Applying Model Based Systems Engineering to NASA's Space Communications Networks
Bhasin, Kul; Barnes, Patrick; Reinert, Jessica; Golden, Bert
2013-01-01
System engineering practices for complex systems and networks now require that requirement, architecture, and concept of operations product development teams, simultaneously harmonize their activities to provide timely, useful and cost-effective products. When dealing with complex systems of systems, traditional systems engineering methodology quickly falls short of achieving project objectives. This approach is encumbered by the use of a number of disparate hardware and software tools, spreadsheets and documents to grasp the concept of the network design and operation. In case of NASA's space communication networks, since the networks are geographically distributed, and so are its subject matter experts, the team is challenged to create a common language and tools to produce its products. Using Model Based Systems Engineering methods and tools allows for a unified representation of the system in a model that enables a highly related level of detail. To date, Program System Engineering (PSE) team has been able to model each network from their top-level operational activities and system functions down to the atomic level through relational modeling decomposition. These models allow for a better understanding of the relationships between NASA's stakeholders, internal organizations, and impacts to all related entities due to integration and sustainment of existing systems. Understanding the existing systems is essential to accurate and detailed study of integration options being considered. In this paper, we identify the challenges the PSE team faced in its quest to unify complex legacy space communications networks and their operational processes. We describe the initial approaches undertaken and the evolution toward model based system engineering applied to produce Space Communication and Navigation (SCaN) PSE products. We will demonstrate the practice of Model Based System Engineering applied to integrating space communication networks and the summary of its
Optical Neural Network Models Applied To Logic Program Execution
Stormon, Charles D.
1988-05-01
Logic programming is being used extensively by Artificial Intelligence researchers to solve problems including natural language processing and expert systems. These languages, of which Prolog is the most widely used, promise to revolutionize software engineering, but much greater performance is needed. Researchers have demonstrated the applicability of neural network models to the solution of certain NP-complete problems, but these methods are not obviously applicable to the execution of logic programs. This paper outlines the use of neural networks in four aspects of the logic program execution cycle, and discusses results of a simulation of three of these. Four neural network functional units are described, called the substitution agent, the clause filter, the structure processor, and the heuristics generator, respectively. Simulation results suggest that the system described may provide several orders of magnitude improvement in execution speed for large logic programs. However, practical implementation of the proposed architecture will require the application of optical computing techniques due to the large number of neurons required, and the need for massive, adaptive connectivity.
Applying fuzzy analytic network process in quality function deployment model
Directory of Open Access Journals (Sweden)
Mohammad Ali Afsharkazemi
2012-08-01
Full Text Available In this paper, we propose an empirical study of QFD implementation when fuzzy numbers are used to handle the uncertainty associated with different components of the proposed model. We implement fuzzy analytical network to find the relative importance of various criteria and using fuzzy numbers we calculate the relative importance of these factors. The proposed model of this paper uses fuzzy matrix and house of quality to study the products development in QFD and also the second phase i.e. part deployment. In most researches, the primary objective is only on CRs to implement the quality function deployment and some other criteria such as production costs, manufacturing costs etc were disregarded. The results of using fuzzy analysis network process based on the QFD model in Daroupat packaging company to develop PVDC show that the most important indexes are being waterproof, resistant pill packages, and production cost. In addition, the PVDC coating is the most important index in terms of company experts’ point of view.
Shrirang Ambaji KULKARNI; Raghavendra G . RAO
2017-01-01
Routing data packets in a dynamic network is a difficult and important problem in computer networks. As the network is dynamic, it is subject to frequent topology changes and is subject to variable link costs due to congestion and bandwidth. Existing shortest path algorithms fail to converge to better solutions under dynamic network conditions. Reinforcement learning algorithms posses better adaptation techniques in dynamic environments. In this paper we apply model based Q-Routing technique ...
Directory of Open Access Journals (Sweden)
Shrirang Ambaji KULKARNI
2017-04-01
Full Text Available Routing data packets in a dynamic network is a difficult and important problem in computer networks. As the network is dynamic, it is subject to frequent topology changes and is subject to variable link costs due to congestion and bandwidth. Existing shortest path algorithms fail to converge to better solutions under dynamic network conditions. Reinforcement learning algorithms posses better adaptation techniques in dynamic environments. In this paper we apply model based Q-Routing technique for routing in dynamic network. To analyze the correctness of Q-Routing algorithms mathematically, we provide a proof and also implement a SPIN based verification model. We also perform simulation based analysis of Q-Routing for given metrics.
Leaders Are the Network: Applying the Kotter Model in Shaping Future Information Systems
2010-01-01
Comunications and Information Systems ,” New York, NY: Springer Publishing, 1997, 183-193. Findley, Mike and Luck, Gary, “Information Overload...1 LEADERS ARE THE NETWORK: APPLYING THE KOTTER MODEL IN SHAPING FUTURE INFORMATION SYSTEMS Submitted...Model in Shaping Future Information Systems 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e
Plant, N. G.; Thieler, E. R.; Gutierrez, B.; Lentz, E. E.; Zeigler, S. L.; Van Dongeren, A.; Fienen, M. N.
2016-12-01
We evaluate the strengths and weaknesses of Bayesian networks that have been used to address scientific and decision-support questions related to coastal geomorphology. We will provide an overview of coastal geomorphology research that has used Bayesian networks and describe what this approach can do and when it works (or fails to work). Over the past decade, Bayesian networks have been formulated to analyze the multi-variate structure and evolution of coastal morphology and associated human and ecological impacts. The approach relates observable system variables to each other by estimating discrete correlations. The resulting Bayesian-networks make predictions that propagate errors, conduct inference via Bayes rule, or both. In scientific applications, the model results are useful for hypothesis testing, using confidence estimates to gage the strength of tests while applications to coastal resource management are aimed at decision-support, where the probabilities of desired ecosystems outcomes are evaluated. The range of Bayesian-network applications to coastal morphology includes emulation of high-resolution wave transformation models to make oceanographic predictions, morphologic response to storms and/or sea-level rise, groundwater response to sea-level rise and morphologic variability, habitat suitability for endangered species, and assessment of monetary or human-life risk associated with storms. All of these examples are based on vast observational data sets, numerical model output, or both. We will discuss the progression of our experiments, which has included testing whether the Bayesian-network approach can be implemented and is appropriate for addressing basic and applied scientific problems and evaluating the hindcast and forecast skill of these implementations. We will present and discuss calibration/validation tests that are used to assess the robustness of Bayesian-network models and we will compare these results to tests of other models. This will
Energy Technology Data Exchange (ETDEWEB)
Hartley, Lee; Roberts, David
2013-04-15
The Swedish Nuclear Fuel and Waste Management Company (SKB) is responsible for the development of a deep geological repository for spent nuclear fuel. The permitting of such a repository is informed by assessment studies to estimate the risks of the disposal method. One of the potential risks involves the transport of radionuclides in groundwater from defective canisters in the repository to the accessible environment. The Swedish programme for geological disposal of spent nuclear fuel has involved undertaking detailed surface-based site characterisation studies at two different sites, Forsmark and Laxemar-Simpevarp. A key component of the hydrogeological modelling of these two sites has been the development of Discrete Fracture Network (DFN) concepts of groundwater flow through the fractures in the crystalline rocks present. A discrete fracture network model represents some of the characteristics of fractures explicitly, such as their, orientation, intensity, size, spatial distribution, shape and transmissivity. This report summarises how the discrete fracture network methodology has been applied to model groundwater flow and transport at Forsmark and Laxemar. The account has involved summarising reports previously published by SKB between 2001 and 2011. The report describes the conceptual framework and assumptions used in interpreting site data, and in particular how data has been used to calibrate the various parameters that define the discrete fracture network representation of bedrock hydrogeology against borehole geologic and hydraulic data. Steps taken to confirm whether the developed discrete fracture network models provide a description of regional-scale groundwater flow and solute transport consistent with wider hydraulic tests hydrochemical data from Forsmark and Laxemar are discussed. It illustrates the use of derived hydrogeological DFN models in the simulations of the temperate period hydrogeology that provided input to radionuclide transport
Curteanu, Silvia; Suditu, Gabriel Dan; Buburuzan, Adela Marina; Dragoi, Elena Niculina
2014-11-01
The depollution of some gaseous streams containing n-hexane is studied by adsorption in a fixed bed column, under dynamic conditions, using granular activated carbon and two types of non-functionalized hypercross-linked polymeric resins. In order to model the process, a new neuro-evolutionary approach is proposed. It is a combination of a modified differential evolution (DE) with neural networks (NNs) and two local search algorithms, the global and local optimizers, working together to determine the optimal NN model. The main elements that characterize the applied variant of DE consist in using an opposition-based learning initialization, a simple self-adaptive procedure for the control parameters, and a modified mutation principle based on the fitness function as a criterion for reorganization. The results obtained prove that the proposed algorithm is able to determine a good model of the considered process, its performance being better than those of an available phenomenological model.
Neural networks-based modeling applied to a process of heavy metals removal from wastewaters.
Suditu, Gabriel D; Curteanu, Silvia; Bulgariu, Laura
2013-01-01
This article approaches the problem of environment pollution with heavy metals from disposal of industrial wastewaters, namely removal of these metals by means of biosorbents, particularly with Romanian peat (from Poiana Stampei). The study is carried out by simulation using feed-forward and modular neural networks with one or two hidden layers, pursuing the influence of certain operating parameters (metal nature, sorbent dose, pH, temperature, initial concentration of metal ion, contact time) on the amount of metal ions retained on the unit mass of sorbent. In neural network modeling, a consistent data set was used, including five metals: lead, mercury, cadmium, nickel and cobalt, the quantification of the metal nature being done by its electronegativity. Even if based on successive trials, the method of designing neural models was systematically conducted, recording and comparing the errors obtained with different types of neural networks, having various numbers of hidden layers and neurons, number of training epochs, or using various learning methods. The errors with values under 5% make clear the efficiency of the applied method.
Díaz Córdova, Diego
2016-01-01
The aim of this article is to introduce two methodological strategies that have not often been utilized in the anthropology of food: agent-based models and social networks analysis. In order to illustrate these methods in action, two cases based in materials typical of the anthropology of food are presented. For the first strategy, fieldwork carried out in Quebrada de Humahuaca (province of Jujuy, Argentina) regarding meal recall was used, and for the second, elements of the concept of "domestic consumption strategies" applied by Aguirre were employed. The underlying idea is that, given that eating is recognized as a "total social fact" and, therefore, as a complex phenomenon, the methodological approach must also be characterized by complexity. The greater the number of methods utilized (with the appropriate rigor), the better able we will be to understand the dynamics of feeding in the social environment.
Ciddio, Manuela; Mari, Lorenzo; Sokolow, Susanne H.; De Leo, Giulio A.; Casagrandi, Renato; Gatto, Marino
2017-10-01
Schistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations.
