage identification problem into a multi-objective optimization framework to tackle this challenge. With damage locations and severities as unknown variables, one of the objective functions is the difference between impedance-based model prediction in the parametric space and the actual measurements. Considering that damage occurrence generally affects only a small number of elements, we choose the sparsity of the unknown variables as another objective function, deliberately, the l 0 norm. Subsequently, a multi-objective Dividing RECTangles (DIRECT) algorithm is developed to facilitate the inverse analysis where the sparsity is further emphasized by sigmoid transformation. As a deterministic technique, this approach yields results that are repeatable and conclusive. In addition, only one algorithmic parameter, the number of function evaluations, is needed. Numerical and experimental case studies demonstrate that the proposed framework is capable of obtaining high-quality damage identification solutions with limited measurement information.

System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

Energy Technology Data Exchange (ETDEWEB)

Snyder-Talkington, Brandi N. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Dymacek, Julian [Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6070 (United States); Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Porter, Dale W.; Wolfarth, Michael G.; Mercer, Robert R. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Pacurari, Maricica [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Denvir, James [Department of Biochemistry and Microbiology, Marshall University, Huntington, WV 25755 (United States); Castranova, Vincent [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Qian, Yong, E-mail: yaq2@cdc.gov [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Guo, Nancy L., E-mail: lguo@hsc.wvu.edu [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States)

2013-10-15

The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung

System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

International Nuclear Information System (INIS)

Snyder-Talkington, Brandi N.; Dymacek, Julian; Porter, Dale W.; Wolfarth, Michael G.; Mercer, Robert R.; Pacurari, Maricica; Denvir, James; Castranova, Vincent; Qian, Yong; Guo, Nancy L.

2013-01-01

The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung

The effects of host-feeding on stability of discrete-time host-parasitoid population dynamic models.

Science.gov (United States)

Emerick, Brooks; Singh, Abhyudai

2016-02-01

Discrete-time models are the traditional approach for capturing population dynamics of a host-parasitoid system. Recent work has introduced a semi-discrete framework for obtaining model update functions that connect host-parasitoid population levels from year-to-year. In particular, this framework uses differential equations to describe the host-parasitoid interaction during the time of year when they come in contact, allowing specific behaviors to be mechanistically incorporated. We use the semi-discrete approach to study the effects of host-feeding, which occurs when a parasitoid consumes a potential host larva without ovipositing. We find that host-feeding by itself cannot stabilize the system, and both populations exhibit behavior similar to the Nicholson-Bailey model. However, when combined with stabilizing mechanisms such as density-dependent host mortality, host-feeding contracts the region of parameter space that allows for a stable host-parasitoid equilibrium. In contrast, when combined with a density-dependent parasitoid attack rate, host-feeding expands the non-zero equilibrium stability region. Our results show that host-feeding causes inefficiency in the parasitoid population, which yields a higher population of hosts per generation. This suggests that host-feeding may have limited long-term impact in terms of suppressing host levels for biological control applications. Copyright © 2015 Elsevier Inc. All rights reserved.

Multi-Wavelength Studies on H2O Maser Host Galaxies J. S. Zhang ...

Indian Academy of Sciences (India)

on two projects: X-ray data analysis of individual maser source using. X-ray penetrability to explore maser host obscured AGN; multi- wavelength ... Figure 1. Adaptively smoothed three-color image in 0.3–8.0keV and spectra with fitting ... It provides a perspective to improve the accuracy of the Hubble constant H0 and to.

A Multi-layer Dynamic Model for Coordination Based Group Decision Making in Water Resource Allocation and Scheduling

Science.gov (United States)

Huang, Wei; Zhang, Xingnan; Li, Chenming; Wang, Jianying

Management of group decision-making is an important issue in water source management development. In order to overcome the defects in lacking of effective communication and cooperation in the existing decision-making models, this paper proposes a multi-layer dynamic model for coordination in water resource allocation and scheduling based group decision making. By introducing the scheme-recognized cooperative satisfaction index and scheme-adjusted rationality index, the proposed model can solve the problem of poor convergence of multi-round decision-making process in water resource allocation and scheduling. Furthermore, the problem about coordination of limited resources-based group decision-making process can be solved based on the effectiveness of distance-based group of conflict resolution. The simulation results show that the proposed model has better convergence than the existing models.

FPGA Implementation for GMM-Based Speaker Identification

Directory of Open Access Journals (Sweden)

Phaklen EhKan

2011-01-01

Full Text Available In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM, then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.

A semi-automatic image-based close range 3D modeling pipeline using a multi-camera configuration.

Science.gov (United States)

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum.

Identification of Host Defense-Related Proteins Using Label-Free Quantitative Proteomic Analysis of Milk Whey from Cows with Staphylococcus aureus Subclinical Mastitis

Directory of Open Access Journals (Sweden)

Shaimaa Abdelmegid

2017-12-01

Full Text Available Staphylococcus aureus is the most common contagious pathogen associated with bovine subclinical mastitis. Current diagnosis of S. aureus mastitis is based on bacteriological culture of milk samples and somatic cell counts, which lack either sensitivity or specificity. Identification of milk proteins that contribute to host defense and their variable responses to pathogenic stimuli would enable the characterization of putative biomarkers of subclinical mastitis. To accomplish this, milk whey samples from healthy and mastitic dairy cows were analyzed using a label-free quantitative proteomics approach. In total, 90 proteins were identified, of which 25 showed significant differential abundance between healthy and mastitic samples. In silico functional analyses indicated the involvement of the differentially abundant proteins in biological mechanisms and signaling pathways related to host defense including pathogen-recognition, direct antimicrobial function, and the acute-phase response. This proteomics and bioinformatics analysis not only facilitates the identification of putative biomarkers of S. aureus subclinical mastitis but also recapitulates previous findings demonstrating the abundance of host defense proteins in intramammary infection. All mass spectrometry data are available via ProteomeXchange with identifier PXD007516.

Sustainable Manufacturing via Multi-Scale, Physics-Based Process Modeling and Manufacturing-Informed Design

Energy Technology Data Exchange (ETDEWEB)

None

2017-04-01

This factsheet describes a project that developed and demonstrated a new manufacturing-informed design framework that utilizes advanced multi-scale, physics-based process modeling to dramatically improve manufacturing productivity and quality in machining operations while reducing the cost of machined components.

Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer

International Nuclear Information System (INIS)

Wei, Zhongbao; Meng, Shujuan; Xiong, Binyu; Ji, Dongxu; Tseng, King Jet

2016-01-01

Highlights: • Integrated online model identification and SOC estimate is explored. • Noise variances are online estimated in a data-driven way. • Identification bias caused by noise corruption is attenuated. • SOC is online estimated with high accuracy and fast convergence. • Algorithm comparison shows the superiority of proposed method. - Abstract: State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed.

Material properties identification using ultrasonic waves and laser Doppler vibrometer measurements: a multi-input multi-output approach

International Nuclear Information System (INIS)

Longo, R; Vanlanduit, S; Guillaume, P

2013-01-01

In this paper a multi-input multi-output approach able to determine the material properties of homogeneous materials is presented. To do so, an experimental set-up which combines the use of multi harmonic signals with interleaved frequencies and laser Doppler vibrometer measurements has been developed. A modeling technique, based on transmission and reflection measurements, allowed the simultaneous determination of longitudinal wave velocity, density and thickness of the materials under test with high levels of precision and accuracy. (paper)

Dynamic modeling and explicit/multi-parametric MPC control of pressure swing adsorption systems

KAUST Repository

Khajuria, Harish

2011-01-01

Pressure swing adsorption (PSA) is a flexible, albeit complex gas separation system. Due to its inherent nonlinear nature and discontinuous operation, the design of a model based PSA controller, especially with varying operating conditions, is a challenging task. This work focuses on the design of an explicit/multi-parametric model predictive controller for a PSA system. Based on a system involving four adsorbent beds separating 70% H2, 30% CH4 mixture into high purity hydrogen, the key controller objective is to fast track H2 purity to a set point value of 99.99%. To perform this task, a rigorous and systematic framework is employed. First, a high fidelity detailed dynamic model is built to represent the system\\'s real operation, and understand its dynamic behavior. The model is then used to derive appropriate linear models by applying suitable system identification techniques. For the reduced models, a model predictive control (MPC) step is formulated, where latest developments in multi-parametric programming and control are applied to derive a novel explicit MPC controller. To test the performance of the designed controller, closed loop simulations are performed where the dynamic model is used as the virtual plant. Comparison studies of the derived explicit MPC controller are also performed with conventional PID controllers. © 2010 Elsevier Ltd. All rights reserved.

Performance of PC-based charged particle multi-channel spectrometer utilising particle identification

International Nuclear Information System (INIS)

Palla, G.; Sziklai, J.; Trajber, Cs.

1993-12-01

A collaterally expandable charged particle spectrometer based on PC control and particle identification is described. A typical system configuration consisting of two channels are used to test the system performance. (author) 7 refs.; 5 figs

A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Directory of Open Access Journals (Sweden)

Ahmad Jalal

2017-08-01

Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

Lenstronomy: Multi-purpose gravitational lens modeling software package

Science.gov (United States)

Birrer, Simon; Amara, Adam

2018-04-01

Lenstronomy is a multi-purpose open-source gravitational lens modeling python package. Lenstronomy reconstructs the lens mass and surface brightness distributions of strong lensing systems using forward modelling and supports a wide range of analytic lens and light models in arbitrary combination. The software is also able to reconstruct complex extended sources as well as point sources. Lenstronomy is flexible and numerically accurate, with a clear user interface that could be deployed across different platforms. Lenstronomy has been used to derive constraints on dark matter properties in strong lenses, measure the expansion history of the universe with time-delay cosmography, measure cosmic shear with Einstein rings, and decompose quasar and host galaxy light.

Structural system identification: Structural dynamics model validation

Energy Technology Data Exchange (ETDEWEB)

Red-Horse, J.R.

1997-04-01

Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.

Host Event Based Network Monitoring

Energy Technology Data Exchange (ETDEWEB)

Jonathan Chugg

2013-01-01

The purpose of INL’s research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.

Multi-scale modeling of the thermo-hydro- mechanical behaviour of heterogeneous materials. Application to cement-based materials under severe loads

International Nuclear Information System (INIS)

Grondin, Frederic Alain

2005-01-01

The work of modeling presented here relates to the study of the thermo-hydro- mechanical behaviour of porous materials based on hydraulic binder such as concrete, High Performance Concrete or more generally cement-based materials. This work is based on the exploitation of the Digital Concrete model, of the finite element code Symphonie developed in the Scientific and Technical Centre for Building (CSTB), in coupling with the homogenization methods to obtain macroscopic behaviour laws drawn from the Micro-Macro relations. Scales of investigation, macroscopic and microscopic, has been exploited by simulation in order to allow the comprehension fine of the behaviour of cement-based materials according to thermal, hydrous and mechanical loads. It appears necessary to take into account various scales of modeling. In order to study the behaviour of the structure, we are brought to reduce the scale of investigation to study the material more particularly. The research tasks presented suggest a new approach for the identification of the multi-physic behaviour of materials by simulation. In complement of the purely experimental approach, based on observations on the sample with measurements of the apparent parameters on the macroscopic scale, this new approach allows to obtain the fine analysis of elementary mechanisms in acting within the material. These elementary mechanisms are at the origin of the evolution of the macroscopic parameters measured in experimental tests. In this work, coefficients of the thermo-hydro-mechanical behaviour law of porous materials and the equivalent hydraulic conductivity were obtained by a multi-scales approach. Applications has been carried out on the study of the damaged behaviour of cement-based materials, in the objective to determine the elasticity tensor and the permeability tensor of a High Performance Concrete at high temperatures under a mechanical load. Also, the study of the strain evolution of cement-based materials at low

Hysteresis modeling and identification of a dielectric electro-active polymer actuator using an APSO-based nonlinear Preisach NARX fuzzy model

International Nuclear Information System (INIS)

Truong, Bui Ngoc Minh; Nam, Doan Ngoc Chi; Ahn, Kyoung Kwan

2013-01-01

Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals. (paper)

A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China

International Nuclear Information System (INIS)

Liu, Zhexuan; Qiu, Tong; Chen, Bingzhen

2014-01-01

Highlights: • A LCA based biofuel supply chain model considering 3E criteria was proposed. • The model was used to design a supply chain considering three conversion pathways. • An experimental biofuel supply chain for China was designed. • A Pareto-optimal solution surface of this multi-objective problem was obtained. • The designed supply chain was rather robust to price variation. - Abstract: In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a ε-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the

Coastal aquifer management based on surrogate models and multi-objective optimization

Science.gov (United States)

Mantoglou, A.; Kourakos, G.

2011-12-01

The demand for fresh water in coastal areas and islands can be very high, especially in summer months, due to increased local needs and tourism. In order to satisfy demand, a combined management plan is proposed which involves: i) desalinization (if needed) of pumped water to a potable level using reverse osmosis and ii) injection of biologically treated waste water into the aquifer. The management plan is formulated into a multiobjective optimization framework, where simultaneous minimization of economic and environmental costs is desired; subject to a constraint to satisfy demand. The method requires modeling tools, which are able to predict the salinity levels of the aquifer in response to different alternative management scenarios. Variable density models can simulate the interaction between fresh and saltwater; however, they are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNN)]. The surrogate models are trained adaptively during optimization based on a Genetic Algorithm. In the crossover step of the genetic algorithm, each pair of parents generates a pool of offspring. All offspring are evaluated based on the fast surrogate model. Then only the most promising offspring are evaluated based on the exact numerical model. This eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. Three new criteria for selecting the most promising offspring were proposed, which improve the Pareto set and maintain the diversity of the optimum solutions. The method has important advancements compared to previous methods, e.g. alleviation of propagation of errors due to surrogate model approximations. The method is applied to a real coastal aquifer in the island of Santorini which is a very touristy island with high water demands. The results show that the algorithm

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC

Directory of Open Access Journals (Sweden)

Ángel Jesús Molina-Viedma

2018-02-01

Full Text Available The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials.

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC.

Science.gov (United States)

Molina-Viedma, Ángel Jesús; López-Alba, Elías; Felipe-Sesé, Luis; Díaz, Francisco A

2018-02-05

The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC) is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials.

Modal Identification in an Automotive Multi-Component System Using HS 3D-DIC

Science.gov (United States)

López-Alba, Elías; Felipe-Sesé, Luis; Díaz, Francisco A.

2018-01-01

The modal characterization of automotive lighting systems becomes difficult using sensors due to the light weight of the elements which compose the component as well as the intricate access to allocate them. In experimental modal analysis, high speed 3D digital image correlation (HS 3D-DIC) is attracting the attention since it provides full-field contactless measurements of 3D displacements as main advantage over other techniques. Different methodologies have been published that perform modal identification, i.e., natural frequencies, damping ratios, and mode shapes using the full-field information. In this work, experimental modal analysis has been performed in a multi-component automotive lighting system using HS 3D-DIC. Base motion excitation was applied to simulate operating conditions. A recently validated methodology has been employed for modal identification using transmissibility functions, i.e., the transfer functions from base motion tests. Results make it possible to identify local and global behavior of the different elements of injected polymeric and metallic materials. PMID:29401725

Identification of drought in Dhalai river watershed using MCDM and ANN models

Science.gov (United States)

Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

2017-03-01

An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

Dynamic Energy Consumption and Emission Modelling of Container Terminal based on Multi Agents

Directory of Open Access Journals (Sweden)

Hou Jue

2017-01-01

Full Text Available Environmental protection and energy saving pressure press the increasing attention of container terminal operators. In order to comply with the more and more strict environmental regulation, reducing energy consumption and air pollution emissions, meanwhile, optimizing the operation efficiency, which, is an urgent problem to container terminal operator of China. This paper based on the characteristic of Container Terminal Operation System (CTOS, which includes several sections of container product processes, consist of berth allocation problem, truck dispatching problem, yard allocation problem and auxiliary process. Dynamic energy consumption and emissions characteristic of each equipment and process is modelled, this paper presents the architecture of CTOS based on the multi agent system with early-warning model, which is based on multi-class support vector machines (SVM. A simulation on container terminal is built on the JADE platform to support the decision-making of container terminal, which can reduce energy consumption and air pollution emissions, allows the container terminal operator to be more flexible in their decision to meet the Emission Control Area regulation and Green Port Plan of China.

Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification

Science.gov (United States)

Alger, M. J.; Banfield, J. K.; Ong, C. S.; Rudnick, L.; Wong, O. I.; Wolf, C.; Andernach, H.; Norris, R. P.; Shabala, S. S.

2018-05-01

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe (EMU). Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GHz Australian Telescope Large Area Survey (ATLAS) observations of the Chandra Deep Field South (CDFS) and the ESO Large Area ISO Survey South 1 (ELAIS-S1) fields by cross-identifying them with the Spitzer Wide-area Infrared Extragalactic (SWIRE) survey. We train our method with two sets of data: expert cross-identifications of CDFS from the initial ATLAS data release and crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo. We found that a simple strategy of cross-identifying a radio component with the nearest galaxy performs comparably to our more complex methods, though our estimated best-case performance is near 100 per cent. ATLAS contains 87 complex radio sources that have been cross-identified by experts, so there are not enough complex examples to learn how to cross-identify them accurately. Much larger datasets are therefore required for training methods like ours. We also show that training our method on Radio Galaxy Zoo cross-identifications gives comparable results to training on expert cross-identifications, demonstrating the value of crowdsourced training data.