Applied impulsive mathematical models
Stamova, Ivanka
2016-01-01
Using the theory of impulsive differential equations, this book focuses on mathematical models which reflect current research in biology, population dynamics, neural networks and economics. The authors provide the basic background from the fundamental theory and give a systematic exposition of recent results related to the qualitative analysis of impulsive mathematical models. Consisting of six chapters, the book presents many applicable techniques, making them available in a single source easily accessible to researchers interested in mathematical models and their applications. Serving as a valuable reference, this text is addressed to a wide audience of professionals, including mathematicians, applied researchers and practitioners.
A hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus
Aquino, Ronald L.; Alcantara, Nialle Loui Mar T.; Addawe, Rizavel C.
2017-11-01
The Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.
Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee
2016-02-01
The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.
Directory of Open Access Journals (Sweden)
Saro Lee
2016-02-01
Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.
Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica
2016-01-01
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.
Directory of Open Access Journals (Sweden)
Dragan Pamučar
2016-01-01
Full Text Available The paper herein presents green p-median problem (GMP which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM, negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.
Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica
2016-01-01
The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. PMID:27195005
Applying Artificial Neural Networks for Face Recognition
Directory of Open Access Journals (Sweden)
Thai Hoang Le
2011-01-01
Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.
Applied data communications and networks
Buchanan, W
1996-01-01
The usage of data communications and computer networks are ever in creasing. It is one of the few technological areas which brings benefits to most of the countries and the peoples of the world. Without it many industries could not exist. It is the objective of this book to discuss data communications in a readable form that students and professionals all over the world can understand. As much as possible the text uses dia grams to illustrate key points. Most currently available data communications books take their view point from either a computer scientists top-down approach or from an electronic engineers bottom-up approach. This book takes a practical ap proach and supports it with a theoretical background to create a textbook which can be used by electronic engineers, computer engineers, computer scientists and industry professionals. It discusses most of the current and future key data communications technologies, including: • Data Communications Standards and Models; • Local Area Networks (...
Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá
2012-01-01
As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.
Directory of Open Access Journals (Sweden)
Jorge Sá Silva
2012-02-01
Full Text Available As the Internet evolved, social networks (such as Facebook have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype.
Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study.
Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; Impe, Jan Van
2017-06-01
In this work, both the structural and practical identifiability of the Anaerobic Digestion Model no. 1 (ADM1) is investigated, which serves as a relevant case study of large non-linear dynamic network models. The structural identifiability is investigated using the probabilistic algorithm, adapted to deal with the specifics of the case study (i.e., a large-scale non-linear dynamic system of differential and algebraic equations). The practical identifiability is analyzed using a Monte Carlo parameter estimation procedure for a 'non-informative' and 'informative' experiment, which are heuristically designed. The model structure of ADM1 has been modified by replacing parameters by parameter combinations, to provide a generally locally structurally identifiable version of ADM1. This means that in an idealized theoretical situation, the parameters can be estimated accurately. Furthermore, the generally positive structural identifiability results can be explained from the large number of interconnections between the states in the network structure. This interconnectivity, however, is also observed in the parameter estimates, making uncorrelated parameter estimations in practice difficult. Copyright © 2017. Published by Elsevier Inc.
Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.
2014-03-01
Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.
Chang, C L; Liu, H C
2015-09-01
The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water turbidity is high. Therefore, this study examines the reliability of the CTSI and uses the Back-Propagation Neural Network (BPNN) model to create a new trophic state index. Fuzzy theory, rather than binary logic, is implemented to classify the trophic status into its three grades. The results show that compared to the CTSI with traditional classification, the new index with fuzzy classification can improve trophic status evaluation with high water turbidity. A reliable trophic state index can correctly describe reservoir water quality and allow relevant agencies to address proper water quality management strategies for a reservoir system.
Corporate Social Networks Applied in the Classroom
Directory of Open Access Journals (Sweden)
Hugo de Juan-Jordán
2016-10-01
This study also tries to propose some guidelines and best practices obtained as a result of the experience of use and the adoption of social networks in class in order to improve the learning process and innovate in the methodology applied to education.
Benford's Law Applies to Online Social Networks.
Directory of Open Access Journals (Sweden)
Jennifer Golbeck
Full Text Available Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal, we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.
Applying Physical-Layer Network Coding in Wireless Networks
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Liew SoungChang
2010-01-01
Full Text Available A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11. This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC scheme to coordinate transmissions among nodes. In contrast to "straightforward" network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.
Collaborative networks: Reference modeling
Camarinha-Matos, L.M.; Afsarmanesh, H.
2008-01-01
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of
Novis, Shenia; Machado, Felipe; Costa, Victor B; Foguel, Debora; Cruz, Marcia W; de Seixas, José Manoel
2017-09-01
Hereditary (familial) amyloid polyneuropathy (FAP) is a systemic disease that includes a sensorimotor polyneuropathy related to transthyretin (TTR) mutations. So far, a scale designed to classify the severity of this disease has not yet been validated. This work proposes the implementation of an artificial neural network (ANN) in order to develop a severity scale for monitoring the disease progression in FAP patients. In order to achieve this goal, relevant symptoms and laboratory findings were collected from 98 Brazilian patients included in THAOS - the Transthyretin Amyloidosis Outcomes Survey. Ninety-three percent of them bore Val30Met, the most prevalent variant of TTR worldwide; 63 were symptomatic and 35 were asymptomatic. These data were numerically codified for the purpose of constructing a Self-Organizing Map (SOM), which maps data onto a grid of artificial neurons. Mapped data could be clustered by similarity into five groups, based on increasing FAP severity (from Groups 1 to 5). Most symptoms were virtually absent from patients who mapped to Group 1, which also includes the asymptomatic patients. Group 2 encompasses the patients bearing symptoms considered to be initial markers of FAP, such as first signs of walking disabilities and lack of sensitivity to temperature and pain. Interestingly, the patients with cardiac symptoms, which also carry cardiac-associated mutations of the TTR gene (such as Val112Ile and Ala19Asp), were concentrated in Group 3. Symptoms such as urinary and fecal incontinence and diarrhea characterized particularly Groups 4 and 5. Renal impairment was found almost exclusively in Group 5. Model validation was accomplished by considering the symptoms from a sample with 48 additional Brazilian patients. The severity scores proposed here not only identify the current stage of a patient's disease but also offer to the physician an easy-to-read, 2D map that makes it possible to track disease progression.
Morgan, Byron JT; Tanner, Martin Abba; Carlin, Bradley P
2008-01-01
Introduction and Examples Introduction Examples of data sets Basic Model Fitting Introduction Maximum-likelihood estimation for a geometric model Maximum-likelihood for the beta-geometric model Modelling polyspermy Which model? What is a model for? Mechanistic models Function Optimisation Introduction MATLAB: graphs and finite differences Deterministic search methods Stochastic search methods Accuracy and a hybrid approach Basic Likelihood ToolsIntroduction Estimating standard errors and correlations Looking at surfaces: profile log-likelihoods Confidence regions from profiles Hypothesis testing in model selectionScore and Wald tests Classical goodness of fit Model selection biasGeneral Principles Introduction Parameterisation Parameter redundancy Boundary estimates Regression and influence The EM algorithm Alternative methods of model fitting Non-regular problemsSimulation Techniques Introduction Simulating random variables Integral estimation Verification Monte Carlo inference Estimating sampling distributi...
Social network analysis applied to team sports analysis
Clemente, Filipe Manuel; Mendes, Rui Sousa
2016-01-01
Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks
DEFF Research Database (Denmark)
Fan, Shuangrui; JI, TINGYUN; Bergqvist, Rickard
2013-01-01
modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics......Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation...
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Modeling the citation network by network cosmology.
Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing
2015-01-01
Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
Modeling the citation network by network cosmology.
Directory of Open Access Journals (Sweden)
Zheng Xie
Full Text Available Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
Nguyen, Q.D.; Elwenspoek, Michael Curt
2006-01-01
Using the network etch rate function model, the anisotropic etch rate of p-type single crystal silicon was characterised in terms of microscopic properties including step velocity, step and terrace roughening. The anisotropic etch rate data needed have been obtained using a combination of 2 wagon
DEFF Research Database (Denmark)
Andersen, Kasper Winther
Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
Modeling network technology deployment rates with different network models
Chung, Yoo
2011-01-01
To understand the factors that encourage the deployment of a new networking technology, we must be able to model how such technology gets deployed. We investigate how network structure influences deployment with a simple deployment model and different network models through computer simulations. The results indicate that a realistic model of networking technology deployment should take network structure into account.
Applying weighted network measures to microarray distance matrices
Ahnert, S. E.; Garlaschelli, D.; Fink, T. M. A.; Caldarelli, G.
2008-06-01
In recent work we presented a new approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks. This approach is based on the translation of a weighted network into an ensemble of edges, and is particularly suited to the analysis of fully connected weighted networks. Here we apply our method to several such networks including distance matrices, and show that the clustering coefficient, constructed by using the ensemble approach, provides meaningful insights into the systems studied. In the particular case of two datasets from microarray experiments the clustering coefficient identifies a number of biologically significant genes, outperforming existing identification approaches.
Applying weighted network measures to microarray distance matrices
Energy Technology Data Exchange (ETDEWEB)
Ahnert, S E [Theory of Condensed Matter Group, Cavendish Laboratory, JJ Thomson Avenue, Cambridge CB3 0HE (United Kingdom); Garlaschelli, D [Dipartimento di Fisica, Universita di Siena, Via Roma 56, 53100 Siena (Italy); Fink, T M A [Institut Curie, CNRS UMR 144, 26 rue d' Ulm, 75248 Paris (France); Caldarelli, G [INFM-CNR Istituto dei Sistemi Complessi and Dipartimento di Fisica Universita di Roma ' La Sapienza' Piazzale Moro 2, 00185 Roma (Italy)
2008-06-06
In recent work we presented a new approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks. This approach is based on the translation of a weighted network into an ensemble of edges, and is particularly suited to the analysis of fully connected weighted networks. Here we apply our method to several such networks including distance matrices, and show that the clustering coefficient, constructed by using the ensemble approach, provides meaningful insights into the systems studied. In the particular case of two datasets from microarray experiments the clustering coefficient identifies a number of biologically significant genes, outperforming existing identification approaches.
Artificial Neural Network applied to lightning flashes
Gin, R. B.; Guedes, D.; Bianchi, R.
2013-05-01
The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a
Applied Hypergame Theory for Network Defense
2013-06-01
themselves. The investigation of the competition between species has given rise to evolutionary game theory. Even evolutionary game theory itself has evolved...to be applied to economics, sociology, and anthropology [7]. Application of game theory principles can be used wherever there is a contest over...Murad Mehmet Bayraktar. “Game Theory and Intrusion Detection Systems”, 2006. ISA 767-Secure E-Commerce. [7] Alexander, J. McKenzie. “ Evolutionary
Modeling Epidemic Network Failures
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...
Data modeling of network dynamics
Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad
2004-01-01
This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.
Floares, Alexandru George
2008-01-01
Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.
Applied Ontology Engineering in Cloud Services, Networks and Management Systems
Serrano Orozco, J Martín
2012-01-01
Metadata standards in today’s ICT sector are proliferating at unprecedented levels, while automated information management systems collect and process exponentially increasing quantities of data. With interoperability and knowledge exchange identified as a core challenge in the sector, this book examines the role ontology engineering can play in providing solutions to the problems of information interoperability and linked data. At the same time as introducing basic concepts of ontology engineering, the book discusses methodological approaches to formal representation of data and information models, thus facilitating information interoperability between heterogeneous, complex and distributed communication systems. In doing so, the text advocates the advantages of using ontology engineering in telecommunications systems. In addition, it offers a wealth of guidance and best-practice techniques for instances in which ontology engineering is applied in cloud services, computer networks and management systems. �...
Models of educational institutions' networking
Shilova Olga Nikolaevna
2015-01-01
The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.
Applied network security monitoring collection, detection, and analysis
Sanders, Chris
2013-01-01
Applied Network Security Monitoring is the essential guide to becoming an NSM analyst from the ground up. This book takes a fundamental approach to NSM, complete with dozens of real-world examples that teach you the key concepts of NSM. Network security monitoring is based on the principle that prevention eventually fails. In the current threat landscape, no matter how much you try, motivated attackers will eventually find their way into your network. At that point, it is your ability to detect and respond to that intrusion that can be the difference between a small incident and a major di
Techniques for Modelling Network Security
Lech Gulbinovič
2012-01-01
The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...
Modeling Network Interdiction Tasks
2015-09-17
allow professionals and families to stay in touch through voice or video calls. Power grids provide electricity to homes , offices, and recreational...instances using IBMr ILOGr CPLEXr Optimization Studio V12.6. For each instance, two solutions are deter- mined. First, the MNDP-a model is solved with no...three values: 0.25, 0.50, or 0.75. The DMP-a model is solved for the various random network instances using IBMr ILOGr CPLEXr Optimization Studio V12.6
The application of network teaching in applied optics teaching
Zhao, Huifu; Piao, Mingxu; Li, Lin; Liu, Dongmei
2017-08-01
Network technology has become a creative tool of changing human productivity, the rapid development of it has brought profound changes to our learning, working and life. Network technology has many advantages such as rich contents, various forms, convenient retrieval, timely communication and efficient combination of resources. Network information resources have become the new education resources, get more and more application in the education, has now become the teaching and learning tools. Network teaching enriches the teaching contents, changes teaching process from the traditional knowledge explanation into the new teaching process by establishing situation, independence and cooperation in the network technology platform. The teacher's role has shifted from teaching in classroom to how to guide students to learn better. Network environment only provides a good platform for the teaching, we can get a better teaching effect only by constantly improve the teaching content. Changchun university of science and technology introduced a BB teaching platform, on the platform, the whole optical classroom teaching and the classroom teaching can be improved. Teachers make assignments online, students learn independently offline or the group learned cooperatively, this expands the time and space of teaching. Teachers use hypertext form related knowledge of applied optics, rich cases and learning resources, set up the network interactive platform, homework submission system, message board, etc. The teaching platform simulated the learning interest of students and strengthens the interaction in the teaching.
Applying Artificial Neural Networks to Evaluate Export Performance: A Relational Approach
Antonio CORREIA de BARROS; Hortensia BARANDAS; Paulo Alexandre PIRES
2009-01-01
The paper applies artificial neural networks to investigate the effect of the exporter’s relationship orientation on the export performance, mediated by the relationship quality, taking into account the supplier’s strategic orientation and the foreign customer’s approach to purchasing. The proposed model is supported mainly by the Second Networking Marketing Paradox, the Commitment-Trust Theory, the Relationship Marketing Paradigm and International Marketing fundamentals. The model developed,...
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
Network models in economics and finance
Pardalos, Panos; Rassias, Themistocles
2014-01-01
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
Do Network Models Just Model Networks? On The Applicability of Network-Oriented Modeling
Treur, J.; Shmueli, Erez
2017-01-01
In this paper for a Network-Oriented Modelling perspective based on temporal-causal networks it is analysed how generic and applicable it is as a general modelling approach and as a computational paradigm. This results in an answer to the question in the title different from: network models just
Thermal Network Modelling Handbook
1972-01-01
Thermal mathematical modelling is discussed in detail. A three-fold purpose was established: (1) to acquaint the new user with the terminology and concepts used in thermal mathematical modelling, (2) to present the more experienced and occasional user with quick formulas and methods for solving everyday problems, coupled with study cases which lend insight into the relationships that exist among the various solution techniques and parameters, and (3) to begin to catalog in an orderly fashion the common formulas which may be applied to automated conversational language techniques.
Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation
Aminia, A. S.
2017-09-01
Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.
A solution for parallel network architectures applied to network defense appliances and sensors
Naber, Eric C.; Velez, Paul G.; Johal, Amanpreet S.
2012-06-01
Network defense has more technologies available for purchase today than ever before. As the number of threats increase, organizations are deploying multiple defense technologies to defend their networks. For instance, an enterprise network boundary often implements multiple network defense appliances, some with overlapping capabilities (e.g., firewalls, IDS/IPS, DNS Defense). These appliances are applied in a serial fashion to create a chain of network processing specifically designed to drop bad traffic from the network. In these architectures, once a packet is dropped by an appliance subsequent appliances do not process it. This introduces significant limitations; (1) Stateful appliances will maintain an internal state which differs from network reality; (2) The network manager cannot determine, or unit test, how each appliance would have treated each packet; (3) The appliance "votes" cannot be combined to achieve higherlevel functionality. To address these limitations, we have developed a novel, backwards-compatible Parallel Architecture for Network Defense Appliances (PANDA). Our approach allows every appliance to process all network traffic and cast a vote to drop or allow each packet. This "crowd-sourcing" approach allows the network designer to take full advantage of each appliance, understand how each appliance is behaving, and achieve new collaborative appliance behavior.
Signed directed social network analysis applied to group conflict
DEFF Research Database (Denmark)
Zheng, Quan; Skillicorn, David; Walther, Olivier
2015-01-01
Real-world social networks contain relationships of multiple different types, but this richness is often ignored in graph-theoretic modelling. We show how two recently developed spectral embedding techniques, for directed graphs (relationships are asymmetric) and for signed graphs (relationships...
Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah
2017-03-01
Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.
Correlation network analysis applied to complex biofilm communities.
Directory of Open Access Journals (Sweden)
Ana E Duran-Pinedo
Full Text Available The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM, which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063. After two rounds of enrichment by a selected helper (Prevotella oris OT311 we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of
Stochastic discrete model of karstic networks
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
Optimized null model for protein structure networks.
Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa
2009-06-26
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by
Optimized null model for protein structure networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model
Applying DNA computation to intractable problems in social network analysis.
Chen, Rick C S; Yang, Stephen J H
2010-09-01
From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Towards Reproducible Descriptions of Neuronal Network Models
Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard
2009-01-01
Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. PMID:19662159
Towards reproducible descriptions of neuronal network models.
Directory of Open Access Journals (Sweden)
Eilen Nordlie
2009-08-01
Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
Wavelet neural networks applied to pulping of oil palm fronds.
Zainuddin, Zarita; Wan Daud, Wan Rosli; Pauline, Ong; Shafie, Amran
2011-12-01
In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained. Copyright © 2011 Elsevier Ltd. All rights reserved.
Collaboration Networks in Applied Conservation Projects across Europe.
Directory of Open Access Journals (Sweden)
Andreea Nita
Full Text Available The main funding instrument for implementing EU policies on nature conservation and supporting environmental and climate action is the LIFE Nature programme, established by the European Commission in 1992. LIFE Nature projects (>1400 awarded are applied conservation projects in which partnerships between institutions are critical for successful conservation outcomes, yet little is known about the structure of collaborative networks within and between EU countries. The aim of our study is to understand the nature of collaboration in LIFE Nature projects using a novel application of social network theory at two levels: (1 collaboration between countries, and (2 collaboration within countries using six case studies: Western Europe (United Kingdom and Netherlands, Eastern Europe (Romania and Latvia and Southern Europe (Greece and Portugal. Using data on 1261 projects financed between 1996 and 2013, we found that Italy was the most successful country not only in terms of awarded number of projects, but also in terms of overall influence being by far the most influent country in the European LIFE Nature network, having the highest eigenvector (0.989 and degree centrality (0.177. Another key player in the network is Netherlands, which ensures a fast communication flow with other network members (closeness-0.318 by staying connected with the most active countries. Although Western European countries have higher centrality scores than most of the Eastern European countries, our results showed that overall there is a lower tendency to create partnerships between different organization categories. Also, the comparisons of the six case studies indicates significant differences in regards to the pattern of creating partnerships, providing valuable information on collaboration on EU nature conservation. This study represents a starting point in predicting the formation of future partnerships within LIFE Nature programme, suggesting ways to improve
Collaboration Networks in Applied Conservation Projects across Europe
Manolache, Steluta; Ciocănea, Cristiana Maria; Miu, Iulia Viorica; Popescu, Viorel Dan
2016-01-01
The main funding instrument for implementing EU policies on nature conservation and supporting environmental and climate action is the LIFE Nature programme, established by the European Commission in 1992. LIFE Nature projects (>1400 awarded) are applied conservation projects in which partnerships between institutions are critical for successful conservation outcomes, yet little is known about the structure of collaborative networks within and between EU countries. The aim of our study is to understand the nature of collaboration in LIFE Nature projects using a novel application of social network theory at two levels: (1) collaboration between countries, and (2) collaboration within countries using six case studies: Western Europe (United Kingdom and Netherlands), Eastern Europe (Romania and Latvia) and Southern Europe (Greece and Portugal). Using data on 1261 projects financed between 1996 and 2013, we found that Italy was the most successful country not only in terms of awarded number of projects, but also in terms of overall influence being by far the most influent country in the European LIFE Nature network, having the highest eigenvector (0.989) and degree centrality (0.177). Another key player in the network is Netherlands, which ensures a fast communication flow with other network members (closeness—0.318) by staying connected with the most active countries. Although Western European countries have higher centrality scores than most of the Eastern European countries, our results showed that overall there is a lower tendency to create partnerships between different organization categories. Also, the comparisons of the six case studies indicates significant differences in regards to the pattern of creating partnerships, providing valuable information on collaboration on EU nature conservation. This study represents a starting point in predicting the formation of future partnerships within LIFE Nature programme, suggesting ways to improve transnational
Collaboration Networks in Applied Conservation Projects across Europe.