A Formal Model of Trust Chain based on Multi-level Security Policy

OpenAIRE

Kong Xiangying

2013-01-01

Trust chain is the core technology of trusted computing. A formal model of trust chain based on finite state automata theory is proposed. We use communicating sequential processes to describe the system state transition in trust chain and by combining with multi-level security strategy give the definition of trust system and trust decision theorem of trust chain transfer which is proved meantime. Finally, a prototype system is given to show the efficiency of the model.

Ontology-based representation and analysis of host-Brucella interactions.

Science.gov (United States)

Lin, Yu; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Biomedical ontologies are representations of classes of entities in the biomedical domain and how these classes are related in computer- and human-interpretable formats. Ontologies support data standardization and exchange and provide a basis for computer-assisted automated reasoning. IDOBRU is an ontology in the domain of Brucella and brucellosis. Brucella is a Gram-negative intracellular bacterium that causes brucellosis, the most common zoonotic disease in the world. In this study, IDOBRU is used as a platform to model and analyze how the hosts, especially host macrophages, interact with virulent Brucella strains or live attenuated Brucella vaccine strains. Such a study allows us to better integrate and understand intricate Brucella pathogenesis and host immunity mechanisms. Different levels of host-Brucella interactions based on different host cell types and Brucella strains were first defined ontologically. Three important processes of virulent Brucella interacting with host macrophages were represented: Brucella entry into macrophage, intracellular trafficking, and intracellular replication. Two Brucella pathogenesis mechanisms were ontologically represented: Brucella Type IV secretion system that supports intracellular trafficking and replication, and Brucella erythritol metabolism that participates in Brucella intracellular survival and pathogenesis. The host cell death pathway is critical to the outcome of host-Brucella interactions. For better survival and replication, virulent Brucella prevents macrophage cell death. However, live attenuated B. abortus vaccine strain RB51 induces caspase-2-mediated proinflammatory cell death. Brucella-associated cell death processes are represented in IDOBRU. The gene and protein information of 432 manually annotated Brucella virulence factors were represented using the Ontology of Genes and Genomes (OGG) and Protein Ontology (PRO), respectively. Seven inference rules were defined to capture the knowledge of host

Multi-agent based modeling for electric vehicle integration in a distribution network operation

DEFF Research Database (Denmark)

Hu, Junjie; Morais, Hugo; Lind, Morten

2016-01-01

The purpose of this paper is to present a multi-agent based modeling technology for simulating and operating a hierarchical energy management of a power distribution system with focus on EVs integration. The proposed multi-agent system consists of four types of agents: i) Distribution system...... operator (DSO) technical agent and ii) DSO market agents that both belong to the top layer of the hierarchy and their roles are to manage the distribution network by avoiding grid congestions and using congestion prices to coordinate the energy scheduled; iii) Electric vehicle virtual power plant agents...

Multi-gas interaction modeling on decorated semiconductor interfaces: A novel Fermi distribution-based response isotherm and the inverse hard/soft acid/base concept

Science.gov (United States)

Laminack, William; Gole, James

2015-12-01

A unique MEMS/NEMS approach is presented for the modeling of a detection platform for mixed gas interactions. Mixed gas analytes interact with nanostructured decorating metal oxide island sites supported on a microporous silicon substrate. The Inverse Hard/Soft acid/base (IHSAB) concept is used to assess a diversity of conductometric responses for mixed gas interactions as a function of these nanostructured metal oxides. The analyte conductometric responses are well represented using a combination diffusion/absorption-based model for multi-gas interactions where a newly developed response absorption isotherm, based on the Fermi distribution function is applied. A further coupling of this model with the IHSAB concept describes the considerations in modeling of multi-gas mixed analyte-interface, and analyte-analyte interactions. Taking into account the molecular electronic interaction of both the analytes with each other and an extrinsic semiconductor interface we demonstrate how the presence of one gas can enhance or diminish the reversible interaction of a second gas with the extrinsic semiconductor interface. These concepts demonstrate important considerations in the array-based formats for multi-gas sensing and its applications.

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

Science.gov (United States)

Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

2014-01-01

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. © 2014 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc.

A Multi-Omic View of Host-Pathogen-Commensal Interplay in Salmonella-Mediated Intestinal Infection

Energy Technology Data Exchange (ETDEWEB)

Kaiser, Brooke LD; Li, Jie; Sanford, James A.; Kim, Young-Mo; Kronewitter, Scott R.; Jones, Marcus B.; Peterson, Christine; Peterson, Scott N.; Frank, Bryan C.; Purvine, Samuel O.; Brown, Joseph N.; Metz, Thomas O.; Smith, Richard D.; Heffron, Fred; Adkins, Joshua N.

2013-06-26

The potential for commensal microorganisms indigenous to a host (the ‘microbiome’ or ‘microbiota’) to alter infection outcome by influencing host-pathogen interplay is largely unknown. We used a multi-omics “systems” approach, incorporating proteomics, metabolomics, glycomics, and metagenomics, to explore the molecular interplay between the murine host, the pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium), and commensal gut microorganisms during intestinal infection with S. Typhimurium. We find proteomic evidence that S. Typhimurium thrives within the infected 129/SvJ mouse gut without antibiotic pre-treatment, inducing inflammation and disrupting the intestinal microbiome (e.g., suppressing Bacteroidetes and Firmicutes while promoting growth of Salmonella and Enterococcus). Alteration of the host microbiome population structure was highly correlated with gut environmental changes, including the accumulation of metabolites normally consumed by commensal microbiota. Finally, the less characterized phase of S. Typhimurium’s lifecycle was investigated, and both proteomic and glycomic evidence suggests S. Typhimurium may take advantage of increased fucose moieties to metabolize fucose while growing in the gut. The application of multiple omics measurements to Salmonella-induced intestinal inflammation provides insights into complex molecular strategies employed during pathogenesis between host, pathogen, and the microbiome.

Multi-state modeling of biomolecules.

Directory of Open Access Journals (Sweden)

Melanie I Stefan

2014-09-01

Full Text Available Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the "specification problem" and the problem of how to use a computer to simulate the progress of the system over time (the "computation problem". To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus, BioNetGen, the Allosteric Network Compiler, and others. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim, DYNSTOC, RuleMonkey, and the Network-Free Stochastic Simulator (NFSim, and spatial simulators, including Meredys, SRSim, and MCell. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

Multi-patch matching for person re-identification

Science.gov (United States)

Labidi, Hocine; Luo, Sen-Lin; Boubekeur, Mohamed B.; Benlefki, Tarek

2015-08-01

Recognizing a target object across non-overlapping distributed cameras is known in the computer vision community as the problem of person re-identification. In this paper, a multi-patch matching method for person reidentification is presented. Starting from the assumption that: the appearance (clothes) of a person does not change during the time of passing in different cameras field of view , which means the regions with the same color in target image will be identical while crossing cameras. First, we extract distinctive features in the training procedure, where each image target is devised into small patches, the SIFT features and LAB color histograms are computed for each patch. Then we use the KNN approach to detect group of patches with high similarity in the target image and then we use a bi-directional weighted group matching mechanism for the re-identification. Experiments on a challenging VIPeR dataset show that the performances of the proposed method outperform several baselines and state of the art approaches.

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

Science.gov (United States)

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Agent-based model with multi-level herding for complex financial systems

Science.gov (United States)

Chen, Jun-Jie; Tan, Lei; Zheng, Bo

2015-02-01

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

A Novel Approach to Develop the Lower Order Model of Multi-Input Multi-Output System

Science.gov (United States)

Rajalakshmy, P.; Dharmalingam, S.; Jayakumar, J.

2017-10-01

A mathematical model is a virtual entity that uses mathematical language to describe the behavior of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines like physics, biology, and electrical engineering as well as in the social sciences like economics, sociology and political science. Physicists, Engineers, Computer scientists, and Economists use mathematical models most extensively. With the advent of high performance processors and advanced mathematical computations, it is possible to develop high performing simulators for complicated Multi Input Multi Ouptut (MIMO) systems like Quadruple tank systems, Aircrafts, Boilers etc. This paper presents the development of the mathematical model of a 500 MW utility boiler which is a highly complex system. A synergistic combination of operational experience, system identification and lower order modeling philosophy has been effectively used to develop a simplified but accurate model of a circulation system of a utility boiler which is a MIMO system. The results obtained are found to be in good agreement with the physics of the process and with the results obtained through design procedure. The model obtained can be directly used for control system studies and to realize hardware simulators for boiler testing and operator training.

Computationally Efficient Transient Stability Modeling of multi-terminal VSC-HVDC

DEFF Research Database (Denmark)

van der Meer, Arjen A; Rueda-Torres, José; Silva, Filipe Miguel Faria da

2016-01-01

This paper studies the inclusion of averaged VSC-based grid interfaces and HVDC networks into stability type simulations, and compares the accuracy and speed of three multi-terminal DC dynamic models: 1) a state-space based model, 2) a multi-rate improved model, and 3) a reduced-order model...

Automated coronal hole identification via multi-thermal intensity segmentation

Science.gov (United States)

Garton, Tadhg M.; Gallagher, Peter T.; Murray, Sophie A.

2018-01-01

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single Extreme Ultra-Violet passband and magnetogram images to extract CH information. Here, the coronal hole identification via multi-thermal emission recognition algorithm (CHIMERA) is described, which analyses multi-thermal images from the atmospheric image assembly (AIA) onboard the solar dynamics observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 Å, 193 Å, and 211 Å). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms.

Research on The Construction of Flexible Multi-body Dynamics Model based on Virtual Components

Science.gov (United States)

Dong, Z. H.; Ye, X.; Yang, F.

2018-05-01

Focus on the harsh operation condition of space manipulator, which cannot afford relative large collision momentum, this paper proposes a new concept and technology, called soft-contact technology. In order to solve the problem of collision dynamics of flexible multi-body system caused by this technology, this paper also proposes the concepts of virtual components and virtual hinges, and constructs flexible dynamic model based on virtual components, and also studies on its solutions. On this basis, this paper uses NX to carry out model and comparison simulation for space manipulator in 3 different modes. The results show that using the model of multi-rigid body + flexible body hinge + controllable damping can make effective control on amplitude for the force and torque caused by target satellite collision.

Multi locus sequence typing of Chlamydia reveals an association between Chlamydia psittaci genotypes and host species.

Science.gov (United States)

Pannekoek, Yvonne; Dickx, Veerle; Beeckman, Delphine S A; Jolley, Keith A; Keijzers, Wendy C; Vretou, Evangelia; Maiden, Martin C J; Vanrompay, Daisy; van der Ende, Arie

2010-12-02

Chlamydia comprises a group of obligate intracellular bacterial parasites responsible for a variety of diseases in humans and animals, including several zoonoses. Chlamydia trachomatis causes diseases such as trachoma, urogenital infection and lymphogranuloma venereum with severe morbidity. Chlamydia pneumoniae is a common cause of community-acquired respiratory tract infections. Chlamydia psittaci, causing zoonotic pneumonia in humans, is usually hosted by birds, while Chlamydia abortus, causing abortion and fetal death in mammals, including humans, is mainly hosted by goats and sheep. We used multi-locus sequence typing to asses the population structure of Chlamydia. In total, 132 Chlamydia isolates were analyzed, including 60 C. trachomatis, 18 C. pneumoniae, 16 C. abortus, 34 C. psittaci and one of each of C. pecorum, C. caviae, C. muridarum and C. felis. Cluster analyses utilizing the Neighbour-Joining algorithm with the maximum composite likelihood model of concatenated sequences of 7 housekeeping fragments showed that C. psittaci 84/2334 isolated from a parrot grouped together with the C. abortus isolates from goats and sheep. Cluster analyses of the individual alleles showed that in all instances C. psittaci 84/2334 formed one group with C. abortus. Moving 84/2334 from the C. psittaci group to the C. abortus group resulted in a significant increase in the number of fixed differences and elimination of the number of shared mutations between C. psittaci and C. abortus. C. psittaci M56 from a muskrat branched separately from the main group of C. psittaci isolates. C. psittaci genotypes appeared to be associated with host species. The phylogenetic tree of C. psittaci did not follow that of its host bird species, suggesting host species jumps. In conclusion, we report for the first time an association between C. psittaci genotypes with host species.

Multi locus sequence typing of Chlamydia reveals an association between Chlamydia psittaci genotypes and host species.

Directory of Open Access Journals (Sweden)

Yvonne Pannekoek

2010-12-01

Full Text Available Chlamydia comprises a group of obligate intracellular bacterial parasites responsible for a variety of diseases in humans and animals, including several zoonoses. Chlamydia trachomatis causes diseases such as trachoma, urogenital infection and lymphogranuloma venereum with severe morbidity. Chlamydia pneumoniae is a common cause of community-acquired respiratory tract infections. Chlamydia psittaci, causing zoonotic pneumonia in humans, is usually hosted by birds, while Chlamydia abortus, causing abortion and fetal death in mammals, including humans, is mainly hosted by goats and sheep. We used multi-locus sequence typing to asses the population structure of Chlamydia. In total, 132 Chlamydia isolates were analyzed, including 60 C. trachomatis, 18 C. pneumoniae, 16 C. abortus, 34 C. psittaci and one of each of C. pecorum, C. caviae, C. muridarum and C. felis. Cluster analyses utilizing the Neighbour-Joining algorithm with the maximum composite likelihood model of concatenated sequences of 7 housekeeping fragments showed that C. psittaci 84/2334 isolated from a parrot grouped together with the C. abortus isolates from goats and sheep. Cluster analyses of the individual alleles showed that in all instances C. psittaci 84/2334 formed one group with C. abortus. Moving 84/2334 from the C. psittaci group to the C. abortus group resulted in a significant increase in the number of fixed differences and elimination of the number of shared mutations between C. psittaci and C. abortus. C. psittaci M56 from a muskrat branched separately from the main group of C. psittaci isolates. C. psittaci genotypes appeared to be associated with host species. The phylogenetic tree of C. psittaci did not follow that of its host bird species, suggesting host species jumps. In conclusion, we report for the first time an association between C. psittaci genotypes with host species.

Validation-based insertional mutagenesis for identification of Nup214 as a host factor for EV71 replication in RD cells

International Nuclear Information System (INIS)

Wang, Bei; Zhang, XiaoYu; Zhao, Zhendong

2013-01-01

Highlights: •We introduced a new mutagenesis strategy named VBIM to the viral research. •This method can identify either host factors or host restriction factors. •Using VBIM system, we identified Nup214 as a host factor for EV71 replication in RD cells. -- Abstract: Lentiviral validation-based insertional mutagenesis (VBIM) is a sophisticated, forward genetic approach that is used for the investigation of signal transduction in mammalian cells. Using VBIM, we conducted function-based genetic screening for host genes that affect enterovirus 71 (EV71) viral replication. This included host factors that are required for the life cycle of EV71 and host restriction factors that inhibit EV71 replication. Several cell clones, resistant to EV71, were produced using EV71 infection as a selection pressure and the nuclear pore protein 214 (Nup214) was identified as a host factor required for EV71 replication. In SD2-2, the corresponding VBIM lentivirus transformed clone, the expression of endogenous Nup214 was significantly down-regulated by the reverse inserted VBIM promoter. After Cre recombinase-mediated excision of the VBIM promoter, the expression of Nup214 recovered and the clone regained sensitivity to the EV71 infection. Furthermore, over-expression of Nup214 in the cells suggested that Nup214 was promoting EV71 replication. Results of this study indicate that a successful mutagenesis strategy has been established for screening host genes related to viral replication

Validation-based insertional mutagenesis for identification of Nup214 as a host factor for EV71 replication in RD cells

Energy Technology Data Exchange (ETDEWEB)

Wang, Bei; Zhang, XiaoYu; Zhao, Zhendong, E-mail: timjszzd@163.com

2013-08-02

Highlights: •We introduced a new mutagenesis strategy named VBIM to the viral research. •This method can identify either host factors or host restriction factors. •Using VBIM system, we identified Nup214 as a host factor for EV71 replication in RD cells. -- Abstract: Lentiviral validation-based insertional mutagenesis (VBIM) is a sophisticated, forward genetic approach that is used for the investigation of signal transduction in mammalian cells. Using VBIM, we conducted function-based genetic screening for host genes that affect enterovirus 71 (EV71) viral replication. This included host factors that are required for the life cycle of EV71 and host restriction factors that inhibit EV71 replication. Several cell clones, resistant to EV71, were produced using EV71 infection as a selection pressure and the nuclear pore protein 214 (Nup214) was identified as a host factor required for EV71 replication. In SD2-2, the corresponding VBIM lentivirus transformed clone, the expression of endogenous Nup214 was significantly down-regulated by the reverse inserted VBIM promoter. After Cre recombinase-mediated excision of the VBIM promoter, the expression of Nup214 recovered and the clone regained sensitivity to the EV71 infection. Furthermore, over-expression of Nup214 in the cells suggested that Nup214 was promoting EV71 replication. Results of this study indicate that a successful mutagenesis strategy has been established for screening host genes related to viral replication.