Nita, Andreea; Rozylowicz, Laurentiu; Manolache, Steluta; Ciocănea, Cristiana Maria; Miu, Iulia Viorica; Popescu, Viorel Dan
2016-01-01
The main funding instrument for implementing EU policies on nature conservation and supporting environmental and climate action is the LIFE Nature programme, established by the European Commission in 1992. LIFE Nature projects (>1400 awarded) are applied conservation projects in which partnerships between institutions are critical for successful conservation outcomes, yet little is known about the structure of collaborative networks within and between EU countries. The aim of our study is to understand the nature of collaboration in LIFE Nature projects using a novel application of social network theory at two levels: (1) collaboration between countries, and (2) collaboration within countries using six case studies: Western Europe (United Kingdom and Netherlands), Eastern Europe (Romania and Latvia) and Southern Europe (Greece and Portugal). Using data on 1261 projects financed between 1996 and 2013, we found that Italy was the most successful country not only in terms of awarded number of projects, but also in terms of overall influence being by far the most influent country in the European LIFE Nature network, having the highest eigenvector (0.989) and degree centrality (0.177). Another key player in the network is Netherlands, which ensures a fast communication flow with other network members (closeness-0.318) by staying connected with the most active countries. Although Western European countries have higher centrality scores than most of the Eastern European countries, our results showed that overall there is a lower tendency to create partnerships between different organization categories. Also, the comparisons of the six case studies indicates significant differences in regards to the pattern of creating partnerships, providing valuable information on collaboration on EU nature conservation. This study represents a starting point in predicting the formation of future partnerships within LIFE Nature programme, suggesting ways to improve transnational
Modeling semiflexible polymer networks
Broedersz, Chase P.; MacKintosh, Fred C.
2014-01-01
Here, we provide an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have been motivated by their importance in biology. Indeed, crosslinked networks of semiflexible polymers form a major structural component of tissue and living cells. Reconstituted networks o...
Directory of Open Access Journals (Sweden)
Kleber Rogério Moreira Prado
2013-06-01
Full Text Available Current essay forwards a biodegradation model of a dye, used in the textile industry, based on a neural network propped by bootstrap remodeling. Bootstrapped neural network is set to generate estimates that are close to results obtained in an intrinsic experience in which a chemical process is applied. Pseudomonas oleovorans was used in the biodegradation of reactive Black 5. Results show a brief comparison between the information estimated by the proposed approach and the experimental data, with a coefficient of correlation between real and predicted values for a more than 0.99 biodegradation rate. Dye concentration and the solution’s pH failed to interfere in biodegradation index rates. A value above 90% of dye biodegradation was achieved between 1.000 and 1.841 mL 10 mL-1 of microorganism concentration and between 1.000 and 2.000 g 100 mL-1 of glucose concentration within the experimental conditions under analysis.
Complex Networks in Psychological Models
Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.
We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.
Pesquer, Lluís; Jirka, Simon; van de Giesen, Nick; Masó, Joan; Stasch, Christoph; Van Nooyen, Ronald; Prat, Ester; Pons, Xavier
2015-04-01
This work describes the strategy of the European Horizon 2020 project WaterInnEU. Its vision is to enhance the exploitation of EU funded ICT models, tools, protocols and policy briefs related to the water sector and to establish suitable conditions for new market opportunities based on these offerings. The main goals are: • Connect the research results and developments of previous EU funded activities with the already existing data available on European level and also with to the companies that are able to offer products and services based on these tools and data. • Offer an independent marketplace platform complemented by technical and commercial expertise as a service for users to allow the access to products and services best fitting their priorities, capabilities and procurement processes. One of the pillars of WaterInnEU is to stimulate and prioritize the application of international standards into ICT tools and policy briefs. The standardization of formats, services and processes will allow for a harmonized water management between different sectors, fragmented areas and scales (local, regional or international) approaches. Several levels of interoperability will be addressed: • Syntactic: Connecting system and tools together: Syntactic interoperability allows for client and service tools to automatically discover, access, and process data and information (query and exchange parts of a database) and to connect each other in process chains. The discovery of water related data is achieved using metadata cataloguing standards and, in particular, the one adopted by the INSPIRE directive: OGC Catalogue Service for the Web (CSW). • Semantic: Sharing a pan-European conceptual framework This is the ability of computer systems to exchange data with unambiguous, shared meaning. The project therefore addresses not only the packaging of data (syntax), but also the simultaneous transmission of the meaning with the data (semantics). This is accomplished by linking
Complex networks repair strategies: Dynamic models
Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang
2017-09-01
Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... are presented and analyzed in light of business modeling of PN....
A model of coauthorship networks
Zhou, Guochang; Li, Jianping; Xie, Zonglin
2017-10-01
A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property
Flood routing modelling with Artificial Neural Networks
Directory of Open Access Journals (Sweden)
R. Peters
2006-01-01
Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.
2016-11-09
standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial, security-critical design...from a security standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial...is outside the scope of this paper. As such, we focus on event probabilities. The output of the network porosity model is a stream of timestamped
Geostatistical methods applied to field model residuals
DEFF Research Database (Denmark)
Maule, Fox; Mosegaard, K.; Olsen, Nils
consists of measurement errors and unmodelled signal), and is typically assumed to be uncorrelated and Gaussian distributed. We have applied geostatistical methods to analyse the residuals of the Oersted(09d/04) field model [http://www.dsri.dk/Oersted/Field_models/IGRF_2005_candidates/], which is based......The geomagnetic field varies on a variety of time- and length scales, which are only rudimentary considered in most present field models. The part of the observed field that can not be explained by a given model, the model residuals, is often considered as an estimate of the data uncertainty (which....... Once these are known, they can be used in the modelling process to improve the model, estimate its uncertainty and eventually determine the optimal sampling rate in time and space. The uncertainty estimate on the field model can be used to estimate the uncertainty on quantities deduced from the field...
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
A neural network applied to estimate Burr XII distribution parameters
Energy Technology Data Exchange (ETDEWEB)
Abbasi, B., E-mail: b.abbasi@gmail.co [Department of Industrial Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Hosseinifard, S.Z. [Department of Statistics and Operations Research, RMIT University, Melbourne (Australia); Coit, D.W. [Department of Industrial and System Engineering, Rutgers University, Piscataway, NJ (United States)
2010-06-15
The Burr XII distribution can closely approximate many other well-known probability density functions such as the normal, gamma, lognormal, exponential distributions as well as Pearson type I, II, V, VII, IX, X, XII families of distributions. Considering a wide range of shape and scale parameters of the Burr XII distribution, it can have an important role in reliability modeling, risk analysis and process capability estimation. However, estimating parameters of the Burr XII distribution can be a complicated task and the use of conventional methods such as maximum likelihood estimation (MLE) and moment method (MM) is not straightforward. Some tables to estimate Burr XII parameters have been provided by Burr (1942) but they are not adequate for many purposes or data sets. Burr tables contain specific values of skewness and kurtosis and their corresponding Burr XII parameters. Using interpolation or extrapolation to estimate other values may provide inappropriate estimations. In this paper, we present a neural network to estimate Burr XII parameters for different values of skewness and kurtosis as inputs. A trained network is presented, and one can use it without previous knowledge about neural networks to estimate Burr XII distribution parameters. Accurate estimation of the Burr parameters is an extension of simulation studies.
Campus network security model study
Zhang, Yong-ku; Song, Li-ren
2011-12-01
Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.
Applying long short-term memory recurrent neural networks to intrusion detection
Directory of Open Access Journals (Sweden)
Ralf C. Staudemeyer
2015-07-01
Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.
Applying incentive sensitization models to behavioral addiction
DEFF Research Database (Denmark)
Rømer Thomsen, Kristine; Fjorback, Lone; Møller, Arne
2014-01-01
The incentive sensitization theory is a promising model for understanding the mechanisms underlying drug addiction, and has received support in animal and human studies. So far the theory has not been applied to the case of behavioral addictions like Gambling Disorder, despite sharing clinical...
Applied probability models with optimization applications
Ross, Sheldon M
1992-01-01
Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....
Neural networks (NN applied to the commercial properties valuation
Directory of Open Access Journals (Sweden)
J. M. Núñez Tabales
2017-03-01
Full Text Available Several agents, such as buyers and sellers, or local or tax authorities need to estimate the value of properties. There are different approaches to obtain the market price of a dwelling. Many papers have been produced in the academic literature for such purposes, but, these are, almost always, oriented to estimate hedonic prices of residential properties, such as houses or apartments. Here these methodologies are used in the field of estimate market price of commercial premises, using AI techniques. A case study is developed in Cordova —city in the South of Spain—. Neural Networks are an attractive alternative to the traditional hedonic modelling approaches, as they are better adapted to non-linearities of causal relationships and they also produce smaller valuation errors. It is also possible, from the NN model, to obtain implicit prices associated to the main attributes that can explain the variability of the market price of commercial properties.
Modeling semiflexible polymer networks
Broedersz, C.P.; MacKintosh, F.C.
2014-01-01
This is an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have
Applied Integer Programming Modeling and Solution
Chen, Der-San; Dang, Yu
2011-01-01
An accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
A Transfer Learning Approach for Network Modeling
Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li
2012-01-01
Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804
Applied research in uncertainty modeling and analysis
Ayyub, Bilal
2005-01-01
Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...
Directory of Open Access Journals (Sweden)
Gabriel R. Bitran
1995-12-01
Full Text Available Este artigo apresenta a segunda (e última parte do nosso exame dos modelos de redes de filas abertas aplicados a sistemas de manufatura discretos. Nosso enfoque é em modelos de projeto e planejamento de job-shops. Na primeira parte (BITRAN & MORABITO, 1995b revisamos métodos de decomposição exatos e aproximados para modelos de avaliação de desempenho em sistemas com múltiplas classes de produtos e diversas estações de trabalho. Nesta segunda parte examinamos modelos de otimização de três categorias de problemas: a primeira minimiza o investimento de capital de maneira a atingir uma medida de desempenho (estoque em processo ou leadtime, a segunda busca otimizar a medida de desempenho sujeito às restrições de recursos, e a terceira explora resultados de pesquisas recentes com a redução de complexidade mediante reprojeto da planta e da partição de produtos.This paper presents the second (and last part of our survey on open queueing network models applied to discrete manufacturing systems. We focus on design and planning for job-shops. In the first part (Bitran and Morabito, 1995b we reviewed exact and approximate decomposition methods for performance evaluation models for single and multiple product class systems. The second part reviews optimization models of three categories of problems: the first minimizes capital investment subject to attaining a performance measure (WIP or leadtime, the second seeks to optimize the performance measure subject to resource constraints, and the third explores recent research developments in complexity reduction through shop redesign and products partitioning.
Applying deep neural networks to HEP job classification
Wang, L.; Shi, J.; Yan, X.
2015-12-01
The cluster of IHEP computing center is a middle-sized computing system which provides 10 thousands CPU cores, 5 PB disk storage, and 40 GB/s IO throughput. Its 1000+ users come from a variety of HEP experiments. In such a system, job classification is an indispensable task. Although experienced administrator can classify a HEP job by its IO pattern, it is unpractical to classify millions of jobs manually. We present how to solve this problem with deep neural networks in a supervised learning way. Firstly, we built a training data set of 320K samples by an IO pattern collection agent and a semi-automatic process of sample labelling. Then we implemented and trained DNNs models with Torch. During the process of model training, several meta-parameters was tuned with cross-validations. Test results show that a 5- hidden-layer DNNs model achieves 96% precision on the classification task. By comparison, it outperforms a linear model by 8% precision.