Phenomenological model for coupled multi-axial piezoelectricity

Science.gov (United States)

Wei, Yuchen; Pellegrino, Sergio

2018-03-01

A quantitative calibration of an existing phenomenological model for polycrystalline ferroelectric ceramics is presented. The model relies on remnant strain and polarization as independent variables. Innovative experimental and numerical model identification procedures are developed for the characterization of the coupled electro-mechanical, multi-axial nonlinear constitutive law. Experiments were conducted on thin PZT-5A4E plates subjected to cross-thickness electric field. Unimorph structures with different thickness ratios between PZT-5A4E plate and substrate were tested, to subject the piezo plates to coupled electro-mechanical fields. Material state histories in electric field-strain-polarization space and stress-strain-polarization space were recorded. An optimization procedure is employed for the determination of the model parameters, and the calibrated constitutive law predicts both the uncoupled and coupled experimental observations accurately.

Multi-state reliability for coolant pump based on dependent competitive failure model

International Nuclear Information System (INIS)

Shang Yanlong; Cai Qi; Zhao Xinwen; Chen Ling

2013-01-01

By taking into account the effect of degradation due to internal vibration and external shocks. and based on service environment and degradation mechanism of nuclear power plant coolant pump, a multi-state reliability model of coolant pump was proposed for the system that involves competitive failure process between shocks and degradation. Using this model, degradation state probability and system reliability were obtained under the consideration of internal vibration and external shocks for the degraded coolant pump. It provided an effective method to reliability analysis for coolant pump in nuclear power plant based on operating environment. The results can provide a decision making basis for design changing and maintenance optimization. (authors)

Using the Pathogen-Host Interactions database (PHI-base to investigate plant pathogen genomes and genes implicated in virulence

Directory of Open Access Journals (Sweden)

Martin eUrban

2015-08-01

Full Text Available New pathogen-host interaction mechanisms can be revealed by integrating mutant phenotype data with genetic information. PHI-base is a multi-species manually curated database combining peer-reviewed published phenotype data from plant and animal pathogens and gene/protein information in a single database.

Multi-issue Agent Negotiation Based on Fairness

Science.gov (United States)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.

Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

Science.gov (United States)

Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

2016-04-01

The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

International Nuclear Information System (INIS)

Kara, Tolgay; Eker, Ilyas

2004-01-01

Modeling and identification of mechanical systems constitute an essential stage in practical control design and applications. Controllers commanding systems that operate at varying conditions or require high precision operation raise the need for a nonlinear approach in modeling and identification. Most mechanical systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behavior in certain regions of operation. For a multi-mass rotational system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the system operation when the rotation changes direction. The paper presents nonlinear modeling and identification of a DC motor rotating in two directions together with real time experiments. Linear and nonlinear models for the system are obtained for identification purposes, and the major nonlinearities in the system, such as Coulomb friction and dead zone, are investigated and integrated in the nonlinear model. The Hammerstein nonlinear system approach is used for identification of the nonlinear system model. Online identification of the linear and nonlinear system models is performed using the recursive least squares method. Results of the real time experiments are graphically and numerically presented, and the advantages of the nonlinear identification approach are revealed

Extending multi-tenant architectures: a database model for a multi-target support in SaaS applications

Science.gov (United States)

Rico, Antonio; Noguera, Manuel; Garrido, José Luis; Benghazi, Kawtar; Barjis, Joseph

2016-05-01

Multi-tenant architectures (MTAs) are considered a cornerstone in the success of Software as a Service as a new application distribution formula. Multi-tenancy allows multiple customers (i.e. tenants) to be consolidated into the same operational system. This way, tenants run and share the same application instance as well as costs, which are significantly reduced. Functional needs vary from one tenant to another; either companies from different sectors run different types of applications or, although deploying the same functionality, they do differ in the extent of their complexity. In any case, MTA leaves one major concern regarding the companies' data, their privacy and security, which requires special attention to the data layer. In this article, we propose an extended data model that enhances traditional MTAs in respect of this concern. This extension - called multi-target - allows MT applications to host, manage and serve multiple functionalities within the same multi-tenant (MT) environment. The practical deployment of this approach will allow SaaS vendors to target multiple markets or address different levels of functional complexity and yet commercialise just one single MT application. The applicability of the approach is demonstrated via a case study of a real multi-tenancy multi-target (MT2) implementation, called Globalgest.

Host-Associated Differentiation: The Gape-and-Pinch Model

Directory of Open Access Journals (Sweden)

Stephen B. Heard

2012-01-01

Full Text Available Ecological speciation via host shifting has contributed to the astonishing diversity of phytophagous insects. The importance for host shifting of trait differences between alternative host plants is well established, but much less is known about trait variation within hosts. I outline a conceptual model, the “gape-and-pinch” (GAP model, of insect response to host-plant trait variation during host shifting and host-associated differentiation. I offer four hypotheses about insect use of plant trait variation on two alternative hosts, for insects at different stages of host-associated differentiation. Collectively, these hypotheses suggest that insect responses to plant trait variation can favour or oppose critical steps in herbivore diversification. I provide statistical tools for analysing herbivore trait-space use, demonstrate their application for four herbivores of the goldenrods Solidago altissima and S. gigantea, and discuss their broader potential to advance our understanding of diet breadth and ecological speciation in phytophagous insects.

Road MAPs to engineer host microbiomes.

Science.gov (United States)

Oyserman, Ben O; Medema, Marnix H; Raaijmakers, Jos M

2017-12-02

Microbiomes contribute directly or indirectly to host health and fitness. Thus far, investigations into these emergent traits, referred to here as microbiome-associated phenotypes (MAPs), have been primarily qualitative and taxonomy-driven rather than quantitative and trait-based. We present the MAPs-first approach, a theoretical and experimental roadmap that involves quantitative profiling of MAPs across genetically variable hosts and subsequent identification of the underlying mechanisms. We outline strategies for developing 'modular microbiomes'-synthetic microbial consortia that are engineered in concert with the host genotype to confer different but mutually compatible MAPs to a single host or host population. By integrating host and microbial traits, these strategies will facilitate targeted engineering of microbiomes to the benefit of agriculture, human/animal health and biotechnology. Copyright © 2017. Published by Elsevier Ltd.

Xylella fastidiosa: Host Range and Advance in Molecular Identification Techniques

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Baldi, Paolo; La Porta, Nicola

2017-01-01

In the never ending struggle against plant pathogenic bacteria, a major goal is the early identification and classification of infecting microorganisms. Xylella fastidiosa, a Gram-negative bacterium belonging to the family Xanthmonadaceae, is no exception as this pathogen showed a broad range of vectors and host plants, many of which may carry the pathogen for a long time without showing any symptom. Till the last years, most of the diseases caused by X. fastidiosa have been reported from North and South America, but recently a widespread infection of olive quick decline syndrome caused by this fastidious pathogen appeared in Apulia (south-eastern Italy), and several cases of X. fastidiosa infection have been reported in other European Countries. At least five different subspecies of X. fastidiosa have been reported and classified: fastidiosa, multiplex, pauca, sandyi, and tashke. A sixth subspecies (morus) has been recently proposed. Therefore, it is vital to develop fast and reliable methods that allow the pathogen detection during the very early stages of infection, in order to prevent further spreading of this dangerous bacterium. To this purpose, the classical immunological methods such as ELISA and immunofluorescence are not always sensitive enough. However, PCR-based methods exploiting specific primers for the amplification of target regions of genomic DNA have been developed and are becoming a powerful tool for the detection and identification of many species of bacteria. The aim of this review is to illustrate the application of the most commonly used PCR approaches to X. fastidiosa study, ranging from classical PCR, to several PCR-based detection methods: random amplified polymorphic DNA (RAPD), quantitative real-time PCR (qRT-PCR), nested-PCR (N-PCR), immunocapture PCR (IC-PCR), short sequence repeats (SSRs, also called VNTR), single nucleotide polymorphisms (SNPs) and multilocus sequence typing (MLST). Amplification and sequence analysis of specific

Xylella fastidiosa: Host Range and Advance in Molecular Identification Techniques

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Paolo Baldi

2017-06-01

Full Text Available In the never ending struggle against plant pathogenic bacteria, a major goal is the early identification and classification of infecting microorganisms. Xylella fastidiosa, a Gram-negative bacterium belonging to the family Xanthmonadaceae, is no exception as this pathogen showed a broad range of vectors and host plants, many of which may carry the pathogen for a long time without showing any symptom. Till the last years, most of the diseases caused by X. fastidiosa have been reported from North and South America, but recently a widespread infection of olive quick decline syndrome caused by this fastidious pathogen appeared in Apulia (south-eastern Italy, and several cases of X. fastidiosa infection have been reported in other European Countries. At least five different subspecies of X. fastidiosa have been reported and classified: fastidiosa, multiplex, pauca, sandyi, and tashke. A sixth subspecies (morus has been recently proposed. Therefore, it is vital to develop fast and reliable methods that allow the pathogen detection during the very early stages of infection, in order to prevent further spreading of this dangerous bacterium. To this purpose, the classical immunological methods such as ELISA and immunofluorescence are not always sensitive enough. However, PCR-based methods exploiting specific primers for the amplification of target regions of genomic DNA have been developed and are becoming a powerful tool for the detection and identification of many species of bacteria. The aim of this review is to illustrate the application of the most commonly used PCR approaches to X. fastidiosa study, ranging from classical PCR, to several PCR-based detection methods: random amplified polymorphic DNA (RAPD, quantitative real-time PCR (qRT-PCR, nested-PCR (N-PCR, immunocapture PCR (IC-PCR, short sequence repeats (SSRs, also called VNTR, single nucleotide polymorphisms (SNPs and multilocus sequence typing (MLST. Amplification and sequence analysis of

A risk-based multi-objective model for optimal placement of sensors in water distribution system

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Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

2018-02-01

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

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Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty

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Xudong Chen

2018-03-01

Full Text Available This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

Emulating Host-Microbiome Ecosystem of Human Gastrointestinal Tract in Vitro.

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Park, Gun-Seok; Park, Min Hee; Shin, Woojung; Zhao, Connie; Sheikh, Sameer; Oh, So Jung; Kim, Hyun Jung

2017-06-01

The human gut microbiome performs prodigious physiological functions such as production of microbial metabolites, modulation of nutrient digestion and drug metabolism, control of immune system, and prevention of infection. Paradoxically, gut microbiome can also negatively orchestrate the host responses in diseases or chronic disorders, suggesting that the regulated and balanced host-gut microbiome crosstalk is a salient prerequisite in gastrointestinal physiology. To understand the pathophysiological role of host-microbiome crosstalk, it is critical to recreate in vivo relevant models of the host-gut microbiome ecosystem in human. However, controlling the multi-species microbial communities and their uncontrolled growth has remained a notable technical challenge. Furthermore, conventional two-dimensional (2D) or 3D culture systems do not recapitulate multicellular microarchitectures, mechanical dynamics, and tissue-specific functions. Here, we review recent advances and current pitfalls of in vitro and ex vivo models that display human GI functions. We also discuss how the disruptive technologies such as 3D organoids or a human organ-on-a-chip microphysiological system can contribute to better emulate host-gut microbiome crosstalks in health and disease. Finally, the medical and pharmaceutical significance of the gut microbiome-based personalized interventions is underlined as a future perspective.

NHL and RCGA Based Multi-Relational Fuzzy Cognitive Map Modeling for Complex Systems

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Zhen Peng

2015-11-01

Full Text Available In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL and Real Code Genetic Algorithm (RCGA. The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.

Host Plants Identification for Adult Agrotis ipsilon, a Long-Distance Migratory Insect

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Yongqiang Liu

2016-06-01

Full Text Available In this study, we determined the host relationship of Agrotis ipsilon moths by identifying pollen species adhering them during their long-distance migration. Pollen carried by A. ipsilon moths was collected from 2012 to 2014 on a small island in the center of the Bohai Strait, which is a seasonal migration pathway of this pest species. Genomic DNA of single pollen grains was amplified by using whole genome amplification technology, and a portion of the chloroplast rbcL sequence was then amplified from this material. Pollen species were identified by a combination of DNA barcoding and pollen morphology. We found 28 species of pollen from 18 families on the tested moths, mainly from Angiosperm, Dicotyledoneae. From this, we were able to determine that these moths visit woody plants more than herbaceous plants that they carry more pollen in the early and late stages of the migration season, and that the amounts of pollen transportation were related to moth sex, moth body part, and plant species. In general, 31% of female and 26% of male moths were found to be carrying pollen. Amounts of pollen on the proboscis was higher for female than male moths, while the reverse was true for pollen loads on the antennae. This work provides a new approach to study the interactions between noctuid moth and their host plants. Identification of plant hosts for adult moths furthers understanding of the coevolution processes between moths and their host plants.

Adaptive MPC based on MIMO ARX-Laguerre model.

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Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

2017-03-01

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Echinococcus multilocularis and Its Intermediate Host: A Model of Parasite-Host Interplay

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Dominique Angèle Vuitton

2010-01-01

Full Text Available Host-parasite interactions in the E. multilocularis-intermediate host model depend on a subtle balance between cellular immunity, which is responsible for host's resistance towards the metacestode, the larval stage of the parasite, and tolerance induction and maintenance. The pathological features of alveolar echinococcosis. the disease caused by E. multilocularis, are related both to parasitic growth and to host's immune response, leading to fibrosis and necrosis, The disease spectrum is clearly dependent on the genetic background of the host as well as on acquired disturbances of Th1-related immunity. The laminated layer of the metacestode, and especially its carbohydrate components, plays a major role in tolerance induction. Th2-type and anti-inflammatory cytokines, IL-10 and TGF-β, as well as nitric oxide, are involved in the maintenance of tolerance and partial inhibition of cytotoxic mechanisms. Results of studies in the experimental mouse model and in patients suggest that immune modulation with cytokines, such as interferon-α, or with specific antigens could be used in the future to treat patients with alveolar echinococcosis and/or to prevent this very severe parasitic disease.

Analysis of Host Range Restriction Determinants in the Rabbit Model: Comparison of Homologous and Heterologous Rotavirus Infections

Science.gov (United States)

Ciarlet, Max; Estes, Mary K.; Barone, Christopher; Ramig, Robert F.; Conner, Margaret E.

1998-01-01

The main limitation of both the rabbit and mouse models of rotavirus infection is that human rotavirus (HRV) strains do not replicate efficiently in either animal. The identification of individual genes necessary for conferring replication competence in a heterologous host is important to an understanding of the host range restriction of rotavirus infections. We recently reported the identification of the P type of the spike protein VP4 of four lapine rotavirus strains as being P[14]. To determine whether VP4 is involved in host range restriction in rabbits, we evaluated infection in rotavirus antibody-free rabbits inoculated orally with two P[14] HRVs, PA169 (G6) and HAL1166 (G8), and with several other HRV strains and animal rotavirus strains of different P and G types. We also evaluated whether the parental rhesus rotavirus (RRV) (P5B[3], G3) and the derived RRV-HRV reassortant candidate vaccine strains RRV × D (G1), RRV × DS-1 (G2), and RRV × ST3 (G4) would productively infect rabbits. Based on virus shedding, limited replication was observed with the P[14] HRV strains and with the SA11 Cl3 (P[2], G3) and SA11 4F (P6[1], G3) animal rotavirus strains, compared to the homologous ALA strain (P[14], G3). However, even limited infection provided complete protection from rotavirus infection when rabbits were challenged orally 28 days postinoculation (DPI) with 103 50% infective doses of ALA rabbit rotavirus. Other HRVs did not productively infect rabbits and provided no significant protection from challenge, in spite of occasional seroconversion. Simian RRV replicated as efficiently as lapine ALA rotavirus in rabbits and provided complete protection from ALA challenge. Live attenuated RRV reassortant vaccine strains resulted in no, limited, or productive infection of rabbits, but all rabbits were completely protected from heterotypic ALA challenge. The altered replication efficiency of the reassortants in rabbits suggests a role for VP7 in host range restriction

Toxocariasis in Carnivora from Argentinean Patagonia: Species molecular identification, hosts, and geographical distribution

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R.M. Vega

2018-04-01

Full Text Available Twenty four specimens of seven species belonging to the families Felidae, Mustelidae, and Canidae were obtained in Lanín and Nahuel Huapi National Parks from March 1996 to April 2016. Specimens were processed by necropsy in order to contribute to the knowledge of toxocariasis in wild carnivores of Argentinean Patagonia. The only Puma concolor and the seven Leopardus geoffroyi were positive for Toxocara cati. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP of the ITS-1 region from larval and adult DNA was carried out to confirm parasite species identification. This is the first molecular determination of T. cati from wild felids in Argentina and the study also fill gaps about the spatial distribution and hosts for Toxocara cati. Keywords: Toxocara cati, Puma concolor, Leopardus geoffroyi, Molecular identification, Argentina

Molecular Identification of Weed hosts of Tomato yellow leaf curl virus in southeast of Kerman Province

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Kh. Salari

2016-02-01

Full Text Available Introduction Tomato yellow leaf curl virus, TYLCV belongs to the family Geminiviridae and Begomovirus genus (27. In recent years, extensive damage to tomatoes and cucurbits plants in the south and the southeast of Iran has arrived (23. This virus family have circular, and single-stranded DNA genome and are widespread in tropical and subtropical areas (30. They are infected several plant species with economic importance. Begomoviruses are dicot-infecting, whitefly-transmitted viruses with a genome comprised of one or two molecules DNA (5. Up to now, studies have been performed to evaluate the status of distribution, and identification of natural host and assess the genetic diversity, but there is not a comprehensive review about its weed hosts yet. Materials and Methods In this research, The weeds from margins and inside greenhouses and farms of tomato and cucurbit in severely infected areas including Manoojan, Kahnooj, Faryab, Anbrabad and Jiroft to identify weed hosts of the virus in nature, were collected. Identification of collected samples were conducted by botanical specialists. Total DNAs were extracted from leaves according to the method of zhang et al. (1998 and stored at -20 oC. Identification of infected samples were carried out by PCR using degenerate primer pairs PCRv 181/Bc that direct the amplification of˷ 550 bp fragment of mono – and bipartite begomoviruses genome comprising the C-terminal portion of the intergenic region (IR N-terminal portion of the CPgene. PCR were performed in 25 µl reaction volumes containing 1 µl of template DNA, o.5 µl of Taq DNA polymerase Sinaclon (IRAN, 1.2 µl MgCl2, 0.5 µl dNTPs. 1 µM of each forward and reverse primers, 4.3 µl of 10× reaction buffer and 15.5 distilled water. The amplification were performed using a peqSTAR 96x Termal Cycler (Peqlabe, Germany. PCR conditions consisted of initial denaturing 94 oC for 3 min followed by 30 cycles of denaturation at 94 oC for 50s, annealing at

Crack Identification in CFRP Laminated Beams Using Multi-Resolution Modal Teager–Kaiser Energy under Noisy Environments

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Xu, Wei; Cao, Maosen; Ding, Keqin; Radzieński, Maciej; Ostachowicz, Wiesław

2017-01-01

Carbon fiber reinforced polymer laminates are increasingly used in the aerospace and civil engineering fields. Identifying cracks in carbon fiber reinforced polymer laminated beam components is of considerable significance for ensuring the integrity and safety of the whole structures. With the development of high-resolution measurement technologies, mode-shape-based crack identification in such laminated beam components has become an active research focus. Despite its sensitivity to cracks, however, this method is susceptible to noise. To address this deficiency, this study proposes a new concept of multi-resolution modal Teager–Kaiser energy, which is the Teager–Kaiser energy of a mode shape represented in multi-resolution, for identifying cracks in carbon fiber reinforced polymer laminated beams. The efficacy of this concept is analytically demonstrated by identifying cracks in Timoshenko beams with general boundary conditions; and its applicability is validated by diagnosing cracks in a carbon fiber reinforced polymer laminated beam, whose mode shapes are precisely acquired via non-contact measurement using a scanning laser vibrometer. The analytical and experimental results show that multi-resolution modal Teager–Kaiser energy is capable of designating the presence and location of cracks in these beams under noisy environments. This proposed method holds promise for developing crack identification systems for carbon fiber reinforced polymer laminates. PMID:28773016

Exploring complex dynamics in multi agent-based intelligent systems: Theoretical and experimental approaches using the Multi Agent-based Behavioral Economic Landscape (MABEL) model

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Alexandridis, Konstantinos T.