Applied Mathematics, Modelling and Computational Science
Kotsireas, Ilias; Makarov, Roman; Melnik, Roderick; Shodiev, Hasan
2015-01-01
The Applied Mathematics, Modelling, and Computational Science (AMMCS) conference aims to promote interdisciplinary research and collaboration. The contributions in this volume cover the latest research in mathematical and computational sciences, modeling, and simulation as well as their applications in natural and social sciences, engineering and technology, industry, and finance. The 2013 conference, the second in a series of AMMCS meetings, was held August 26–30 and organized in cooperation with AIMS and SIAM, with support from the Fields Institute in Toronto, and Wilfrid Laurier University. There were many young scientists at AMMCS-2013, both as presenters and as organizers. This proceedings contains refereed papers contributed by the participants of the AMMCS-2013 after the conference. This volume is suitable for researchers and graduate students, mathematicians and engineers, industrialists, and anyone who would like to delve into the interdisciplinary research of applied and computational mathematics ...
Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review.
Langarizadeh, Mostafa; Moghbeli, Fateme
2016-10-01
Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.
Neural networks applied to discriminate botanical origin of honeys.
Anjos, Ofélia; Iglesias, Carla; Peres, Fátima; Martínez, Javier; García, Ángela; Taboada, Javier
2015-05-15
The aim of this work is develop a tool based on neural networks to predict the botanical origin of honeys using physical and chemical parameters. The managed database consists of 49 honey samples of 2 different classes: monofloral (almond, holm oak, sweet chestnut, eucalyptus, orange, rosemary, lavender, strawberry trees, thyme, heather, sunflower) and multifloral. The moisture content, electrical conductivity, water activity, ashes content, pH, free acidity, colorimetric coordinates in CIELAB space (L(∗), a(∗), b(∗)) and total phenols content of the honey samples were evaluated. Those properties were considered as input variables of the predictive model. The neural network is optimised through several tests with different numbers of neurons in the hidden layer and also with different input variables. The reduced error rates (5%) allow us to conclude that the botanical origin of honey can be reliably and quickly known from the colorimetric information and the electrical conductivity of honey. Copyright © 2014 Elsevier Ltd. All rights reserved.
Mobility Model for Tactical Networks
Rollo, Milan; Komenda, Antonín
In this paper a synthetic mobility model which represents behavior and movement pattern of heterogeneous units in disaster relief and battlefield scenarios is proposed. These operations usually take place in environment without preexisting communication infrastructure and units thus have to be connected by wireless communication network. Units cooperate to fulfill common tasks and communication network has to serve high amount of communication requests, especially data, voice and video stream transmissions. To verify features of topology control, routing and interaction protocols software simulations are usually used, because of their scalability, repeatability and speed. Behavior of all these protocols relies on the mobility model of the network nodes, which has to resemble real-life movement pattern. Proposed mobility model is goal-driven and provides support for various types of units, group mobility and realistic environment model with obstacles. Basic characteristics of the mobility model like node spatial distribution and average node degree were analyzed.
Modelling freeway networks by hybrid stochastic models
Boel, R.; Mihaylova, L.
2004-01-01
Traffic flow on freeways is a nonlinear, many-particle phenomenon, with complex interactions between the vehicles. This paper presents a stochastic hybrid model of freeway traffic at a time scale and at a level of detail suitable for on-line flow estimation, for routing and ramp metering control. The model describes the evolution of continuous and discrete state variables. The freeway is considered as a network of components, each component representing a different section of the network. The...
Directory of Open Access Journals (Sweden)
Martin Florian
2012-05-01
Full Text Available Abstract Background High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK
Models as Tools of Analysis of a Network Organisation
Directory of Open Access Journals (Sweden)
Wojciech Pająk
2013-06-01
Full Text Available The paper presents models which may be applied as tools of analysis of a network organisation. The starting point of the discussion is defining the following terms: supply chain and network organisation. Further parts of the paper present basic assumptions analysis of a network organisation. Then the study characterises the best known models utilised in analysis of a network organisation. The purpose of the article is to define the notion and the essence of network organizations and to present the models used for their analysis.
Applying temporal network analysis to the venture capital market
Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene
2015-10-01
Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.
Network model of security system
Directory of Open Access Journals (Sweden)
Adamczyk Piotr
2016-01-01
Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.
1987-10-01
include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen
Model Microvascular Networks Can Have Many Equilibria.
Karst, Nathaniel J; Geddes, John B; Carr, Russell T
2017-03-01
We show that large microvascular networks with realistic topologies, geometries, boundary conditions, and constitutive laws can exhibit many steady-state flow configurations. This is in direct contrast to most previous studies which have assumed, implicitly or explicitly, that a given network can only possess one equilibrium state. While our techniques are general and can be applied to any network, we focus on two distinct network types that model human tissues: perturbed honeycomb networks and random networks generated from Voronoi diagrams. We demonstrate that the disparity between observed and predicted flow directions reported in previous studies might be attributable to the presence of multiple equilibria. We show that the pathway effect, in which hematocrit is steadily increased along a series of diverging junctions, has important implications for equilibrium discovery, and that our estimates of the number of equilibria supported by these networks are conservative. If a more complete description of the plasma skimming effect that captures red blood cell allocation at junctions with high feed hematocrit were to be obtained empirically, then the number of equilibria found by our approach would at worst remain the same and would in all likelihood increase significantly.
Bayesian networks applied to process diagnostics. Applications in energy industry
Energy Technology Data Exchange (ETDEWEB)
Widarsson, Bjoern (ed.); Karlsson, Christer; Dahlquist, Erik [Maelardalen Univ., Vaesteraas (Sweden); Nielsen, Thomas D.; Jensen, Finn V. [Aalborg Univ. (Denmark)
2004-10-01
Uncertainty in process operation occurs frequently in heat and power industry. This makes it hard to find the occurrence of an abnormal process state from a number of process signals (measurements) or find the correct cause to an abnormality. Among several other methods, Bayesian Networks (BN) is a method to build a model which can handle uncertainty in both process signals and the process itself. The purpose of this project is to investigate the possibilities to use BN for fault detection and diagnostics in combined heat and power industries through execution of two different applications. Participants from Aalborg University represent the knowledge of BN and participants from Maelardalen University have the experience from modelling heat and power applications. The co-operation also includes two energy companies; Elsam A/S (Nordjyllandsverket) and Maelarenergi AB (Vaesteraas CHP-plant), where the two applications are made with support from the plant personnel. The project ended out in two quite different applications. At Nordjyllandsverket, an application based (due to the lack of process knowledge) on pure operation data is build with capability to detect an abnormal process state in a coal mill. Detection is made through a conflict analysis when entering process signals into a model built by analysing the operation database. The application at Maelarenergi is built with a combination of process knowledge and operation data and can detect various faults caused by the fuel. The process knowledge is used to build a causal network structure and the structure is then trained by data from the operation database. Both applications are made as off-online applications, but they are ready for being run on-line. The performance of fault detection and diagnostics are good, but a lack of abnormal process states with known cause reduces the evaluation possibilities. Advantages with combining expert knowledge of the process with operation data are the possibility to represent
QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
Social network models predict movement and connectivity in ecological landscapes
Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.
2011-01-01
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks
2014-01-01
Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226
Applying incentive sensitization models to behavioral addiction.
Rømer Thomsen, Kristine; Fjorback, Lone O; Møller, Arne; Lou, Hans C
2014-09-01
The incentive sensitization theory is a promising model for understanding the mechanisms underlying drug addiction, and has received support in animal and human studies. So far the theory has not been applied to the case of behavioral addictions like Gambling Disorder, despite sharing clinical symptoms and underlying neurobiology. We examine the relevance of this theory for Gambling Disorder and point to predictions for future studies. The theory promises a significant contribution to the understanding of behavioral addiction and opens new avenues for treatment. Copyright © 2014 Elsevier Ltd. All rights reserved.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
A Complex Network Approach to Distributional Semantic Models.
Directory of Open Access Journals (Sweden)
Akira Utsumi
Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.
Commercial Consolidation Model Applied to Transport Infrastructure
Energy Technology Data Exchange (ETDEWEB)
Guilherme de Aragão, J.J.; Santos Fontes Pereira, L. dos; Yamashita, Y.
2016-07-01
Since the 1990s, transport concessions, including public-private partnerships (PPPs), have been increasingly adopted by governments as an alternative for financing and operations in public investments, especially in transport infrastructure. The advantage pointed out by proponents of these models lies in merging the expertise and capital of the private sector to the public interest. Several arrangements are possible and have been employed in different cases. After the duration of the first PPP contracts in transportation, many authors have analyzed the success and failure factors of partnerships. The occurrence of failures in some stages of the process can greatly encumber the public administration, incurring losses to the fiscal responsibility of the competent bodies. This article aims to propose a new commercial consolidation model applied to transport infrastructure to ensure fiscal sustainability and overcome the weaknesses of current models. Initially, a systematic review of the literature covering studies on transport concessions between 1990 and 2015 is offered, where the different approaches between various countries are compared and the critical success factors indicated in the studies are identified. In the subsequent part of the paper, an approach for the commercial consolidation of the infrastructure concessions is presented, where the concessionary is paid following a finalistic performance model, which includes the overall fiscal balance of regional growth. Finally, the papers analyses the usefulness of the model in coping with the critical success factors explained before. (Author)
Methods for model selection in applied science and engineering.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2004-10-01
Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be
Applying Information Theory to Neuronal Networks: From Theory to Experiments
Jung, T.I.; Vogiatzian, F.; Har-Shemesh, O.; Fitzsimons, C.P.; Quax, R.
2014-01-01
Information-theory is being increasingly used to analyze complex, self-organizing processes on networks, predominantly in analytical and numerical studies. Perhaps one of the most paradigmatic complex systems is a network of neurons, in which cognition arises from the information storage, transfer,
The harmonics detection method based on neural network applied ...
African Journals Online (AJOL)
user
Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.
An Efficient Multitask Scheduling Model for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Hongsheng Yin
2014-01-01
Full Text Available The sensor nodes of multitask wireless network are constrained in performance-driven computation. Theoretical studies on the data processing model of wireless sensor nodes suggest satisfying the requirements of high qualities of service (QoS of multiple application networks, thus improving the efficiency of network. In this paper, we present the priority based data processing model for multitask sensor nodes in the architecture of multitask wireless sensor network. The proposed model is deduced with the M/M/1 queuing model based on the queuing theory where the average delay of data packets passing by sensor nodes is estimated. The model is validated with the real data from the Huoerxinhe Coal Mine. By applying the proposed priority based data processing model in the multitask wireless sensor network, the average delay of data packets in a sensor nodes is reduced nearly to 50%. The simulation results show that the proposed model can improve the throughput of network efficiently.
ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Tamara Gvozdenović
2007-06-01
Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.