This dissertation adopts a holistic and detailed approach to modeling spatially explicit agent-based artificial intelligent systems, using the Multi Agent-based Behavioral Economic Landscape (MABEL) model. The research questions that addresses stem from the need to understand and analyze the real-world patterns and dynamics of land use change from a coupled human-environmental systems perspective. Describes the systemic, mathematical, statistical, socio-economic and spatial dynamics of the MABEL modeling framework, and provides a wide array of cross-disciplinary modeling applications within the research, decision-making and policy domains. Establishes the symbolic properties of the MABEL model as a Markov decision process, analyzes the decision-theoretic utility and optimization attributes of agents towards comprising statistically and spatially optimal policies and actions, and explores the probabilogic character of the agents' decision-making and inference mechanisms via the use of Bayesian belief and decision networks. Develops and describes a Monte Carlo methodology for experimental replications of agent's decisions regarding complex spatial parcel acquisition and learning. Recognizes the gap on spatially-explicit accuracy assessment techniques for complex spatial models, and proposes an ensemble of statistical tools designed to address this problem. Advanced information assessment techniques such as the Receiver-Operator Characteristic curve, the impurity entropy and Gini functions, and the Bayesian classification functions are proposed. The theoretical foundation for modular Bayesian inference in spatially-explicit multi-agent artificial intelligent systems, and the ensembles of cognitive and scenario assessment modular tools build for the MABEL model are provided. Emphasizes the modularity and robustness as valuable qualitative modeling attributes, and examines the role of robust intelligent modeling as a tool for improving policy-decisions related to land

Modelling within-host spatiotemporal dynamics of invasive bacterial disease.

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Andrew J Grant

2008-04-01

Full Text Available Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS] in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

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Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina; Yilmaz, Ferkan; Dawy, Zaher; Alouini, Mohamed-Slim

2013-01-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

Resource allocation based uplink intercell interference model in multi-carrier networks

KAUST Repository

Tabassum, Hina

2013-06-01

Intercell interference (ICI) is a primary cause for performance limitation in emerging wireless cellular systems due to its highly indeterministic nature. In this paper, we derive an analytical statistical model for the uplink ICI in a multiuser multi-carrier cellular network considering the impact of various uncoordinated scheduling schemes on the locations and transmit powers of the interferers. The derived model applies to generic composite fading distributions and provides a useful computational tool to evaluate key performance metrics such as the network ergodic capacity. The derived model is extended to incorporate coordinated scheduling schemes. A study is then presented to quantify the potential performance gains of coordinated over uncoordinated scheduling schemes under various base station coordination scenarios. Numerical results demonstrate that different frequency allocation patterns significantly impact the network performance depending on the coordination among neighboring base stations. The accuracy of the derived analytical expressions is verified via Monte-Carlo simulations. © 2013 IEEE.

ABM and GIS-based multi-scenarios volcanic evacuation modelling of Merapi

Science.gov (United States)

Jumadi, Carver, Steve; Quincey, Duncan

2016-05-01

Conducting effective evacuation is one of the successful keys to deal with such crisis. Therefore, a plan that considers the probability of the spatial extent of the hazard occurrences is needed. Likewise, the evacuation plan in Merapi is already prepared before the eruption on 2010. However, the plan could not be performed because the eruption magnitude was bigger than it was predicted. In this condition, the extent of the hazardous area was increased larger than the prepared hazard model. Managing such unpredicted situation need adequate information that flexible and adaptable to the current situation. Therefore, we applied an Agent-based Model (ABM) and Geographic Information System (GIS) using multi-scenarios hazard model to support the evacuation management. The methodology and the case study in Merapi is provided.

Mobile Application Identification based on Hidden Markov Model

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Yang Xinyan

2018-01-01

Full Text Available With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.

Modeling and identification for robot motion control

NARCIS (Netherlands)

Kostic, D.; Jager, de A.G.; Steinbuch, M.; Kurfess, T.R.

2004-01-01

This chapter deals with the problems of robot modelling and identification for high-performance model-based motion control. A derivation of robot kinematic and dynamic models was explained. Modelling of friction effects was also discussed. Use of a writing task to establish correctness of the models

Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

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Xiaoya Ma

2015-11-01

Full Text Available As the main feature of land use planning, land use allocation (LUA optimization is an important means of creating a balance between the land-use supply and demand in a region and promoting the sustainable utilization of land resources. In essence, LUA optimization is a multi-objective optimization problem under the land use supply and demand constraints in a region. In order to obtain a better sustainable multi-objective LUA optimization solution, the present study proposes a LUA model based on the multi-objective artificial immune optimization algorithm (MOAIM-LUA model. The main achievements of the present study are as follows: (a the land-use supply and demand factors are analyzed and the constraint conditions of LUA optimization problems are constructed based on the analysis framework of the balance between the land use supply and demand; (b the optimization objectives of LUA optimization problems are defined and modeled using ecosystem service value theory and land rent and price theory; and (c a multi-objective optimization algorithm is designed for solving multi-objective LUA optimization problems based on the novel immune clonal algorithm (NICA. On the basis of the aforementioned achievements, MOAIM-LUA was applied to a real case study of land-use planning in Anlu County, China. Compared to the current land use situation in Anlu County, optimized LUA solutions offer improvements in the social and ecological objective areas. Compared to the existing models, such as the non-dominated sorting genetic algorithm-II, experimental results demonstrate that the model designed in the present study can obtain better non-dominated solution sets and is superior in terms of algorithm stability.

Physics-Based Identification, Modeling and Risk Management for Aeroelastic Flutter and Limit-Cycle Oscillations (LCO), Phase I

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National Aeronautics and Space Administration — The proposed research program will develop a physics-based identification, modeling and risk management infrastructure for aeroelastic transonic flutter and...

Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries

NARCIS (Netherlands)

Sun, F.; Xiong, R.; He, H.; Li, W.; Aussems, J.E.E.

2012-01-01

A model-based dynamic multi-parameter method for peak power estimation is proposed for batteries and battery management systems (BMSs) used in hybrid electric vehicles (HEVs). The available power must be accurately calculated in order to not damage the battery by over charging or over discharging or

Leptospiral outer membrane protein microarray, a novel approach to identification of host ligand-binding proteins.

Science.gov (United States)

Pinne, Marija; Matsunaga, James; Haake, David A

2012-11-01

Leptospirosis is a zoonosis with worldwide distribution caused by pathogenic spirochetes belonging to the genus Leptospira. The leptospiral life cycle involves transmission via freshwater and colonization of the renal tubules of their reservoir hosts. Infection requires adherence to cell surfaces and extracellular matrix components of host tissues. These host-pathogen interactions involve outer membrane proteins (OMPs) expressed on the bacterial surface. In this study, we developed an Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 OMP microarray containing all predicted lipoproteins and transmembrane OMPs. A total of 401 leptospiral genes or their fragments were transcribed and translated in vitro and printed on nitrocellulose-coated glass slides. We investigated the potential of this protein microarray to screen for interactions between leptospiral OMPs and fibronectin (Fn). This approach resulted in the identification of the recently described fibronectin-binding protein, LIC10258 (MFn8, Lsa66), and 14 novel Fn-binding proteins, denoted Microarray Fn-binding proteins (MFns). We confirmed Fn binding of purified recombinant LIC11612 (MFn1), LIC10714 (MFn2), LIC11051 (MFn6), LIC11436 (MFn7), LIC10258 (MFn8, Lsa66), and LIC10537 (MFn9) by far-Western blot assays. Moreover, we obtained specific antibodies to MFn1, MFn7, MFn8 (Lsa66), and MFn9 and demonstrated that MFn1, MFn7, and MFn9 are expressed and surface exposed under in vitro growth conditions. Further, we demonstrated that MFn1, MFn4 (LIC12631, Sph2), and MFn7 enable leptospires to bind fibronectin when expressed in the saprophyte, Leptospira biflexa. Protein microarrays are valuable tools for high-throughput identification of novel host ligand-binding proteins that have the potential to play key roles in the virulence mechanisms of pathogens.

IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion

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Omid Dehzangi

2017-11-01

Full Text Available The wide spread usage of wearable sensors such as in smart watches has provided continuous access to valuable user generated data such as human motion that could be used to identify an individual based on his/her motion patterns such as, gait. Several methods have been suggested to extract various heuristic and high-level features from gait motion data to identify discriminative gait signatures and distinguish the target individual from others. However, the manual and hand crafted feature extraction is error prone and subjective. Furthermore, the motion data collected from inertial sensors have complex structure and the detachment between manual feature extraction module and the predictive learning models might limit the generalization capabilities. In this paper, we propose a novel approach for human gait identification using time-frequency (TF expansion of human gait cycles in order to capture joint 2 dimensional (2D spectral and temporal patterns of gait cycles. Then, we design a deep convolutional neural network (DCNN learning to extract discriminative features from the 2D expanded gait cycles and jointly optimize the identification model and the spectro-temporal features in a discriminative fashion. We collect raw motion data from five inertial sensors placed at the chest, lower-back, right hand wrist, right knee, and right ankle of each human subject synchronously in order to investigate the impact of sensor location on the gait identification performance. We then present two methods for early (input level and late (decision score level multi-sensor fusion to improve the gait identification generalization performance. We specifically propose the minimum error score fusion (MESF method that discriminatively learns the linear fusion weights of individual DCNN scores at the decision level by minimizing the error rate on the training data in an iterative manner. 10 subjects participated in this study and hence, the problem is a 10-class

Mathematical model comparing of the multi-level economics systems

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Brykalov, S. M.; Kryanev, A. V.

2017-12-01

The mathematical model (scheme) of a multi-level comparison of the economic system, characterized by the system of indices, is worked out. In the mathematical model of the multi-level comparison of the economic systems, the indicators of peer review and forecasting of the economic system under consideration can be used. The model can take into account the uncertainty in the estimated values of the parameters or expert estimations. The model uses the multi-criteria approach based on the Pareto solutions.

A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia.

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Simon Alderton

2016-12-01

Full Text Available This paper presents a new agent-based model (ABM for investigating T. b. rhodesiense human African trypanosomiasis (rHAT disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies.The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation.Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245 human infections and 1.83 (S.E. 0.183 cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years was lower amongst cattle owning households (0.079, S.E. 0.017, than those without cattle (0.134, S.E. 0.017. Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155 and school-age children (e.g. 5-10 year old I.R. = 0.239, S.E. 0.041 were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies.ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale.

Sediment-hosted gold deposits of the world: database and grade and tonnage models

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Berger, Vladimir I.; Mosier, Dan L.; Bliss, James D.; Moring, Barry C.

2014-01-01

All sediment-hosted gold deposits (as a single population) share one characteristic—they all have disseminated micron-sized invisible gold in sedimentary rocks. Sediment-hosted gold deposits are recognized in the Great Basin province of the western United States and in China along with a few recognized deposits in Indonesia, Iran, and Malaysia. Three new grade and tonnage models for sediment-hosted gold deposits are presented in this paper: (1) a general sediment-hosted gold type model, (2) a Carlin subtype model, and (3) a Chinese subtype model. These models are based on grade and tonnage data from a database compilation of 118 sediment-hosted gold deposits including a total of 123 global deposits. The new general grade and tonnage model for sediment-hosted gold deposits (n=118) has a median tonnage of 5.7 million metric tonnes (Mt) and a gold grade of 2.9 grams per tonne (g/t). This new grade and tonnage model is remarkable in that the estimated parameters of the resulting grade and tonnage distributions are comparable to the previous model of Mosier and others (1992). A notable change is in the reporting of silver in more than 10 percent of deposits; moreover, the previous model had not considered deposits in China. From this general grade and tonnage model, two significantly different subtypes of sediment-hosted gold deposits are differentiated: Carlin and Chinese. The Carlin subtype includes 88 deposits in the western United States, Indonesia, Iran, and Malaysia, with median tonnage and grade of 7.1 Mt and 2.0 g/t Au, respectively. The silver grade is 0.78 g/t Ag for the 10th percentile of deposits. The Chinese subtype represents 30 deposits in China, with a median tonnage of 3.9 Mt and medium grade of 4.6 g/t Au. Important differences are recognized in the mineralogy and alteration of the two sediment-hosted gold subtypes such as: increased sulfide minerals in the Chinese subtype and decalcification alteration dominant in the Carlin type. We therefore

A procedure for multi-objective optimization of tire design parameters

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Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

Uncovering the drivers of host-associated microbiota with joint species distribution modelling.

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Björk, Johannes R; Hui, Francis K C; O'Hara, Robert B; Montoya, Jose M

2018-06-01

In addition to the processes structuring free-living communities, host-associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host-specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model-based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host-specific factors are in structuring the microbiota; and (c) co-occurrence networks to visualize microbe-to-microbe associations. © 2018 John Wiley & Sons Ltd.

First identification of Echinococcus multilocularis in rodent intermediate hosts in Sweden

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Andrea L. Miller

2016-04-01

Full Text Available Echinococcus multilocularis is a zoonotic tapeworm with a sylvatic lifecycle and an expanding range in Europe. Monitoring efforts following its first identification in 2011 in Sweden have focused on the parasite's definitive host, the red fox (Vulpes vulpes. However, identifying rodent intermediate hosts is important to recognize opportunities for parasite transmission. During 2013–2015, livers from a total of 1566 rodents from four regions in Sweden were examined for E. multilocularis metacestode lesions. Species identity of suspect parasite lesions was confirmed by PCR and sequencing. E. multilocularis positive lesions >6 mm in diameter were also examined histologically. One Microtus agrestis out of 187 (0.5%, 95%CI: 0–2.9%, 8/439 (1.8%, 95%CI: 0.8–3.6% Arvicola amphibius, 0/655 (0%, 95%CI: 0–0.6% Myodes glareolus, and 0/285 (0%, 95%CI: 0–1.3% Apodemus spp. contained E. multilocularis metacestode lesions. Presence of protoscoleces was confirmed in the infected M. agrestis and in three of eight infected A. amphibius. Six of the nine positive rodents were captured from the same field. This is the first report of E. multilocularis in intermediate hosts in Sweden. The cluster of positive rodents in one field shows that local parasite prevalence can be high in Sweden despite overall low national prevalence in foxes (<0.1%. The presence of protoscoleces in infected M. agrestis and A. amphibius indicate these species can serve as competent intermediate hosts in Sweden. However, their relative importance for E. multilocularis transmission in the Swedish environment is not yet possible to assess. In contrast, the negative findings in all M. glareolus and Apodemus spp. suggest that these species are of no importance.

Research of Uncertainty Reasoning in Pineapple Disease Identification System

Science.gov (United States)

Liu, Liqun; Fan, Haifeng

In order to deal with the uncertainty of evidences mostly existing in pineapple disease identification system, a reasoning model based on evidence credibility factor was established. The uncertainty reasoning method is discussed,including: uncertain representation of knowledge, uncertain representation of rules, uncertain representation of multi-evidences and update of reasoning rules. The reasoning can fully reflect the uncertainty in disease identification and reduce the influence of subjective factors on the accuracy of the system.