Network Models of Mechanical Assemblies
Whitney, Daniel E.
Recent network research has sought to characterize complex systems with a number of statistical metrics, such as power law exponent (if any), clustering coefficient, community behavior, and degree correlation. Use of such metrics represents a choice of level of abstraction, a balance of generality and detailed accuracy. It has been noted that "social networks" consistently display clustering coefficients that are higher than those of random or generalized random networks, that they have small world properties such as short path lengths, and that they have positive degree correlations (assortative mixing). "Technological" or "non-social" networks display many of these characteristics except that they generally have negative degree correlations (disassortative mixing). [Newman 2003i] In this paper we examine network models of mechanical assemblies. Such systems are well understood functionally. We show that there is a cap on their average nodal degree and that they have negative degree correlations (disassortative mixing). We identify specific constraints arising from first principles, their structural patterns, and engineering practice that suggest why they have these properties. In addition, we note that their main "motif" is closed loops (as it is for electric and electronic circuits), a pattern that conventional network analysis does not detect but which is used by software intended to aid in the design of such systems.
Service entity network virtualization architecture and model
Jin, Xue-Guang; Shou, Guo-Chu; Hu, Yi-Hong; Guo, Zhi-Gang
2017-07-01
Communication network can be treated as a complex network carrying a variety of services and service can be treated as a network composed of functional entities. There are growing interests in multiplex service entities where individual entity and link can be used for different services simultaneously. Entities and their relationships constitute a service entity network. In this paper, we introduced a service entity network virtualization architecture including service entity network hierarchical model, service entity network model, service implementation and deployment of service entity networks. Service entity network oriented multiplex planning model were also studied and many of these multiplex models were characterized by a significant multiplex of the links or entities in different service entity network. Service entity networks were mapped onto shared physical resources by dynamic resource allocation controller. The efficiency of the proposed architecture was illustrated in a simulation environment that allows for comparative performance evaluation. The results show that, compared to traditional networking architecture, this architecture has a better performance.
Polymer networks: Modeling and applications
Masoud, Hassan
Polymer networks are an important class of materials that are ubiquitously found in natural, biological, and man-made systems. The complex mesoscale structure of these soft materials has made it difficult for researchers to fully explore their properties. In this dissertation, we introduce a coarse-grained computational model for permanently cross-linked polymer networks than can properly capture common properties of these materials. We use this model to study several practical problems involving dry and solvated networks. Specifically, we analyze the permeability and diffusivity of polymer networks under mechanical deformations, we examine the release of encapsulated solutes from microgel capsules during volume transitions, and we explore the complex tribological behavior of elastomers. Our simulations reveal that the network transport properties are defined by the network porosity and by the degree of network anisotropy due to mechanical deformations. In particular, the permeability of mechanically deformed networks can be predicted based on the alignment of network filaments that is characterized by a second order orientation tensor. Moreover, our numerical calculations demonstrate that responsive microcapsules can be effectively utilized for steady and pulsatile release of encapsulated solutes. We show that swollen gel capsules allow steady, diffusive release of nanoparticles and polymer chains, whereas gel deswelling causes burst-like discharge of solutes driven by an outward flow of the solvent initially enclosed within a shrinking capsule. We further demonstrate that this hydrodynamic release can be regulated by introducing rigid microscopic rods in the capsule interior. We also probe the effects of velocity, temperature, and normal load on the sliding of elastomers on smooth and corrugated substrates. Our friction simulations predict a bell-shaped curve for the dependence of the friction coefficient on the sliding velocity. Our simulations also illustrate
Fast frequency hopping codes applied to SAC optical CDMA network
Tseng, Shin-Pin
2015-06-01
This study designed a fast frequency hopping (FFH) code family suitable for application in spectral-amplitude-coding (SAC) optical code-division multiple-access (CDMA) networks. The FFH code family can effectively suppress the effects of multiuser interference and had its origin in the frequency hopping code family. Additional codes were developed as secure codewords for enhancing the security of the network. In considering the system cost and flexibility, simple optical encoders/decoders using fiber Bragg gratings (FBGs) and a set of optical securers using two arrayed-waveguide grating (AWG) demultiplexers (DeMUXs) were also constructed. Based on a Gaussian approximation, expressions for evaluating the bit error rate (BER) and spectral efficiency (SE) of SAC optical CDMA networks are presented. The results indicated that the proposed SAC optical CDMA network exhibited favorable performance.
A neural network model for texture discrimination.
Xing, J; Gerstein, G L
1993-01-01
A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.
Terahertz spectroscopy applied to food model systems
DEFF Research Database (Denmark)
Møller, Uffe
Water plays a crucial role in the quality of food. Apart from the natural water content of a food product, the state of that water is very important. Water can be found integrated into the biological material or it can be added during production of the product. Currently it is difficult to differ...... to differentiate between these types of water in subsequent quality controls. This thesis describes terahertz time-domain spectroscopy applied on aqueous food model systems, with particular focus on ethanol-water mixtures and confined water pools in inverse micelles.......Water plays a crucial role in the quality of food. Apart from the natural water content of a food product, the state of that water is very important. Water can be found integrated into the biological material or it can be added during production of the product. Currently it is difficult...
Conformal Field Theory Applied to Loop Models
Jacobsen, Jesper Lykke
The application of methods of quantum field theory to problems of statistical mechanics can in some sense be traced back to Onsager's 1944 solution [1] of the two-dimensional Ising model. It does however appear fair to state that the 1970's witnessed a real gain of momentum for this approach, when Wilson's ideas on scale invariance [2] were applied to study critical phenomena, in the form of the celebrated renormalisation group [3]. In particular, the so-called ɛ expansion permitted the systematic calculation of critical exponents [4], as formal power series in the space dimensionality d, below the upper critical dimension d c . An important lesson of these efforts was that critical exponents often do not depend on the precise details of the microscopic interactions, leading to the notion of a restricted number of distinct universality classes.
Applying a Dynamic Resource Supply Model in a Smart Grid
Directory of Open Access Journals (Sweden)
Kaiyu Wan
2014-09-01
Full Text Available Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS. In our previous work, a resource model called the dynamic resource supply model (DRSM has been proposed to handle resources specification, management and allocation in CPS. In this paper, we are integrating the DRSM with service-oriented architecture and applying it to a smart grid (SG, one of the most complex CPS examples. We give the detailed design of the SG for electricity charging request and electricity allocation between plug-in hybrid electric vehicles (PHEV and DRSM through the Android system. In the design, we explain a mechanism for electricity consumption with data collection and re-allocation through ZigBee network. In this design, we verify the correctness of this resource model for expected electricity allocation.
How can social network analysis contribute to social behavior research in applied ethology?
Makagon, Maja M; McCowan, Brenda; Mench, Joy A
2012-05-01
Social network analysis is increasingly used by behavioral ecologists and primatologists to describe the patterns and quality of interactions among individuals. We provide an overview of this methodology, with examples illustrating how it can be used to study social behavior in applied contexts. Like most kinds of social interaction analyses, social network analysis provides information about direct relationships (e.g. dominant-subordinate relationships). However, it also generates a more global model of social organization that determines how individual patterns of social interaction relate to individual and group characteristics. A particular strength of this approach is that it provides standardized mathematical methods for calculating metrics of sociality across levels of social organization, from the population and group levels to the individual level. At the group level these metrics can be used to track changes in social network structures over time, evaluate the effect of the environment on social network structure, or compare social structures across groups, populations or species. At the individual level, the metrics allow quantification of the heterogeneity of social experience within groups and identification of individuals who may play especially important roles in maintaining social stability or information flow throughout the network.
Target-Centric Network Modeling
DEFF Research Database (Denmark)
Mitchell, Dr. William L.; Clark, Dr. Robert M.
In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts....
A Cascade-Based Emergency Model for Water Distribution Network
Directory of Open Access Journals (Sweden)
Qing Shuang
2015-01-01
Full Text Available Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.
Biological transportation networks: Modeling and simulation
Albi, Giacomo
2015-09-15
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
An image segmentation method based on network clustering model
Jiao, Yang; Wu, Jianshe; Jiao, Licheng
2018-01-01
Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.
A Cooperation Model Applied in a Kindergarten
Directory of Open Access Journals (Sweden)
Jose I. Rodriguez
2011-10-01
Full Text Available The need for collaboration in a global world has become a key factor for success for many organizations and individuals. However in several regions and organizations in the world, it has not happened yet. One of the settings where major obstacles occur for collaboration is in the business arena, mainly because of competitive beliefs that cooperation could hurt profitability. We have found such behavior in a wide variety of countries, in advanced and developing economies. Such cultural behaviors or traits characterized entrepreneurs by working in isolation, avoiding the possibilities of building clusters to promote regional development. The needs to improve the essential abilities that conforms cooperation are evident. It is also very difficult to change such conduct with adults. So we decided to work with children to prepare future generations to live in a cooperative world, so badly hit by greed and individualism nowadays. We have validated that working with children at an early age improves such behavior. This paper develops a model to enhance the essential abilities in order to improve cooperation. The model has been validated by applying it at a kindergarten school.
Directory of Open Access Journals (Sweden)
Juan Pablo Bermeo M.
2016-01-01
Full Text Available To get successful the companies need a combination of several factors, the most important one is the management of Supply Chain. This paper proposes the use of intelligent systems such as Artificial Neural Networks (ANN and Genetic Algorithms as support systems together with monitoring indicators and monitoring, in order to implement the management model Triple A, which is focused on Agility-Adaptability-Alignment, where the “Agility” is the speed of response to changes in demand, “Adaptability” is the ability to tailor the supply chain front market fluctuations and "Alignment" is to align the chain between consumers and suppliers. The Neural Network was trained to work as a predictor of demand and will improve the “agility” of the supply chain, the genetic algorithm is used to obtain optimal routes of pickup from providers, this support to the “alignment” the product of suppliers in the supply chain to final customers; the Neural Network with the Genetic Algorithm together serve as support to “adapt” the supply chain to variations of demand and the suppliers, however, for successful of the model are need other factors such as the use of indicators and training of staff on the administration of management model triple A in the supply chain.
Applying a rateless code in content delivery networks
Suherman; Zarlis, Muhammad; Parulian Sitorus, Sahat; Al-Akaidi, Marwan
2017-09-01
Content delivery network (CDN) allows internet providers to locate their services, to map their coverage into networks without necessarily to own them. CDN is part of the current internet infrastructures, supporting multi server applications especially social media. Various works have been proposed to improve CDN performances. Since accesses on social media servers tend to be short but frequent, providing redundant to the transmitted packets to ensure lost packets not degrade the information integrity may improve service performances. This paper examines the implementation of rateless code in the CDN infrastructure. The NS-2 evaluations show that rateless code is able to reduce packet loss up to 50%.
Applying New Network Security Technologies to SCADA Systems.
Energy Technology Data Exchange (ETDEWEB)
Hurd, Steven A; Stamp, Jason Edwin; Duggan, David P; Chavez, Adrian R.