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

Control strategies for a stochastic model of host-parasite interaction in a seasonal environment.

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Gómez-Corral, A; López García, M

2014-08-07

We examine a nonlinear stochastic model for the parasite load of a single host over a predetermined time interval. We use nonhomogeneous Poisson processes to model the acquisition of parasites, the parasite-induced host mortality, the natural (no parasite-induced) host mortality, and the reproduction and death of parasites within the host. Algebraic results are first obtained on the age-dependent distribution of the number of parasites infesting the host at an arbitrary time t. The interest is in control strategies based on isolation of the host and the use of an anthelmintic at a certain intervention instant t0. This means that the host is free living in a seasonal environment, and it is transferred to a uninfected area at age t0. In the uninfected area, the host does not acquire new parasites, undergoes a treatment to decrease the parasite load, and its natural and parasite-induced mortality are altered. For a suitable selection of t0, we present two control criteria that appropriately balance effectiveness and cost of intervention. Our approach is based on simple probabilistic principles, and it allows us to examine seasonal fluctuations of gastrointestinal nematode burden in growing lambs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identifying the Achilles heel of multi-host pathogens: the concept of keystone ‘host’ species illustrated by Mycobacterium ulcerans transmission

International Nuclear Information System (INIS)

Roche, Benjamin; Eric Benbow, M; Merritt, Richard; Kimbirauskas, Ryan; McIntosh, Mollie; Small, Pamela L C; Williamson, Heather; Guégan, Jean-François

2013-01-01

Pathogens that use multiple host species are an increasing public health issue due to their complex transmission, which makes them difficult to mitigate. Here, we explore the possibility of using networks of ecological interactions among potential host species to identify the particular disease-source species to target to break down transmission of such pathogens. We fit a mathematical model on prevalence data of Mycobacterium ulcerans in western Africa and we show that removing the most abundant taxa for this category of pathogen is not an optimal strategy to decrease the transmission of the mycobacterium within aquatic ecosystems. On the contrary, we reveal that the removal of some taxa, especially Oligochaeta worms, can clearly reduce rates of pathogen transmission, and these should be considered as keystone organisms for its transmission because they lead to a substantial reduction in pathogen prevalence regardless of the network topology. Besides their potential application for the understanding of M. ulcerans ecology, we discuss how networks of species interactions can modulate transmission of multi-host pathogens. (letter)

Multi-gas interaction modeling on decorated semiconductor interfaces: A novel Fermi distribution-based response isotherm and the inverse hard/soft acid/base concept

Energy Technology Data Exchange (ETDEWEB)

Laminack, William [Department of Physics, Georgia Institute of Technology, Atlanta, GA 30332 (United States); Gole, James, E-mail: James.Gole@physics.gatech.edu [Department of Physics, Georgia Institute of Technology, Atlanta, GA 30332 (United States); Department of Mechanical Engineering, Georgia Tech, Atlanta, GA 30332 (United States)

2015-12-30

Graphical abstract: Visual representation of the PS interface interacting with mixed gas configurations. The red dots correspond to nanostructured metal oxides. Each combination of distinct molecules are labeled below the pores, which are oversized in the figure. - Highlights: • First study of mixed gas analytes interacting with a micro-porous silicon substrate. • Responses are represented by a newly developed response absorption isotherm. • This isotherm is modeled on the basis of the Fermi distribution function. • The developing IHSAB concept explains multi-gas analyte–analyte interactions. - Abstract: A unique MEMS/NEMS approach is presented for the modeling of a detection platform for mixed gas interactions. Mixed gas analytes interact with nanostructured decorating metal oxide island sites supported on a microporous silicon substrate. The Inverse Hard/Soft acid/base (IHSAB) concept is used to assess a diversity of conductometric responses for mixed gas interactions as a function of these nanostructured metal oxides. The analyte conductometric responses are well represented using a combination diffusion/absorption-based model for multi-gas interactions where a newly developed response absorption isotherm, based on the Fermi distribution function is applied. A further coupling of this model with the IHSAB concept describes the considerations in modeling of multi-gas mixed analyte–interface, and analyte–analyte interactions. Taking into account the molecular electronic interaction of both the analytes with each other and an extrinsic semiconductor interface we demonstrate how the presence of one gas can enhance or diminish the reversible interaction of a second gas with the extrinsic semiconductor interface. These concepts demonstrate important considerations in the array-based formats for multi-gas sensing and its applications.

Carbon emissions trading scheme exploration in China: A multi-agent-based model

International Nuclear Information System (INIS)

Tang, Ling; Wu, Jiaqian; Yu, Lean; Bao, Qin

2015-01-01

To develop a low-carbon economy, China launched seven pilot programs for carbon emissions trading (CET) in 2011 and plans to establish a nationwide CET mechanism in 2015. This paper formulated a multi-agent-based model to investigate the impacts of different CET designs in order to find the most appropriate one for China. The proposed bottom-up model includes all main economic agents in a general equilibrium framework. The simulation results indicate that (1) CET would effectively reduce carbon emissions, with a certain negative impact on the economy, (2) as for allowance allocation, the grandfathering rule is relatively moderate, while the benchmarking rule is more aggressive, (3) as for the carbon price, when the price level in the secondary CET market is regulated to be around RMB 40 per metric ton, a satisfactory emission mitigation effect can be obtained, (4) the penalty rate is suggested to be carefully designed to balance the economy development and mitigation effect, and (5) subsidy policy for energy technology improvement can effectively reduce carbon emissions without an additional negative impact on the economy. The results also indicate that the proposed novel model is a promising tool for CET policy making and analyses. -- Highlights: •A multi-agent-based model is proposed for carbon emissions trading (CET) in China. •Three agents are included: government, firms in different sectors and households. •The impacts of CET on the economy and environment in China are analyzed. •Different CET designs are simulated to find an appropriate policy for China. •Results confirm the effectiveness of the model and give helpful insights into CET design

Continuous host-macroparasite models with application to aquaculture

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Catherine Bouloux Marquet

2004-11-01

Full Text Available We study a continuous deterministic host-macroparasite system which involves populations of hosts, parasites, and larvae. This system leads to a countable number of partial differential equations that under certain hypotheses, is reduced to finitely many equations. Also we assume hypotheses to close the system and to define the global dynamics for the hosts. Then, we analyze the spatially homogeneous model without demography (aquaculture hypothesis, and show some preliminary results for the spatially structured model.

Multi baryons with flavors in the Skyrme model

Energy Technology Data Exchange (ETDEWEB)

Schat, Carlos L. [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil); Scoccola, Norberto N. [Comision Nacional de Energia Atomica, Buenos Aires (Argentina). Dept. of Physics

1999-07-01

We investigate the possible existence of multi baryons with heavy flavor quantum numbers using the bound state approach to the topological soliton model and the recently proposed approximation for multi skyrmion fields based on rational maps. We use an effective interaction Lagrangian which consistently incorporates both chiral symmetry and the heavy quark symmetry including the corrections up to order {omicron}(1/m{sub Q}). The model predicts some narrow heavy flavored multi baryon states with baryon number four and seven. (author)

Multi baryons with flavors in the Skyrme model

International Nuclear Information System (INIS)

Schat, Carlos L.; Scoccola, Norberto N.

1999-07-01

We investigate the possible existence of multi baryons with heavy flavor quantum numbers using the bound state approach to the topological soliton model and the recently proposed approximation for multi skyrmion fields based on rational maps. We use an effective interaction Lagrangian which consistently incorporates both chiral symmetry and the heavy quark symmetry including the corrections up to order ο(1/m Q ). The model predicts some narrow heavy flavored multi baryon states with baryon number four and seven. (author)

Microfluidic-Based Multi-Organ Platforms for Drug Discovery

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Ahmad Rezaei Kolahchi

2016-09-01

Full Text Available Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study

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P. Stier

2013-03-01

Full Text Available Simulated multi-model "diversity" in aerosol direct radiative forcing estimates is often perceived as a measure of aerosol uncertainty. However, current models used for aerosol radiative forcing calculations vary considerably in model components relevant for forcing calculations and the associated "host-model uncertainties" are generally convoluted with the actual aerosol uncertainty. In this AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in twelve participating models. Even with prescribed aerosol radiative properties, simulated clear-sky and all-sky aerosol radiative forcings show significant diversity. For a purely scattering case with globally constant optical depth of 0.2, the global-mean all-sky top-of-atmosphere radiative forcing is −4.47 Wm−2 and the inter-model standard deviation is 0.55 Wm−2, corresponding to a relative standard deviation of 12%. For a case with partially absorbing aerosol with an aerosol optical depth of 0.2 and single scattering albedo of 0.8, the forcing changes to 1.04 Wm−2, and the standard deviation increases to 1.01 W−2, corresponding to a significant relative standard deviation of 97%. However, the top-of-atmosphere forcing variability owing to absorption (subtracting the scattering case from the case with scattering and absorption is low, with absolute (relative standard deviations of 0.45 Wm−2 (8% clear-sky and 0.62 Wm−2 (11% all-sky. Scaling the forcing standard deviation for a purely scattering case to match the sulfate radiative forcing in the AeroCom Direct Effect experiment demonstrates that host model uncertainties could explain about 36% of the overall sulfate forcing diversity of 0.11 Wm−2 in the AeroCom Direct Radiative Effect experiment. Host model errors in aerosol radiative forcing are largest in regions of uncertain host model

A Parameter Identification Method for Helicopter Noise Source Identification and Physics-Based Semi-Empirical Modeling

Science.gov (United States)

Greenwood, Eric, II; Schmitz, Fredric H.

2010-01-01

A new physics-based parameter identification method for rotor harmonic noise sources is developed using an acoustic inverse simulation technique. This new method allows for the identification of individual rotor harmonic noise sources and allows them to be characterized in terms of their individual non-dimensional governing parameters. This new method is applied to both wind tunnel measurements and ground noise measurements of two-bladed rotors. The method is shown to match the parametric trends of main rotor Blade-Vortex Interaction (BVI) noise, allowing accurate estimates of BVI noise to be made for operating conditions based on a small number of measurements taken at different operating conditions.

Binomial probability distribution model-based protein identification algorithm for tandem mass spectrometry utilizing peak intensity information.

Science.gov (United States)

Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu

2013-01-04

Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ .

Model-based MPC enables curvilinear ILT using either VSB or multi-beam mask writers

Science.gov (United States)

Pang, Linyong; Takatsukasa, Yutetsu; Hara, Daisuke; Pomerantsev, Michael; Su, Bo; Fujimura, Aki

2017-07-01

Inverse Lithography Technology (ILT) is becoming the choice for Optical Proximity Correction (OPC) of advanced technology nodes in IC design and production. Multi-beam mask writers promise significant mask writing time reduction for complex ILT style masks. Before multi-beam mask writers become the main stream working tools in mask production, VSB writers will continue to be the tool of choice to write both curvilinear ILT and Manhattanized ILT masks. To enable VSB mask writers for complex ILT style masks, model-based mask process correction (MB-MPC) is required to do the following: 1). Make reasonable corrections for complex edges for those features that exhibit relatively large deviations from both curvilinear ILT and Manhattanized ILT designs. 2). Control and manage both Edge Placement Errors (EPE) and shot count. 3. Assist in easing the migration to future multi-beam mask writer and serve as an effective backup solution during the transition. In this paper, a solution meeting all those requirements, MB-MPC with GPU acceleration, will be presented. One model calibration per process allows accurate correction regardless of the target mask writer.

Modeling Multi-Level Systems

CERN Document Server

Iordache, Octavian

2011-01-01

This book is devoted to modeling of multi-level complex systems, a challenging domain for engineers, researchers and entrepreneurs, confronted with the transition from learning and adaptability to evolvability and autonomy for technologies, devices and problem solving methods. Chapter 1 introduces the multi-scale and multi-level systems and highlights their presence in different domains of science and technology. Methodologies as, random systems, non-Archimedean analysis, category theory and specific techniques as model categorification and integrative closure, are presented in chapter 2. Chapters 3 and 4 describe polystochastic models, PSM, and their developments. Categorical formulation of integrative closure offers the general PSM framework which serves as a flexible guideline for a large variety of multi-level modeling problems. Focusing on chemical engineering, pharmaceutical and environmental case studies, the chapters 5 to 8 analyze mixing, turbulent dispersion and entropy production for multi-scale sy...

Identification of the same polyomavirus species in different African horseshoe bat species is indicative of short-range host-switching events.

Science.gov (United States)

Carr, Michael; Gonzalez, Gabriel; Sasaki, Michihito; Dool, Serena E; Ito, Kimihito; Ishii, Akihiro; Hang'ombe, Bernard M; Mweene, Aaron S; Teeling, Emma C; Hall, William W; Orba, Yasuko; Sawa, Hirofumi

2017-10-06

Polyomaviruses (PyVs) are considered to be highly host-specific in different mammalian species, with no well-supported evidence for host-switching events. We examined the species diversity and host specificity of PyVs in horseshoe bats (Rhinolophus spp.), a broadly distributed and highly speciose mammalian genus. We annotated six PyV genomes, comprising four new PyV species, based on pairwise identity within the large T antigen (LTAg) coding region. Phylogenetic comparisons revealed two instances of highly related PyV species, one in each of the Alphapolyomavirus and Betapolyomavirus genera, present in different horseshoe bat host species (Rhinolophus blasii and R. simulator), suggestive of short-range host-switching events. The two pairs of Rhinolophus PyVs in different horseshoe bat host species were 99.9 and 88.8 % identical with each other over their respective LTAg coding sequences and thus constitute the same virus species. To corroborate the species identification of the bat hosts, we analysed mitochondrial cytb and a large nuclear intron dataset derived from six independent and neutrally evolving loci for bat taxa of interest. Bayesian estimates of the ages of the most recent common ancestors suggested that the near-identical and more distantly related PyV species diverged approximately 9.1E4 (5E3-2.8E5) and 9.9E6 (4E6-18E6) years before the present, respectively, in contrast to the divergence times of the bat host species: 12.4E6 (10.4E6-15.4E6). Our findings provide evidence that short-range host-switching of PyVs is possible in horseshoe bats, suggesting that PyV transmission between closely related mammalian species can occur.

A multi-reservoir based water-hydroenergy management model for identifying the risk horizon of regional resources-energy policy under uncertainties

International Nuclear Information System (INIS)

Zeng, X.T.; Zhang, S.J.; Feng, J.; Huang, G.H.; Li, Y.P.; Zhang, P.; Chen, J.P.; Li, K.L.

2017-01-01

Highlights: • A multi-reservoir system can handle water/energy deficit, flood and sediment damage. • A MWH model is developed for planning a water allocation and energy generation issue. • A mixed fuzzy-stochastic risk analysis method (MFSR) can handle uncertainties in MWH. • A hybrid MWH model can plan human-recourse-energy with a robust and effective manner. • Results can support adjusting water-energy policy to satisfy increasing demands. - Abstract: In this study, a multi-reservoir based water-hydroenergy management (MWH) model is developed for planning water allocation and hydroenergy generation (WAHG) under uncertainties. A mixed fuzzy-stochastic risk analysis method (MFSR) is introduced to handle objective and subjective uncertainties in MWH model, which can couple fuzzy credibility programming and risk management within a general two-stage context, with aim to reflect the infeasibility risks between expected targets and random second-stage recourse costs. The developed MWH model (embedded by MFSR method) can be applied to a practical study of WAHG issue in Jing River Basin (China), which encounters conflicts between human activity and resource/energy crisis. The construction of water-energy nexus (WEN) is built to reflect integrity of economic development and resource/energy conservation, as well as confronting natural and artificial damages such as water deficit, electricity insufficient, floodwater, high sedimentation deposition contemporarily. Meanwhile, the obtained results with various credibility levels and target-violated risk levels can support generating a robust plan associated with risk control for identification of the optimized water-allocation and hydroenergy-generation alternatives, as well as flood controls. Moreover, results can be beneficial for policymakers to discern the optimal water/sediment release routes, reservoirs’ storage variations (impacted by sediment deposition), electricity supply schedules and system benefit

Settings for Physical Activity – Developing a Site-specific Physical Activity Behavior Model based on Multi-level Intervention Studies

DEFF Research Database (Denmark)

Troelsen, Jens; Klinker, Charlotte Demant; Breum, Lars

Settings for Physical Activity – Developing a Site-specific Physical Activity Behavior Model based on Multi-level Intervention Studies Introduction: Ecological models of health behavior have potential as theoretical framework to comprehend the multiple levels of factors influencing physical...... to be taken into consideration. A theoretical implication of this finding is to develop a site-specific physical activity behavior model adding a layered structure to the ecological model representing the determinants related to the specific site. Support: This study was supported by TrygFonden, Realdania...... activity (PA). The potential is shown by the fact that there has been a dramatic increase in application of ecological models in research and practice. One proposed core principle is that an ecological model is most powerful if the model is behavior-specific. However, based on multi-level interventions...

A metasystem of framework model organisms to study emergence of new host-microbe adaptations.

Science.gov (United States)

Gopalan, Suresh; Ausubel, Frederick M

2008-01-01

An unintended consequence of global industrialization and associated societal rearrangements is new interactions of microbes and potential hosts (especially mammals and plants), providing an opportunity for the rapid emergence of host-microbe adaptation and eventual establishment of new microbe-related diseases. We describe a new model system comprising the model plant Arabidopsis thaliana and several microbes, each representing different modes of interaction, to study such "maladaptations". The model microbes include human and agricultural pathogens and microbes that are commonly considered innocuous. The system has a large knowledge base corresponding to each component organism and is amenable to high-throughput automation assisted perturbation screens for identifying components that modulate host-pathogen interactions. This would aid in the study of emergence and progression of host-microbe maladaptations in a controlled environment.

Multi-stage identification scheme for detecting damage in structures under ambient excitations

International Nuclear Information System (INIS)

Bao, Chunxiao; Li, Zhong-Xian; Hao, Hong

2013-01-01

Structural damage identification methods are critical to the successful application of structural health monitoring (SHM) systems to civil engineering structures. The dynamic response of civil engineering structures is usually characterized by high nonlinearity and non-stationarity. Accordingly, an improved Hilbert–Huang transform (HHT) method which is adaptive, output-only and applicable to system identification of in-service structures under ambient excitations is developed in this study. Based on this method, a multi-stage damage detection scheme including the detection of damage occurrence, damage existence, damage location and the estimation of damage severity is developed. In this scheme, the improved HHT method is used to analyse the structural acceleration response, the obtained instantaneous frequency detects the instant of damage occurrence, the instantaneous phase is sensitive to minor damage and provides reliable damage indication, and the damage indicator developed based on statistical analysis of the Hilbert marginal spectrum detects damage locations. Finally, the response sampled at the detected damage location is continuously analysed to estimate the damage severity. Numerical and experimental studies of frame structures under ambient excitations are performed. The results demonstrate that this scheme accomplishes the above damage detection functions within one flow. It is robust, time efficient, simply implemented and applicable to the real-time SHM of in-service structures. (paper)

Identification of GMS friction model without friction force measurement

International Nuclear Information System (INIS)

Grami, Said; Aissaoui, Hicham

2011-01-01

This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.

Algorithm for Financial Derivatives Evaluation in a Generalized Multi-Heston Model

Directory of Open Access Journals (Sweden)

Dan Negura

2013-02-01

Full Text Available In this paper we show how could a financial derivative be estimated based on an assumed Multi-Heston model support.Keywords: Euler Maruyama discretization method, Monte Carlo simulation, Heston model, Double-Heston model, Multi-Heston model

Multi-agent control system with information fusion based comfort model for smart buildings

International Nuclear Information System (INIS)

Wang, Zhu; Wang, Lingfeng; Dounis, Anastasios I.; Yang, Rui

2012-01-01

Highlights: ► Proposed a model to manage indoor energy and comfort for smart buildings. ► Developed a control system to maximize comfort with minimum energy consumption. ► Information fusion with ordered weighted averaging aggregation is used. ► Multi-agent technology and heuristic intelligent optimization are deployed in developing the control system. -- Abstract: From the perspective of system control, a smart and green building is a large-scale dynamic system with high complexity and a huge amount of information. Proper combination of the available information and effective control of the overall building system turns out to be a big challenge. In this study, we proposed a building indoor energy and comfort management model based on information fusion using ordered weighted averaging (OWA) aggregation. A multi-agent control system with heuristic intelligent optimization is developed to achieve a high level of comfort with the minimum power consumption. Case studies and simulation results are presented and discussed in this paper.

Fish, fans and hydroids: host species of pygmy seahorses

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Bastian Reijnen

2011-06-01

Full Text Available An overview of the octocoral and hydrozoa host species of pygmy seahorses is provided, based on recently collected data for H. bargibanti, H. denise and H. pontohi and literature records. Seven new interspecific host-species associations are recognized, and an overview of the so far documented number of host species is given. Detailed re-examination of octocoral type material and a review of the taxonomic history are included, as a baseline for further studies. The host-specificity and colour morphs of pygmy seahorses are discussed, as well as the validity of (previous identifications and conservations issues.

The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting

Science.gov (United States)

Barbetta, Silvia; Coccia, Gabriele; Moramarco, Tommaso; Brocca, Luca; Todini, Ezio

2017-08-01

This work extends the multi-temporal approach of the Model Conditional Processor (MCP-MT) to the multi-model case and to the four Truncated Normal Distributions (TNDs) approach, demonstrating the improvement on the single-temporal one. The study is framed in the context of probabilistic Bayesian decision-making that is appropriate to take rational decisions on uncertain future outcomes. As opposed to the direct use of deterministic forecasts, the probabilistic forecast identifies a predictive probability density function that represents a fundamental knowledge on future occurrences. The added value of MCP-MT is the identification of the probability that a critical situation will happen within the forecast lead-time and when, more likely, it will occur. MCP-MT is thoroughly tested for both single-model and multi-model configurations at a gauged site on the Tiber River, central Italy. The stages forecasted by two operative deterministic models, STAFOM-RCM and MISDc, are considered for the study. The dataset used for the analysis consists of hourly data from 34 flood events selected on a time series of six years. MCP-MT improves over the original models' forecasts: the peak overestimation and the rising limb delayed forecast, characterizing MISDc and STAFOM-RCM respectively, are significantly mitigated, with a reduced mean error on peak stage from 45 to 5 cm and an increased coefficient of persistence from 0.53 up to 0.75. The results show that MCP-MT outperforms the single-temporal approach and is potentially useful for supporting decision-making because the exceedance probability of hydrometric thresholds within a forecast horizon and the most probable flooding time can be estimated.

Identification of damage in composite structures using Gaussian mixture model-processed Lamb waves

Science.gov (United States)

Wang, Qiang; Ma, Shuxian; Yue, Dong

2018-04-01

Composite materials have comprehensively better properties than traditional materials, and therefore have been more and more widely used, especially because of its higher strength-weight ratio. However, the damage of composite structures is usually varied and complicated. In order to ensure the security of these structures, it is necessary to monitor and distinguish the structural damage in a timely manner. Lamb wave-based structural health monitoring (SHM) has been proved to be effective in online structural damage detection and evaluation; furthermore, the characteristic parameters of the multi-mode Lamb wave varies in response to different types of damage in the composite material. This paper studies the damage identification approach for composite structures using the Lamb wave and the Gaussian mixture model (GMM). The algorithm and principle of the GMM, and the parameter estimation, is introduced. Multi-statistical characteristic parameters of the excited Lamb waves are extracted, and the parameter space with reduced dimensions is adopted by principal component analysis (PCA). The damage identification system using the GMM is then established through training. Experiments on a glass fiber-reinforced epoxy composite laminate plate are conducted to verify the feasibility of the proposed approach in terms of damage classification. The experimental results show that different types of damage can be identified according to the value of the likelihood function of the GMM.

Vortex Tube Modeling Using the System Identification Method

Energy Technology Data Exchange (ETDEWEB)

Han, Jaeyoung; Jeong, Jiwoong; Yu, Sangseok [Chungnam Nat’l Univ., Daejeon (Korea, Republic of); Im, Seokyeon [Tongmyong Univ., Busan (Korea, Republic of)

2017-05-15

In this study, vortex tube system model is developed to predict the temperature of the hot and the cold sides. The vortex tube model is developed based on the system identification method, and the model utilized in this work to design the vortex tube is ARX type (Auto-Regressive with eXtra inputs). The derived polynomial model is validated against experimental data to verify the overall model accuracy. It is also shown that the derived model passes the stability test. It is confirmed that the derived model closely mimics the physical behavior of the vortex tube from both the static and dynamic numerical experiments by changing the angles of the low-temperature side throttle valve, clearly showing temperature separation. These results imply that the system identification based modeling can be a promising approach for the prediction of complex physical systems, including the vortex tube.

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

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Wonki Lee

2018-03-01

Full Text Available The electrocardiogram (ECG waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG. To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns.

Science.gov (United States)

Lee, Wonki; Kim, Seulgee; Kim, Daeeun

2018-03-28

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

Efficient Multi-Valued Bounded Model Checking for LTL over Quasi-Boolean Algebras

Science.gov (United States)

Andrade, Jefferson O.; Kameyama, Yukiyoshi

Multi-valued Model Checking extends classical, two-valued model checking to multi-valued logic such as Quasi-Boolean logic. The added expressivity is useful in dealing with such concepts as incompleteness and uncertainty in target systems, while it comes with the cost of time and space. Chechik and others proposed an efficient reduction from multi-valued model checking problems to two-valued ones, but to the authors' knowledge, no study was done for multi-valued bounded model checking. In this paper, we propose a novel, efficient algorithm for multi-valued bounded model checking. A notable feature of our algorithm is that it is not based on reduction of multi-values into two-values; instead, it generates a single formula which represents multi-valuedness by a suitable encoding, and asks a standard SAT solver to check its satisfiability. Our experimental results show a significant improvement in the number of variables and clauses and also in execution time compared with the reduction-based one.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

Science.gov (United States)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

A data-driven modeling approach to identify disease-specific multi-organ networks driving physiological dysregulation.

Directory of Open Access Journals (Sweden)

Warren D Anderson

2017-07-01

Full Text Available Multiple physiological systems interact throughout the development of a complex disease. Knowledge of the dynamics and connectivity of interactions across physiological systems could facilitate the prevention or mitigation of organ damage underlying complex diseases, many of which are currently refractory to available therapeutics (e.g., hypertension. We studied the regulatory interactions operating within and across organs throughout disease development by integrating in vivo analysis of gene expression dynamics with a reverse engineering approach to infer data-driven dynamic network models of multi-organ gene regulatory influences. We obtained experimental data on the expression of 22 genes across five organs, over a time span that encompassed the development of autonomic nervous system dysfunction and hypertension. We pursued a unique approach for identification of continuous-time models that jointly described the dynamics and structure of multi-organ networks by estimating a sparse subset of ∼12,000 possible gene regulatory interactions. Our analyses revealed that an autonomic dysfunction-specific multi-organ sequence of gene expression activation patterns was associated with a distinct gene regulatory network. We analyzed the model structures for adaptation motifs, and identified disease-specific network motifs involving genes that exhibited aberrant temporal dynamics. Bioinformatic analyses identified disease-specific single nucleotide variants within or near transcription factor binding sites upstream of key genes implicated in maintaining physiological homeostasis. Our approach illustrates a novel framework for investigating the pathogenesis through model-based analysis of multi-organ system dynamics and network properties. Our results yielded novel candidate molecular targets driving the development of cardiovascular disease, metabolic syndrome, and immune dysfunction.

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption.

Science.gov (United States)

Hong, Taehoon; Koo, Choongwan; Kim, Hyunjoong

2012-12-15

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties. Copyright © 2012 Elsevier Ltd. All rights reserved.

Village Building Identification Based on Ensemble Convolutional Neural Networks

Science.gov (United States)

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

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

Directory of Open Access Journals (Sweden)

Jinjun Li

2011-01-01

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

Control-Oriented Modeling and System Identification for Nonlinear Trajectory Tracking Control of a Small-Scale Unmanned Helicopter

Science.gov (United States)

Pourrezaei Khaligh, Sepehr

Model-based control design of small-scale helicopters involves considerable challenges due to their nonlinear and underactuated dynamics with strong couplings between the different degrees-of-freedom (DOFs). Most nonlinear model-based multi-input multi-output (MIMO) control approaches require the dynamic model of the system to be affine-in-control and fully actuated. Since the existing formulations for helicopter nonlinear dynamic model do not meet these requirements, these MIMO approaches cannot be applied for control of helicopters and control designs in the literature mostly use the linearized model of the helicopter dynamics around different trim conditions instead of directly using the nonlinear model. The purpose of this thesis is to derive the 6-DOF nonlinear model of the helicopter in an affine-in-control, non-iterative and square input-output formulation to enable many nonlinear control approaches, that require a control-affine and square model such as the sliding mode control (SMC), to be used for control design of small-scale helicopters. A combination of the first-principles approach and system identification is used to derive this model. To complete the nonlinear model of the helicopter required for the control design, the inverse kinematics of the actuating mechanisms of the main and tail rotors are also derived using an approach suitable for the real-time control applications. The parameters of the new control-oriented formulation are identified using a time-domain system identification strategy and the model is validated using flight test data. A robust sliding mode control (SMC) is then designed using the new formulation of the helicopter dynamics and its robustness to parameter uncertainties and wind disturbances is tested in simulations. Next, a hardware-in-the-loop (HIL) testbed is designed to allow for the control implementation and gain tuning as well as testing the robustness of the controller to external disturbances in a controlled

Application of Metamodels to Identification of Metallic Materials Models

Directory of Open Access Journals (Sweden)

Maciej Pietrzyk

2016-01-01

Full Text Available Improvement of the efficiency of the inverse analysis (IA for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients and additionally for softening due to recrystallization (5 coefficients and for softening and saturation (7 coefficients. Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.

A mathematical modelling framework for linked within-host and between-host dynamics for infections with free-living pathogens in the environment.

Science.gov (United States)

Garira, Winston; Mathebula, Dephney; Netshikweta, Rendani

2014-10-01

In this study we develop a mathematical modelling framework for linking the within-host and between-host dynamics of infections with free-living pathogens in the environment. The resulting linked models are sometimes called immuno-epidemiological models. However, there is still no generalised framework for linking the within-host and between-host dynamics of infectious diseases. Furthermore, for infections with free-living pathogens in the environment, there is an additional stumbling block in that there is a gap in knowledge on how environmental factors (through water, air, soil, food, fomites, etc.) alter many aspects of such infections including susceptibility to infective dose, persistence of infection, pathogen shedding and severity of the disease. In this work, we link the two subsystems (within-host and between-host models) by identifying the within-host and between-host variables and parameters associated with the environmental dynamics of the pathogen and then design a feedback of the variables and parameters across the within-host and between-host models using human schistosomiasis as a case study. We study the mathematical properties of the linked model and show that the model is epidemiologically well-posed. Using results from the analysis of the endemic equilibrium expression, the disease reproductive number R0, and numerical simulations of the full model, we adequately account for the reciprocal influence of the linked within-host and between-host models. In particular, we illustrate that for human schistosomiasis, the outcome of infection at the individual level determines if, when and how much the individual host will further transmit the infectious agent into the environment, eventually affecting the spread of the infection in the host population. We expect the conceptual modelling framework developed here to be applicable to many infectious disease with free-living pathogens in the environment beyond the specific disease system of human

Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach.

Science.gov (United States)

Benguigui, Madeleine; Alishekevitz, Dror; Timaner, Michael; Shechter, Dvir; Raviv, Ziv; Benzekry, Sebastien; Shaked, Yuval

2018-01-05

It has recently been suggested that pro-tumorigenic host-mediated processes induced in response to chemotherapy counteract the anti-tumor activity of therapy, and thereby decrease net therapeutic outcome. Here we use experimental data to formulate a mathematical model describing the host response to different doses of paclitaxel (PTX) chemotherapy as well as the duration of the response. Three previously described host-mediated effects are used as readouts for the host response to therapy. These include the levels of circulating endothelial progenitor cells in peripheral blood and the effect of plasma derived from PTX-treated mice on migratory and invasive properties of tumor cells in vitro . A first set of mathematical models, based on basic principles of pharmacokinetics/pharmacodynamics, did not appropriately describe the dose-dependence and duration of the host response regarding the effects on invasion. We therefore provide an alternative mathematical model with a dose-dependent threshold, instead of a concentration-dependent one, that describes better the data. This model is integrated into a global model defining all three host-mediated effects. It not only precisely describes the data, but also correctly predicts host-mediated effects at different doses as well as the duration of the host response. This mathematical model may serve as a tool to predict the host response to chemotherapy in cancer patients, and therefore may be used to design chemotherapy regimens with improved therapeutic outcome by minimizing host mediated effects.

The Blind Identification of Multi-Inputs and Multi-Outputs Shallow-Water Acoustic Channel

International Nuclear Information System (INIS)

Li, R Y; Zhou, J H; Wang, L

2006-01-01

Blind channel identification/estimation is very important for object detection, trace, localization in the ocean acoustics. Time domain blind identification algorithm requiring exact length of the channel being identification. Due to the characteristics of the shallow-water channel, the length of channel impulse response sequence is uncertain, Hence a frequency domain method for the blind MIMO (Multiple-Input Multiple-Output) underwater identification based on higher order statistics (HOS) is used to estimate the original acoustic channel from received signals on hydrophones only, with the low signal to noise ratio (SNR). The simulation results in the acoustic environment proved this work is effective and efficient for blind identification of the shallow-water acoustic channel

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Science.gov (United States)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-06-01

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.

EmpiriciSN: Re-sampling Observed Supernova/Host Galaxy Populations Using an XD Gaussian Mixture Model

Energy Technology Data Exchange (ETDEWEB)

Holoien, Thomas W.-S.; /Ohio State U., Dept. Astron. /Ohio State U., CCAPP /KIPAC, Menlo Park /SLAC; Marshall, Philip J.; Wechsler, Risa H.; /KIPAC, Menlo Park /SLAC

2017-05-11

We describe two new open-source tools written in Python for performing extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned model to re-sample observed supernova and host galaxy populations. XDGMM is new program that uses Gaussian mixtures to perform density estimation of noisy data using extreme deconvolution (XD) algorithms. Additionally, it has functionality not available in other XD tools. It allows the user to select between the AstroML and Bovy et al. fitting methods and is compatible with scikit-learn machine learning algorithms. Most crucially, it allows the user to condition a model based on the known values of a subset of parameters. This gives the user the ability to produce a tool that can predict unknown parameters based on a model that is conditioned on known values of other parameters. EmpiriciSN is an exemplary application of this functionality, which can be used to fit an XDGMM model to observed supernova/host data sets and predict likely supernova parameters using a model conditioned on observed host properties. It is primarily intended to simulate realistic supernovae for LSST data simulations based on empirical galaxy properties.