2006-11-01
Supervisory Control and Data Acquisition (SCADA) systems for automation are very important for critical infrastructure and manufacturing operations. They have been implemented to work in a number of physical environments using a variety of hardware, software, networking protocols, and communications technologies, often before security issues became of paramount concern. To offer solutions to security shortcomings in the short/medium term, this project was to identify technologies used to secure "traditional" IT networks and systems, and then assess their efficacy with respect to SCADA systems. These proposed solutions must be relatively simple to implement, reliable, and acceptable to SCADA owners and operators. 4This page intentionally left blank.
Capacity allocation in wireless communication networks - models and analyses
Litjens, Remco
2003-01-01
This monograph has concentrated on capacity allocation in cellular and Wireless Local Area Networks, primarily with a network operator’s perspective. In the introduc- tory chapter, a reference model has been proposed for the extensive suite of capacity allocation mechanisms that can be applied at
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
Heterogeneous information network model for equipment-standard system
Yin, Liang; Shi, Li-Chen; Zhao, Jun-Yan; Du, Song-Yang; Xie, Wen-Bo; Yuan, Fei; Chen, Duan-Bing
2018-01-01
Entity information network is used to describe structural relationships between entities. Taking advantage of its extension and heterogeneity, entity information network is more and more widely applied to relationship modeling. Recent years, lots of researches about entity information network modeling have been proposed, while seldom of them concentrate on equipment-standard system with properties of multi-layer, multi-dimension and multi-scale. In order to efficiently deal with some complex issues in equipment-standard system such as standard revising, standard controlling, and production designing, a heterogeneous information network model for equipment-standard system is proposed in this paper. Three types of entities and six types of relationships are considered in the proposed model. Correspondingly, several different similarity-measuring methods are used in the modeling process. The experiments show that the heterogeneous information network model established in this paper can reflect relationships between entities accurately. Meanwhile, the modeling process has a good performance on time consumption.
Neural networks applied to the classification of remotely sensed data
Mulder, Nanno; Spreeuwers, Lieuwe Jan
1991-01-01
A neural network with topology 2-8-8 is evaluated against the standard of supervised non-parametric maximum likelihood classification. The purpose of the evaluation is to compare the performance in terms of training speed and quality of classification. Classification is done on multispectral data
Applying Real Options Thinking to Information Security in Networked Organizations
Daneva, Maia
2006-01-01
An information security strategy of an organization participating in a networked business sets out the plans for designing a variety of actions that ensure confidentiality, availability, and integrity of company’s key information assets. The actions are concerned with authentication and
Energy modelling in sensor networks
Directory of Open Access Journals (Sweden)
D. Schmidt
2007-06-01
Full Text Available Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.
Probabilistic logic modeling of network reliability for hybrid network architectures
Energy Technology Data Exchange (ETDEWEB)
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Introducing Synchronisation in Deterministic Network Models
DEFF Research Database (Denmark)
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...
Applying gene regulatory network logic to the evolution of social behavior.
Baran, Nicole M; McGrath, Patrick T; Streelman, J Todd
2017-06-06
Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2017-11-02
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
An Enhanced Probabilistic Neural Network Approach Applied to Text Classification
Marques Ciarelli, Patrick; Oliveira, Elias
Text classification is still a quite difficult problem to be dealt with both by the academia and by the industrial areas. On the top of that, the importance of aggregating a set of related amount of text documents is steadily growing in importance these days. The presence of multi-labeled texts and great quantity of classes turn this problem even more challenging. In this article we present an enhanced version of Probabilistic Neural Network using centroids to tackle the multi-label classification problem. We carried out some experiments comparing our proposed classifier against the other well known classifiers in the literature which were specially designed to treat this type of problem. By the achieved results, we observed that our novel approach were superior to the other classifiers and faster than the Probabilistic Neural Network without the use of centroids.
A Configurable Sensor Network Applied to Ambient Assisted Living
Directory of Open Access Journals (Sweden)
Juan J. Villacorta
2011-11-01
Full Text Available The rising older people population has increased the interest in Ambient Assisted Living systems. This article presents a system for monitoring the disabled or older persons developed from an existing surveillance system. The modularity and adaptability characteristics of the system allow an easy adaptation for a different purpose. The proposed system uses a network of sensors capable of motion detection that includes fall warning, identification of persons and a configurable control system which allows its use in different scenarios.
Applied and computational harmonic analysis on graphs and networks
Irion, Jeff; Saito, Naoki
2015-09-01
In recent years, the advent of new sensor technologies and social network infrastructure has provided huge opportunities and challenges for analyzing data recorded on such networks. In the case of data on regular lattices, computational harmonic analysis tools such as the Fourier and wavelet transforms have well-developed theories and proven track records of success. It is therefore quite important to extend such tools from the classical setting of regular lattices to the more general setting of graphs and networks. In this article, we first review basics of graph Laplacian matrices, whose eigenpairs are often interpreted as the frequencies and the Fourier basis vectors on a given graph. We point out, however, that such an interpretation is misleading unless the underlying graph is either an unweighted path or cycle. We then discuss our recent effort of constructing multiscale basis dictionaries on a graph, including the Hierarchical Graph Laplacian Eigenbasis Dictionary and the Generalized Haar-Walsh Wavelet Packet Dictionary, which are viewed as generalizations of the classical hierarchical block DCTs and the Haar-Walsh wavelet packets, respectively, to the graph setting. Finally, we demonstrate the usefulness of our dictionaries by using them to simultaneously segment and denoise 1-D noisy signals sampled on regular lattices, a problem where classical tools have difficulty.
Neural network stochastic simulation applied for quantifying uncertainties
Directory of Open Access Journals (Sweden)
N Foudil-Bey
2016-09-01
Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
RMBNToolbox: random models for biochemical networks
Directory of Open Access Journals (Sweden)
Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
Threat model framework and methodology for personal networks (PNs)
DEFF Research Database (Denmark)
Prasad, Neeli R.
2007-01-01
is to give a structured, convenient approach for building threat models. A framework for the threat model is presented with a list of requirements for methodology. The methodology will be applied to build a threat model for Personal Networks. Practical tools like UML sequence diagrams and attack trees have...
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
CellNet: network biology applied to stem cell engineering.
Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J
2014-08-14
Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering. Copyright © 2014 Elsevier Inc. All rights reserved.
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Network bandwidth utilization forecast model on high bandwidth networks
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2015-03-30
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Neural network applied to elemental archaeological Marajoara ceramic compositions
Energy Technology Data Exchange (ETDEWEB)
Toyota, Rosimeiri G.; Munita, Casimiro S., E-mail: rosimeiritoy@yahoo.com.b, E-mail: camunita@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Boscarioli, Clodis, E-mail: boscarioli@gmail.co [Universidade Estadual do Oeste do Parana, Cascavel, PR (Brazil). Centro de Ciencias Exatas e Tecnologicas. Colegiado de Informatica; Hernandez, Emilio D.M., E-mail: boscarioli@gmail.co [Universidade de Sao Paulo (USP), SP (Brazil). Escola Politecnica; Neves, Eduardo G.; Demartini, Celia C., E-mail: eduardo@pq.cnpq.b [Museu de Arqueologia e Etnologia (MAE/USP), Sao Paulo, SP (Brazil)
2009-07-01
In the last decades several analytical techniques have been used in archaeological ceramics studies. However, instrumental neutron activation analysis, INAA, employing gamma-ray spectrometry seems to be the most suitable technique because it is a simple analytical method in its purely instrumental form. The purpose of this work was to determine the concentration of Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Lu, Na, Nd, Rb, Sb, Sc, Sm, Ta, Tb, Th, U, Yb, and Zn in 160 original marajoara ceramic fragments by INAA. Marajoara ceramics culture was sophisticated and well developed. This culture reached its peak during the V and XIV centuries in Marajo Island located on the Amazon River delta area in Brazil. The purpose of the quantitative data was to identify compositionally homogeneous groups within the database. Having this in mind, the data set was first converted to base-10 logarithms to compensate for the differences in magnitude between major elements and trace elements, and also to yield a closer to normal distribution for several trace elements. After that, the data were analyzed using the Mahalanobis distance and using the lambda Wilks as critical value to identify the outliers. The similarities among the samples were studied by means of cluster analysis, principal components analysis and discriminant analysis. Additional confirmation of these groups was made by using elemental concentration bivariate plots. The results showed that there were two very well defined groups in the data set. In addition, the database was studied using artificial neural network with unsupervised learning strategy known as self-organizing maps to classify the marajoara ceramics. The experiments carried out showed that self-organizing maps artificial neural network is capable of discriminating ceramic fragments like multivariate statistical methods, and, again the results showed that the database was formed by two groups. (author)
Hybrid neural network bushing model for vehicle dynamics simulation
Energy Technology Data Exchange (ETDEWEB)
Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)
2008-12-15
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
Principles of Biomimetic Vascular Network Design Applied to a Tissue-Engineered Liver Scaffold
Hoganson, David M.; Pryor, Howard I.; Spool, Ira D.; Burns, Owen H.; Gilmore, J. Randall
2010-01-01
Branched vascular networks are a central component of scaffold architecture for solid organ tissue engineering. In this work, seven biomimetic principles were established as the major guiding technical design considerations of a branched vascular network for a tissue-engineered scaffold. These biomimetic design principles were applied to a branched radial architecture to develop a liver-specific vascular network. Iterative design changes and computational fluid dynamic analysis were used to optimize the network before mold manufacturing. The vascular network mold was created using a new mold technique that achieves a 1:1 aspect ratio for all channels. In vitro blood flow testing confirmed the physiologic hemodynamics of the network as predicted by computational fluid dynamic analysis. These results indicate that this biomimetic liver vascular network design will provide a foundation for developing complex vascular networks for solid organ tissue engineering that achieve physiologic blood flow. PMID:20001254
An acoustical model based monitoring network
Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der
2010-01-01
In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the
An adaptive complex network model for brain functional networks.
Directory of Open Access Journals (Sweden)
Ignacio J Gomez Portillo
Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.
Applying Modeling Tools to Ground System Procedures
Di Pasquale, Peter
2012-01-01
As part of a long-term effort to revitalize the Ground Systems (GS) Engineering Section practices, Systems Modeling Language (SysML) and Business Process Model and Notation (BPMN) have been used to model existing GS products and the procedures GS engineers use to produce them.
Applying mechanistic models in bioprocess development
DEFF Research Database (Denmark)
Lencastre Fernandes, Rita; Bodla, Vijaya Krishna; Carlquist, Magnus
2013-01-01
incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic......The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore...... models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part...
Modeling gene regulatory networks: A network simplification algorithm
Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.
2016-12-01
Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
The model of social crypto-network
Directory of Open Access Journals (Sweden)
Марк Миколайович Орел
2015-06-01
Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Entropy Characterization of Random Network Models
Directory of Open Access Journals (Sweden)
Pedro J. Zufiria
2017-06-01
Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.
The model of social crypto-network
Марк Миколайович Орел
2015-01-01
The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Neural network models: Insights and prescriptions from practical applications
Energy Technology Data Exchange (ETDEWEB)
Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)
1995-12-31
Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.