Multi-level decision making models, methods and applications

CERN Document Server

Zhang, Guangquan; Gao, Ya

2015-01-01

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.  

The application of a multi-dimensional assessment approach to talent identification in Australian football.

Science.gov (United States)

Woods, Carl T; Raynor, Annette J; Bruce, Lyndell; McDonald, Zane; Robertson, Sam

2016-07-01

This study investigated whether a multi-dimensional assessment could assist with talent identification in junior Australian football (AF). Participants were recruited from an elite under 18 (U18) AF competition and classified into two groups; talent identified (State U18 Academy representatives; n = 42; 17.6 ± 0.4 y) and non-talent identified (non-State U18 Academy representatives; n = 42; 17.4 ± 0.5 y). Both groups completed a multi-dimensional assessment, which consisted of physical (standing height, dynamic vertical jump height and 20 m multistage fitness test), technical (kicking and handballing tests) and perceptual-cognitive (video decision-making task) performance outcome tests. A multivariate analysis of variance tested the main effect of status on the test criterions, whilst a receiver operating characteristic curve assessed the discrimination provided from the full assessment. The talent identified players outperformed their non-talent identified peers in each test (P talent identified and non-talent identified participants, respectively. When compared to single assessment approaches, this multi-dimensional assessment reflects a more comprehensive means of talent identification in AF. This study further highlights the importance of assessing multi-dimensional performance qualities when identifying talented team sports.

Multi-Valued Modal Fixed Point Logics for Model Checking

Science.gov (United States)

Nishizawa, Koki

In this paper, I will show how multi-valued logics are used for model checking. Model checking is an automatic technique to analyze correctness of hardware and software systems. A model checker is based on a temporal logic or a modal fixed point logic. That is to say, a system to be checked is formalized as a Kripke model, a property to be satisfied by the system is formalized as a temporal formula or a modal formula, and the model checker checks that the Kripke model satisfies the formula. Although most existing model checkers are based on 2-valued logics, recently new attempts have been made to extend the underlying logics of model checkers to multi-valued logics. I will summarize these new results.

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

Science.gov (United States)

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Using wavelet multi-resolution nature to accelerate the identification of fractional order system

International Nuclear Information System (INIS)

Li Yuan-Lu; Meng Xiao; Ding Ya-Qing

2017-01-01

Because of the fractional order derivatives, the identification of the fractional order system (FOS) is more complex than that of an integral order system (IOS). In order to avoid high time consumption in the system identification, the least-squares method is used to find other parameters by fixing the fractional derivative order. Hereafter, the optimal parameters of a system will be found by varying the derivative order in an interval. In addition, the operational matrix of the fractional order integration combined with the multi-resolution nature of a wavelet is used to accelerate the FOS identification, which is achieved by discarding wavelet coefficients of high-frequency components of input and output signals. In the end, the identifications of some known fractional order systems and an elastic torsion system are used to verify the proposed method. (paper)

Feature-based Alignment of Volumetric Multi-modal Images

Science.gov (United States)

Toews, Matthew; Zöllei, Lilla; Wells, William M.

2014-01-01

This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology. PMID:24683955

Global sensitivity analysis of the joint kinematics during gait to the parameters of a lower limb multi-body model.

Science.gov (United States)

El Habachi, Aimad; Moissenet, Florent; Duprey, Sonia; Cheze, Laurence; Dumas, Raphaël

2015-07-01

Sensitivity analysis is a typical part of biomechanical models evaluation. For lower limb multi-body models, sensitivity analyses have been mainly performed on musculoskeletal parameters, more rarely on the parameters of the joint models. This study deals with a global sensitivity analysis achieved on a lower limb multi-body model that introduces anatomical constraints at the ankle, tibiofemoral, and patellofemoral joints. The aim of the study was to take into account the uncertainty of parameters (e.g. 2.5 cm on the positions of the skin markers embedded in the segments, 5° on the orientation of hinge axis, 2.5 mm on the origin and insertion of ligaments) using statistical distributions and propagate it through a multi-body optimisation method used for the computation of joint kinematics from skin markers during gait. This will allow us to identify the most influential parameters on the minimum of the objective function of the multi-body optimisation (i.e. the sum of the squared distances between measured and model-determined skin marker positions) and on the joint angles and displacements. To quantify this influence, a Fourier-based algorithm of global sensitivity analysis coupled with a Latin hypercube sampling is used. This sensitivity analysis shows that some parameters of the motor constraints, that is to say the distances between measured and model-determined skin marker positions, and the kinematic constraints are highly influencing the joint kinematics obtained from the lower limb multi-body model, for example, positions of the skin markers embedded in the shank and pelvis, parameters of the patellofemoral hinge axis, and parameters of the ankle and tibiofemoral ligaments. The resulting standard deviations on the joint angles and displacements reach 36° and 12 mm. Therefore, personalisation, customisation or identification of these most sensitive parameters of the lower limb multi-body models may be considered as essential.

Hybrid Multi-Agent Control in Microgrids: Framework, Models and Implementations Based on IEC 61850

Directory of Open Access Journals (Sweden)

Xiaobo Dou

2014-12-01

Full Text Available Operation control is a vital and complex issue for microgrids. The objective of this paper is to explore the practical means of applying decentralized control by using a multi agent system in actual microgrids and devices. This paper presents a hierarchical control framework (HCF consisting of local reaction control (LRC level, local decision control (LDC level, horizontal cooperation control (HCC level and vertical cooperation control (VCC level to meet different control requirements of a microgrid. Then, a hybrid multi-agent control model (HAM is proposed to implement HCF, and the properties, functionalities and operating rules of HAM are described. Furthermore, the paper elaborates on the implementation of HAM based on the IEC 61850 Standard, and proposes some new implementation methods, such as extended information models of IEC 61850 with agent communication language and bidirectional interaction mechanism of generic object oriented substation event (GOOSE communication. A hardware design and software system are proposed and the results of simulation and laboratory tests verify the effectiveness of the proposed strategies, models and implementations.

Multi-Decadal Coastal Behavioural States From A Fusion Of Geohistorical Conceptual Modelling With 2-D Morphodynamic Modelling

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Goodwin, I. D.; Mortlock, T.

2016-02-01

Geohistorical archives of shoreline and foredune planform geometry provides a unique evidence-based record of the time integral response to coupled directional wave climate and sediment supply variability on annual to multi-decadal time scales. We develop conceptual shoreline modelling from the geohistorical shoreline archive using a novel combination of methods, including: LIDAR DEM and field mapping of coastal geology; a decadal-scale climate reconstruction of sea-level pressure, marine windfields, and paleo-storm synoptic type and frequency, and historical bathymetry. The conceptual modelling allows for the discrimination of directional wave climate shifts and the relative contributions of cross-shore and along-shore sand supply rates at multi-decadal resolution. We present regional examples from south-eastern Australia over a large latitudinal gradient from subtropical Queensland (S 25°) to mid-latitude Bass Strait (S 40°) that illustrate the morphodynamic evolution and reorganization to wave climate change. We then use the conceptual modeling to inform a two-dimensional coupled spectral wave-hydrodynamic-morphodynamic model to investigate the shoreface response to paleo-directional wind and wave climates. Unlike one-line shoreline modelling, this fully dynamical approach allows for the investigation of cumulative and spatial bathymetric change due to wave-induced currents, as well as proxy-shoreline change. The fusion of the two modeling approaches allows for: (i) the identification of the natural range of coastal planform geometries in response to wave climate shifts; and, (ii) the decomposition of the multidecadal coastal change into the cross-shore and along-shore sand supply drivers, according to the best-matching planforms.

Cell-Free and Cell-Based Approaches to Explore the Roles of Host Membranes and Lipids in the Formation of Viral Replication Compartment Induced by Tombusviruses.

Science.gov (United States)

Nagy, Peter D; Pogany, Judit; Xu, Kai

2016-03-03

Plant positive strand RNA viruses are intracellular infectious agents that take advantage of cellular lipids and membranes to support replication and protect viral RNA from degradation by host antiviral responses. In this review, we discuss how Tomato bushy stunt virus (TBSV) co-opts lipid transfer proteins and modulates lipid metabolism and transport to facilitate the assembly of the membrane-bound viral replicase complexes within intricate replication compartments. Identification and characterization of the proviral roles of specific lipids and proteins involved in lipid metabolism based on results from yeast (Saccharomyces cerevisiae) model host and cell-free approaches are discussed. The review also highlights the advantage of using liposomes with chemically defined composition to identify specific lipids required for TBSV replication. Remarkably, all the known steps in TBSV replication are dependent on cellular lipids and co-opted membranes.

Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

International Nuclear Information System (INIS)

Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

2010-01-01

This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

Systematic identification of novel, essential host genes affecting bromovirus RNA replication.

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Brandi L Gancarz

Full Text Available Positive-strand RNA virus replication involves viral proteins and cellular proteins at nearly every replication step. Brome mosaic virus (BMV is a well-established model for dissecting virus-host interactions and is one of very few viruses whose RNA replication, gene expression and encapsidation have been reproduced in the yeast Saccharomyces cerevisiae. Previously, our laboratory identified ∼100 non-essential host genes whose loss inhibited or enhanced BMV replication at least 3-fold. However, our isolation of additional BMV-modulating host genes by classical genetics and other results underscore that genes essential for cell growth also contribute to BMV RNA replication at a frequency that may be greater than that of non-essential genes. To systematically identify novel, essential host genes affecting BMV RNA replication, we tested a collection of ∼900 yeast strains, each with a single essential gene promoter replaced by a doxycycline-repressible promoter, allowing repression of gene expression by adding doxycycline to the growth medium. Using this strain array of ∼81% of essential yeast genes, we identified 24 essential host genes whose depleted expression reproducibly inhibited or enhanced BMV RNA replication. Relevant host genes are involved in ribosome biosynthesis, cell cycle regulation and protein homeostasis, among other cellular processes. BMV 2a(Pol levels were significantly increased in strains depleted for a heat shock protein (HSF1 or proteasome components (PRE1 and RPT6, suggesting these genes may affect BMV RNA replication by directly or indirectly modulating 2a(Pol localization, post-translational modification or interacting partners. Investigating the diverse functions of these newly identified essential host genes should advance our understanding of BMV-host interactions and normal cellular pathways, and suggest new modes of virus control.

Two analytical models for evaluating performance of Gigabit Ethernet Hosts

International Nuclear Information System (INIS)

Salah, K.

2006-01-01

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts when subjected to Gigabit network traffic. Under heavy network traffic, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, excessive latency and significant degradation in system throughput can be experienced. Also user application may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based Markov processes and queuing theory, while the second, which is more accurate but more complex is a pure Markov process. For the most part both models give mathematically-equivalent closed-form solutions for a number of important system performance metrics. These metrics include throughput, latency and stability condition, CPU utilization of interrupt handling and protocol processing and CPU availability for user applications. The analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More, importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation. (author)

Algorithm development and verification of UASCM for multi-dimension and multi-group neutron kinetics model

International Nuclear Information System (INIS)

Si, S.

2012-01-01

The Universal Algorithm of Stiffness Confinement Method (UASCM) for neutron kinetics model of multi-dimensional and multi-group transport equations or diffusion equations has been developed. The numerical experiments based on transport theory code MGSNM and diffusion theory code MGNEM have demonstrated that the algorithm has sufficient accuracy and stability. (authors)

Dynamical response of multi-walled carbon nanotube resonators based on continuum mechanics modeling for mass sensing applications

Energy Technology Data Exchange (ETDEWEB)

Choi, Myungseok; Olshevskiy, Alexander; Kim, Chang-Wan [Konkuk University, Seoul (Korea, Republic of); Eom, Kilho [Sungkyunkwan University, Suwon (Korea, Republic of); Gwak, Kwanwoong [Sejong University, Seoul (Korea, Republic of); Dai, Mai Duc [Ho Chi Minh City University of Technology and Education, Ho Chi Minh (Viet Nam)

2017-05-15

Carbon nanotube (CNT) has recently received much attention due to its excellent electromechanical properties, indicating that CNT can be employed for development of Nanoelectromechanical system (NEMS) such as nanomechanical resonators. For effective design of CNT-based resonators, it is required to accurately predict the vibration behavior of CNT resonators as well as their frequency response to mass adsorption. In this work, we have studied the vibrational behavior of Multi-walled CNT (MWCNT) resonators by using a continuum mechanics modeling that was implemented in Finite element method (FEM). In particular, we consider a transversely isotropic hollow cylinder solid model with Finite element (FE) implementation for modeling the vibration behavior of Multi-walled CNT (MWCNT) resonators. It is shown that our continuum mechanics model provides the resonant frequencies of various MWCNTs being comparable to those obtained from experiments. Moreover, we have investigated the frequency response of MWCNT resonators to mass adsorption by using our continuum model with FE implementation. Our study sheds light on our continuum mechanics model that is useful in predicting not only the vibration behavior of MWCNT resonators but also their sensing performance for further effective design of MWCNT- based NEMS devices.

Jet identification based on probability calculations using Bayes' theorem

International Nuclear Information System (INIS)

Jacobsson, C.; Joensson, L.; Lindgren, G.; Nyberg-Werther, M.

1994-11-01

The problem of identifying jets at LEP and HERA has been studied. Identification using jet energies and fragmentation properties was treated separately in order to investigate the degree of quark-gluon separation that can be achieved by either of these approaches. In the case of the fragmentation-based identification, a neural network was used, and a test of the dependence on the jet production process and the fragmentation model was done. Instead of working with the separation variables directly, these have been used to calculate probabilities of having a specific type of jet, according to Bayes' theorem. This offers a direct interpretation of the performance of the jet identification and provides a simple means of combining the results of the energy- and fragmentation-based identifications. (orig.)

A Portable, Air-Jet-Actuator-Based Device for System Identification

Science.gov (United States)

Staats, Wayne; Belden, Jesse; Mazumdar, Anirban; Hunter, Ian

2010-11-01

System identification (ID) of human and robotic limbs could help in diagnosis of ailments and aid in optimization of control parameters and future redesigns. We present a self-contained actuator, which uses the Coanda effect to rapidly switch the direction of a high speed air jet to create a binary stochastic force input to a limb for system ID. The design of the actuator is approached with the goal of creating a portable device, which could deployed on robot or human limbs for in situ identification. The viability of the device is demonstrated by performing stochastic system ID on an underdamped elastic beam system with fixed inertia and stiffness, and variable damping. The non-parametric impulse response yielded from the stochastic system ID is modeled as a second order system, and the resultant parameters are found to be in excellent agreement with those found using more traditional system ID techniques. The current design could be further miniaturized and developed as a portable, wireless, on-site multi-axis system identification system for less intrusive and more widespread use.

Reservoir Identification: Parameter Characterization or Feature Classification

Science.gov (United States)

Cao, J.

2017-12-01

The ultimate goal of oil and gas exploration is to find the oil or gas reservoirs with industrial mining value. Therefore, the core task of modern oil and gas exploration is to identify oil or gas reservoirs on the seismic profiles. Traditionally, the reservoir is identify by seismic inversion of a series of physical parameters such as porosity, saturation, permeability, formation pressure, and so on. Due to the heterogeneity of the geological medium, the approximation of the inversion model and the incompleteness and noisy of the data, the inversion results are highly uncertain and must be calibrated or corrected with well data. In areas where there are few wells or no well, reservoir identification based on seismic inversion is high-risk. Reservoir identification is essentially a classification issue. In the identification process, the underground rocks are divided into reservoirs with industrial mining value and host rocks with non-industrial mining value. In addition to the traditional physical parameters classification, the classification may be achieved using one or a few comprehensive features. By introducing the concept of seismic-print, we have developed a new reservoir identification method based on seismic-print analysis. Furthermore, we explore the possibility to use deep leaning to discover the seismic-print characteristics of oil and gas reservoirs. Preliminary experiments have shown that the deep learning of seismic data could distinguish gas reservoirs from host rocks. The combination of both seismic-print analysis and seismic deep learning is expected to be a more robust reservoir identification method. The work was supported by NSFC under grant No. 41430323 and No. U1562219, and the National Key Research and Development Program under Grant No. 2016YFC0601

Stepwise observation and quantification and mixed matrix membrane separation of CO2 within a hydroxy-decorated porous host.

Science.gov (United States)

Morris, Christopher G; Jacques, Nicholas M; Godfrey, Harry G W; Mitra, Tamoghna; Fritsch, Detlev; Lu, Zhenzhong; Murray, Claire A; Potter, Jonathan; Cobb, Tom M; Yuan, Fajin; Tang, Chiu C; Yang, Sihai; Schröder, Martin

2017-04-01

The identification of preferred binding domains within a host structure provides important insights into the function of materials. State-of-the-art reports mostly focus on crystallographic studies of empty and single component guest-loaded host structures to determine the location of guests. However, measurements of material properties ( e.g. , adsorption and breakthrough of substrates) are usually performed for a wide range of pressure (guest coverage) and/or using multi-component gas mixtures. Here we report the development of a multifunctional gas dosing system for use in X-ray powder diffraction studies on Beamline I11 at Diamond Light Source. This facility is fully automated and enables in situ crystallographic studies of host structures under (i) unlimited target gas loadings and (ii) loading of multi-component gas mixtures. A proof-of-concept study was conducted on a hydroxyl-decorated porous material MFM-300(V III ) under (i) five different CO 2 pressures covering the isotherm range and (ii) the loading of equimolar mixtures of CO 2 /N 2 . The study has successfully captured the structural dynamics underpinning CO 2 uptake as a function of surface coverage. Moreover, MFM-300(V III ) was incorporated in a mixed matrix membrane (MMM) with PIM-1 in order to evaluate the CO 2 /N 2 separation potential of this material. Gas permeation measurements on the MMM show a great improvement over the bare PIM-1 polymer for CO 2 /N 2 separation based on the ideal selectivity.

Parameter identification in a generalized time-harmonic Rayleigh damping model for elastography.

Directory of Open Access Journals (Sweden)

Elijah E W Van Houten

Full Text Available The identifiability of the two damping components of a Generalized Rayleigh Damping model is investigated through analysis of the continuum equilibrium equations as well as a simple spring-mass system. Generalized Rayleigh Damping provides a more diversified attenuation model than pure Viscoelasticity, with two parameters to describe attenuation effects and account for the complex damping behavior found in biological tissue. For heterogeneous Rayleigh Damped materials, there is no equivalent Viscoelastic system to describe the observed motions. For homogeneous systems, the inverse problem to determine the two Rayleigh Damping components is seen to be uniquely posed, in the sense that the inverse matrix for parameter identification is full rank, with certain conditions: when either multi-frequency data is available or when both shear and dilatational wave propagation is taken into account. For the multi-frequency case, the frequency dependency of the elastic parameters adds a level of complexity to the reconstruction problem that must be addressed for reasonable solutions. For the dilatational wave case, the accuracy of compressional wave measurement in fluid saturated soft tissues becomes an issue for qualitative parameter identification. These issues can be addressed with reasonable assumptions on the negligible damping levels of dilatational waves in soft tissue. In general, the parameters of a Generalized Rayleigh Damping model are identifiable for the elastography inverse problem, although with more complex conditions than the simpler Viscoelastic damping model. The value of this approach is the additional structural information provided by the Generalized Rayleigh Damping model, which can be linked to tissue composition as well as rheological interpretations.

Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm

Science.gov (United States)

Song, Ke; Li, Feiqiang; Hu, Xiao; He, Lin; Niu, Wenxu; Lu, Sihao; Zhang, Tong

2018-06-01

The development of fuel cell electric vehicles can to a certain extent alleviate worldwide energy and environmental issues. While a single energy management strategy cannot meet the complex road conditions of an actual vehicle, this article proposes a multi-mode energy management strategy for electric vehicles with a fuel cell range extender based on driving condition recognition technology, which contains a patterns recognizer and a multi-mode energy management controller. This paper introduces a learning vector quantization (LVQ) neural network to design the driving patterns recognizer according to a vehicle's driving information. This multi-mode strategy can automatically switch to the genetic algorithm optimized thermostat strategy under specific driving conditions in the light of the differences in condition recognition results. Simulation experiments were carried out based on the model's validity verification using a dynamometer test bench. Simulation results show that the proposed strategy can obtain better economic performance than the single-mode thermostat strategy under dynamic driving conditions.

Creating a Test Validated Structural Dynamic Finite Element Model of the Multi-Utility Technology Test Bed Aircraft

Science.gov (United States)

Pak, Chan-Gi; Truong, Samson S.

2014-01-01

Small modeling errors in the finite element model will eventually induce errors in the structural flexibility and mass, thus propagating into unpredictable errors in the unsteady aerodynamics and the control law design. One of the primary objectives of Multi Utility Technology Test Bed, X-56A, aircraft is the flight demonstration of active flutter suppression, and therefore in this study, the identification of the primary and secondary modes for the structural model tuning based on the flutter analysis of X-56A. The ground vibration test validated structural dynamic finite element model of the X-56A is created in this study. The structural dynamic finite element model of the X-56A is improved using a model tuning tool. In this study, two different weight configurations of the X-56A have been improved in a single optimization run.

MULTI: a shared memory approach to cooperative molecular modeling.

Science.gov (United States)

Darden, T; Johnson, P; Smith, H

1991-03-01

A general purpose molecular modeling system, MULTI, based on the UNIX shared memory and semaphore facilities for interprocess communication is described. In addition to the normal querying or monitoring of geometric data, MULTI also provides processes for manipulating conformations, and for displaying peptide or nucleic acid ribbons, Connolly surfaces, close nonbonded contacts, crystal-symmetry related images, least-squares superpositions, and so forth. This paper outlines the basic techniques used in MULTI to ensure cooperation among these specialized processes, and then describes how they can work together to provide a flexible modeling environment.

Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

Science.gov (United States)

Sesar, Branimir; Hernitschek, Nina; Mitrović, Sandra; Ivezić, Željko; Rix, Hans-Walter; Cohen, Judith G.; Bernard, Edouard J.; Grebel, Eva K.; Martin, Nicolas F.; Schlafly, Edward F.; Burgett, William S.; Draper, Peter W.; Flewelling, Heather; Kaiser, Nick; Kudritzki, Rolf P.; Magnier, Eugene A.; Metcalfe, Nigel; Tonry, John L.; Waters, Christopher

2017-05-01

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) 3π survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PS1's sparse, asynchronous multi-band light curves (≲ 12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in > 80 % of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machine-learned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of ˜45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period-luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.

Multi-day activity scheduling reactions to planned activities and future events in a dynamic agent-based model of activity-travel behavior

NARCIS (Netherlands)

Nijland, E.W.L.; Arentze, T.A.; Timmermans, H.J.P.

2009-01-01

Modeling multi-day planning has received scarce attention today in activity-based transport demand modeling. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that

Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

International Nuclear Information System (INIS)

Yong, Li; Ying-Gan, Tang

2010-01-01

A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Directory of Open Access Journals (Sweden)

Kaveh Khalili-Damghani

2017-07-01

Full Text Available In this paper a multi-period multi-product multi-objective aggregate production planning (APP model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

Immunity-based detection, identification, and evaluation of aircraft sub-system failures

Science.gov (United States)

Moncayo, Hever Y.

This thesis describes the design, development, and flight-simulation testing of an integrated Artificial Immune System (AIS) for detection, identification, and evaluation of a wide variety of sensor, actuator, propulsion, and structural failures/damages including the prediction of the achievable states and other limitations on performance and handling qualities. The AIS scheme achieves high detection rate and low number of false alarms for all the failure categories considered. Data collected using a motion-based flight simulator are used to define the self for an extended sub-region of the flight envelope. The NASA IFCS F-15 research aircraft model is used and represents a supersonic fighter which include model following adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The flight simulation tests are designed to analyze and demonstrate the performance of the immunity-based aircraft failure detection, identification and evaluation (FDIE) scheme. A general robustness analysis is also presented by determining the achievable limits for a desired performance in the presence of atmospheric perturbations. For the purpose of this work, the integrated AIS scheme is implemented based on three main components. The first component performs the detection when one of the considered failures is present in the system. The second component consists in the identification of the failure category and the classification according to the failed element. During the third phase a general evaluation of the failure is performed with the estimation of the magnitude/severity of the failure and the prediction of its effect on reducing the flight envelope of the aircraft system. Solutions and alternatives to specific design issues of the AIS scheme, such as data clustering and empty space optimization, data fusion and duplication removal, definition of features, dimensionality reduction, and selection of cluster/detector shape are also

Morphing the feature-based multi-blocks of normative/healthy vertebral geometries to scoliosis vertebral geometries: development of personalized finite element models.

Science.gov (United States)

Hadagali, Prasannaah; Peters, James R; Balasubramanian, Sriram

2018-03-12

Personalized Finite Element (FE) models and hexahedral elements are preferred for biomechanical investigations. Feature-based multi-block methods are used to develop anatomically accurate personalized FE models with hexahedral mesh. It is tedious to manually construct multi-blocks for large number of geometries on an individual basis to develop personalized FE models. Mesh-morphing method mitigates the aforementioned tediousness in meshing personalized geometries every time, but leads to element warping and loss of geometrical data. Such issues increase in magnitude when normative spine FE model is morphed to scoliosis-affected spinal geometry. The only way to bypass the issue of hex-mesh distortion or loss of geometry as a result of morphing is to rely on manually constructing the multi-blocks for scoliosis-affected spine geometry of each individual, which is time intensive. A method to semi-automate the construction of multi-blocks on the geometry of scoliosis vertebrae from the existing multi-blocks of normative vertebrae is demonstrated in this paper. High-quality hexahedral elements were generated on the scoliosis vertebrae from the morphed multi-blocks of normative vertebrae. Time taken was 3 months to construct the multi-blocks for normative spine and less than a day for scoliosis. Efforts taken to construct multi-blocks on personalized scoliosis spinal geometries are significantly reduced by morphing existing multi-blocks.

Fish, fans and hydroids: host species of pygmy seahorses.

Science.gov (United States)

Reijnen, Bastian T; van der Meij, Sancia E T; van Ofwegen, Leen P

2011-01-01

An overview of the octocoral and hydrozoan host species of pygmy seahorses is provided based on literature records and recently collected field data for Hippocampus bargibanti, Hippocampus denise and Hippocampus pontohi. Seven new associations are recognized and an overview of the so far documented host species is given. A detailed re-examination of octocoral type material and a review of the taxonomic history of the alcyonacean genera Annella (Subergorgiidae) and Muricella (Acanthogorgiidae) are included as baseline for future revisions. The host specificity and colour morphs of pygmy seahorses are discussed, as well as the reliability of (previous) identifications and conservation issues.

Text-based language identification of multilingual names

CSIR Research Space (South Africa)

Giwa, O

2015-11-01

Full Text Available Text-based language identification (T-LID) of isolated words has been shown to be useful for various speech processing tasks, including pronunciation modelling and data categorisation. When the words to be categorised are proper names, the task...

A model of multi-purpose shopping trip behavior

NARCIS (Netherlands)

Arentze, T.A.; Borgers, A.W.J.; Timmermans, H.J.P.

1993-01-01

Existing utility-based models of complex choice behavior do not adequately deal with the interdependencies of chained choices. In this paper, we introduce a model of multi-purpose shopping which is aimed at overcoming this shortcoming. In the proposed model, dependencies between choices within as

Robust uncertainty evaluation for system identification on distributed wireless platforms

Science.gov (United States)

Crinière, Antoine; Döhler, Michael; Le Cam, Vincent; Mevel, Laurent

2016-04-01

Health monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on

Multi-scale diffuse interface modeling of multi-component two-phase flow with partial miscibility

Science.gov (United States)

Kou, Jisheng; Sun, Shuyu

2016-08-01

In this paper, we introduce a diffuse interface model to simulate multi-component two-phase flow with partial miscibility based on a realistic equation of state (e.g. Peng-Robinson equation of state). Because of partial miscibility, thermodynamic relations are used to model not only interfacial properties but also bulk properties, including density, composition, pressure, and realistic viscosity. As far as we know, this effort is the first time to use diffuse interface modeling based on equation of state for modeling of multi-component two-phase flow with partial miscibility. In numerical simulation, the key issue is to resolve the high contrast of scales from the microscopic interface composition to macroscale bulk fluid motion since the interface has a nanoscale thickness only. To efficiently solve this challenging problem, we develop a multi-scale simulation method. At the microscopic scale, we deduce a reduced interfacial equation under reasonable assumptions, and then we propose a formulation of capillary pressure, which is consistent with macroscale flow equations. Moreover, we show that Young-Laplace equation is an approximation of this capillarity formulation, and this formulation is also consistent with the concept of Tolman length, which is a correction of Young-Laplace equation. At the macroscopical scale, the interfaces are treated as discontinuous surfaces separating two phases of fluids. Our approach differs from conventional sharp-interface two-phase flow model in that we use the capillary pressure directly instead of a combination of surface tension and Young-Laplace equation because capillarity can be calculated from our proposed capillarity formulation. A compatible condition is also derived for the pressure in flow equations. Furthermore, based on the proposed capillarity formulation, we design an efficient numerical method for directly computing the capillary pressure between two fluids composed of multiple components. Finally, numerical tests

Multi-scale diffuse interface modeling of multi-component two-phase flow with partial miscibility

KAUST Repository

Kou, Jisheng

2016-05-10

In this paper, we introduce a diffuse interface model to simulate multi-component two-phase flow with partial miscibility based on a realistic equation of state (e.g. Peng-Robinson equation of state). Because of partial miscibility, thermodynamic relations are used to model not only interfacial properties but also bulk properties, including density, composition, pressure, and realistic viscosity. As far as we know, this effort is the first time to use diffuse interface modeling based on equation of state for modeling of multi-component two-phase flow with partial miscibility. In numerical simulation, the key issue is to resolve the high contrast of scales from the microscopic interface composition to macroscale bulk fluid motion since the interface has a nanoscale thickness only. To efficiently solve this challenging problem, we develop a multi-scale simulation method. At the microscopic scale, we deduce a reduced interfacial equation under reasonable assumptions, and then we propose a formulation of capillary pressure, which is consistent with macroscale flow equations. Moreover, we show that Young-Laplace equation is an approximation of this capillarity formulation, and this formulation is also consistent with the concept of Tolman length, which is a correction of Young-Laplace equation. At the macroscopical scale, the interfaces are treated as discontinuous surfaces separating two phases of fluids. Our approach differs from conventional sharp-interface two-phase flow model in that we use the capillary pressure directly instead of a combination of surface tension and Young-Laplace equation because capillarity can be calculated from our proposed capillarity formulation. A compatible condition is also derived for the pressure in flow equations. Furthermore, based on the proposed capillarity formulation, we design an efficient numerical method for directly computing the capillary pressure between two fluids composed of multiple components. Finally, numerical tests

Identification techniques for phenomenological models of hysteresis based on the conjugate gradient method

International Nuclear Information System (INIS)

Andrei, Petru; Oniciuc, Liviu; Stancu, Alexandru; Stoleriu, Laurentiu

2007-01-01

An identification technique for the parameters of phenomenological models of hysteresis is presented. The basic idea of our technique is to set up a system of equations for the parameters of the model as a function of known quantities on the major or minor hysteresis loops (e.g. coercive force, susceptibilities at various points, remanence), or other magnetization curves. This system of equations can be either over or underspecified and is solved by using the conjugate gradient method. Numerical results related to the identification of parameters in the Energetic, Jiles-Atherton, and Preisach models are presented

Multi-scale modeling of the thermo-mechanical behavior of particle-based composites

International Nuclear Information System (INIS)

Di Paola, F.

2010-01-01

The aim of this work was to perform numerical simulations of the thermal and mechanical behavior of a particle-based nuclear fuel. This is a refractory composite material made of UO 2 spherical particles which are coated with two layers of pyrocarbon and embedded in a graphite matrix at a high volume fraction (45%). The objective was to develop a multi-scale modeling of this composite material which can estimate its mean behavior as well as the heterogeneity of the local mechanical variables. The first part of this work was dedicated to the modeling of the microstructure in 3D. To do this, we developed tools to generate random distributions of spheres, meshes and to characterize the morphology of the microstructure towards the finite element code Cast3M. A hundred of numerical samples of the composite were created. The second part was devoted to the characterization of the thermo-elastic behavior by the finite element modeling of the samples. We studied the influence of different modeling parameters, one of them is the boundary conditions. We proposed a method to vanish the boundary conditions effects from the computed solution by analyzing it on an internal sub-volume of the sample obtained by erosion. Then, we determined the effective properties (elastic moduli, thermal conductivity and thermal expansion) and the stress distribution within the matrix. Finally, in the third part we proposed a multi-scale modeling to determine the mean values and the variance and covariance of the local mechanical variables for any macroscopic load. This statistical approach have been used to estimate the intra-phase distribution of these variables in the composite material. (author) [fr

Multi-scale modeling of the thermo-mechanical behavior of particle-based composites

International Nuclear Information System (INIS)

Di Paola, F.

2010-11-01

The aim of this work was to perform numerical simulations of the thermal and mechanical behavior of a particle-based nuclear fuel. This is a refractory composite material made of UO 2 spherical particles which are coated with two layers of pyrocarbon and embedded in a graphite matrix at a high volume fraction (45 %). The objective was to develop a multi-scale modeling of this composite material which can estimate its mean behavior as well as the heterogeneity of the local mechanical variables. The first part of this work was dedicated to the modeling of the microstructure in 3D. To do this, we developed tools to generate random distributions of spheres, meshes and to characterize the morphology of the microstructure towards the finite element code Cast3M. A hundred of numerical samples of the composite were created. The second part was devoted to the characterization of the thermo-elastic behavior by the finite element modeling of the samples. We studied the influence of different modeling parameters, one of them is the boundary conditions. We proposed a method to vanish the boundary conditions effects from the computed solution by analyzing it on an internal sub-volume of the sample obtained by erosion. Then, we determined the effective properties (elastic moduli, thermal conductivity and thermal expansion) and the stress distribution within the matrix. Finally, in the third part we proposed a multi-scale modeling to determine the mean values and the variance and covariance of the local mechanical variables for any macroscopic load. This statistical approach have been used to estimate the intra-phase distribution of these variables in the composite material. (author)

A Support Vector Machine-Based Gender Identification Using Speech Signal

Science.gov (United States)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

Science.gov (United States)

Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

2015-11-01

Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of interactions between viral and host proteins using supervised machine learning methods.