Character-level Convolutional Network for Text Classification Applied to Chinese Corpus
Huang, Weijie; Wang, Jun
2016-01-01
This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Applying the Sport Education Model to Tennis
Ayvazo, Shiri
2009-01-01
The physical education field abounds with theoretically sound curricular approaches such as fitness education, skill theme approach, tactical approach, and sport education. In an era that emphasizes authentic sport experiences, the Sport Education Model includes unique features that sets it apart from other curricular models and can be a valuable…
Bayesian Network Webserver: a comprehensive tool for biological network modeling.
Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan
2013-11-01
The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.
Complex Network Theory Applied to the Growth of Kuala Lumpur's Public Urban Rail Transit Network.
Directory of Open Access Journals (Sweden)
Rui Ding
Full Text Available Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality's closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network's growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.
Applying Game Theory in 802.11 Wireless Networks
Directory of Open Access Journals (Sweden)
Tomas Cuzanauskas
2015-07-01
Full Text Available IEEE 802.11 is one of the most popular wireless technologies in recent days. Due to easiness of adaption and relatively low cost the demand for IEEE 802.11 devices is increasing exponentially. IEEE works in two bands 2.4 GHz and 5 GHz, these bands are known as ISM band. The unlicensed bands are managed by authority which set simple rules to follow when using unlicensed bands, the rules includes requirements as maximum power, out-of-band emissions control as well as interference mitigation. However these rules became outdated as IEEE 802.11 technology is emerging and evolving in hours the rules aren’t well suited for current capabilities of IEEE 802.11 devices. In this article we present game theory based algorithm for IEEE 802.11 wireless devices, we will show that by using game theory it’s possible to achieve better usage of unlicensed spectrum as well as partially decline CSMA/CA. Finally by using this approach we might relax the currently applied maximum power rules for ISM bands, which enable IEEE 802.11 to work on longer distance and have better propagation characteristics.
Applied modelling and computing in social science
Povh, Janez
2015-01-01
In social science outstanding results are yielded by advanced simulation methods, based on state of the art software technologies and an appropriate combination of qualitative and quantitative methods. This book presents examples of successful applications of modelling and computing in social science: business and logistic process simulation and optimization, deeper knowledge extractions from big data, better understanding and predicting of social behaviour and modelling health and environment changes.
Probabilistic Priority Message Checking Modeling Based on Controller Area Networks
Lin, Cheng-Min
Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.
AVIRIS data and neural networks applied to an urban ecosystem
Ridd, Merrill K.; Ritter, Niles D.; Bryant, Nevin A.; Green, Robert O.
1992-01-01
Urbanization is expanding on every continent. Although urban/industrial areas occupy a small percentage of the total landscape of the earth, their influence extends far beyond their borders, affecting terrestrial, aquatic, and atmospheric systems globally. Yet little has been done to characterize urban ecosystems of their linkages to other systems horizontally or vertically. With remote sensing we now have the tools to characterize, monitor, and model urban landscapes world-wide. However, the remote sensing performed on cities so far has concentrated on land-use patterns as distinct from land-cover or composition. The popular Anderson system is entirely land-use oriented in urban areas. This paper begins with the premise that characterizing the biophysical composition of urban environments is fundamental to understanding urban/industrial ecosystems, and, in turn, supports the modeling of other systems interfacing with urban systems. Further, it is contended that remote sensing is a tool poised to provide the biophysical composition data to characterize urban landscapes.
Towards an evolutionary model of transcription networks.
Directory of Open Access Journals (Sweden)
Dan Xie
2011-06-01
Full Text Available DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs, we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.
Retail optimization in Romanian metallurgical industry by applying of fuzzy networks concept
Directory of Open Access Journals (Sweden)
Ioana Adrian
2017-01-01
Full Text Available Our article presents possibilities of applying the concept Fuzzy Networks for an efficient metallurgical industry in Romania. We also present and analyze Fuzzy Networks complementary concepts, such as Expert Systems (ES, Enterprise Resource Planning (ERP, Analytics and Intelligent Strategies (SAI. The main results of our article are based on a case study of the possibilities of applying these concepts in metallurgy through Fuzzy Networks. Also, it is presented a case study on the application of the FUZZY concept on the Romanian metallurgical industry.
Applying Bayesian belief networks in rapid response situations
Energy Technology Data Exchange (ETDEWEB)
Gibson, William L [Los Alamos National Laboratory; Deborah, Leishman, A. [Los Alamos National Laboratory; Van Eeckhout, Edward [Los Alamos National Laboratory
2008-01-01
The authors have developed an enhanced Bayesian analysis tool called the Integrated Knowledge Engine (IKE) for monitoring and surveillance. The enhancements are suited for Rapid Response Situations where decisions must be made based on uncertain and incomplete evidence from many diverse and heterogeneous sources. The enhancements extend the probabilistic results of the traditional Bayesian analysis by (1) better quantifying uncertainty arising from model parameter uncertainty and uncertain evidence, (2) optimizing the collection of evidence to reach conclusions more quickly, and (3) allowing the analyst to determine the influence of the remaining evidence that cannot be obtained in the time allowed. These extended features give the analyst and decision maker a better comprehension of the adequacy of the acquired evidence and hence the quality of the hurried decisions. They also describe two example systems where the above features are highlighted.
Energy Technology Data Exchange (ETDEWEB)
Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil); Pereira, Iraci Martinez; Silva, Antonio Teixeira e, E-mail: martinez@ipen.b, E-mail: teixeira@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2011-07-01
In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)
Applying waste logistics modeling to regional planning
Energy Technology Data Exchange (ETDEWEB)
Holter, G.M.; Khawaja, A.; Shaver, S.R.; Peterson, K.L.
1995-05-01
Waste logistics modeling is a powerful analytical technique that can be used for effective planning of future solid waste storage, treatment, and disposal activities. Proper waste management is essential for preventing unacceptable environmental degradation from ongoing operations, and is also a critical part of any environmental remediation activity. Logistics modeling allows for analysis of alternate scenarios for future waste flowrates and routings, facility schedules, and processing or handling capacities. Such analyses provide an increased understanding of the critical needs for waste storage, treatment, transport, and disposal while there is still adequate lead time to plan accordingly. They also provide a basis for determining the sensitivity of these critical needs to the various system parameters. This paper discusses the application of waste logistics modeling concepts to regional planning. In addition to ongoing efforts to aid in planning for a large industrial complex, the Pacific Northwest Laboratory (PNL) is currently involved in implementing waste logistics modeling as part of the planning process for material recovery and recycling within a multi-city region in the western US.
Object Oriented Modeling Of Social Networks
Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.
1996-01-01
The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the
Bayesian estimation of the network autocorrelation model
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
2017-01-01
The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Energy Technology Data Exchange (ETDEWEB)
Hwangbo, Soonho; Lee, In-Beum [POSTECH, Pohang (Korea, Republic of); Han, Jeehoon [University of Wisconsin-Madison, Madison (United States)
2014-10-15
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.
Applying the WEAP Model to Water Resource
DEFF Research Database (Denmark)
Gao, Jingjing; Christensen, Per; Li, Wei
in assessing the effects on water resources using a case study on a Coal Industry Development Plan in an arid region in North Western China. In the case the WEAP model (Water Evaluation And Planning System) were used to simulate various scenarios using a diversity of technological instruments like irrigation...... thus providing a good basis for an SEA that can support the choice among different alternative scenarios and contribute to adjusting and optimizing the original plan....
Applying Dataflow Analysis to Dimension Buffers for Guaranteed Performance in Networks on Chip
Hansson, A.; Hansson, Andreas; Wiggers, M.H.; Moonen, Arno; Goossens, Kees; Bekooij, Marco Jan Gerrit; Bekooij, Marco
A Network on Chip (NoC) with end-to-end flow control is modelled by a cyclo-static dataflow graph. Using the proposed model together with state-of-the-art dataflow analysis algorithms, we size the buffers in the network interfaces. We show, for a range of NoC designs, that buffer sizes are
Modeling data throughput on communication networks
Energy Technology Data Exchange (ETDEWEB)
Eldridge, J.M.
1993-11-01
New challenges in high performance computing and communications are driving the need for fast, geographically distributed networks. Applications such as modeling physical phenomena, interactive visualization, large data set transfers, and distributed supercomputing require high performance networking [St89][Ra92][Ca92]. One measure of a communication network`s performance is the time it takes to complete a task -- such as transferring a data file or displaying a graphics image on a remote monitor. Throughput, defined as the ratio of the number of useful data bits transmitted per the time required to transmit those bits, is a useful gauge of how well a communication system meets this performance measure. This paper develops and describes an analytical model of throughput. The model is a tool network designers can use to predict network throughput. It also provides insight into those parts of the network that act as a performance bottleneck.
Comparison and evaluation of network clustering algorithms applied to genetic interaction networks.
Hou, Lin; Wang, Lin; Berg, Arthur; Qian, Minping; Zhu, Yunping; Li, Fangting; Deng, Minghua
2012-01-01
The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.
Mathematical model of transmission network static state estimation
Directory of Open Access Journals (Sweden)
Ivanov Aleksandar
2012-01-01
Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.
Dependency structure matrix modelling for stakeholder value networks
Feng, Wen; Crawley, Edward F.; de Weck, Olivier L.; Keller, Rene; Robinson, Bob
2010-01-01
This paper develops a qualitative/quantitative network approach, namely a “Stakeholder Value Network”, to understand the impacts of both direct and indirect relationships between stakeholders on the success of large engineering projects. Specifically, this paper explores the feasibility and benefit of applying the Dependency Structure Matrix (DSM) as the modelling platform for Stakeholder Value Networks. Further, an efficient algorithm is designed for computing indirect stakeholder influence ...
Private healthcare quality: applying a SERVQUAL model.
Butt, Mohsin Muhammad; de Run, Ernest Cyril
2010-01-01
This paper seeks to develop and test the SERVQUAL model scale for measuring Malaysian private health service quality. The study consists of 340 randomly selected participants visiting a private healthcare facility during a three-month data collection period. Data were analyzed using means, correlations, principal component and confirmatory factor analysis to establish the modified SERVQUAL scale's reliability, underlying dimensionality and convergent, discriminant validity. Results indicate a moderate negative quality gap for overall Malaysian private healthcare service quality. Results also indicate a moderate negative quality gap on each service quality scale dimension. However, scale development analysis yielded excellent results, which can be used in wider healthcare policy and practice. Respondents were skewed towards a younger population, causing concern that the results might not represent all Malaysian age groups. The study's major contribution is that it offers a way to assess private healthcare service quality. Second, it successfully develops a scale that can be used to measure health service quality in Malaysian contexts.
Inferring gene regression networks with model trees
Directory of Open Access Journals (Sweden)
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Molecular modeling of amorphous, non-woven polymer networks.
Krausse, Constantin A; Milek, Theodor; Zahn, Dirk
2015-10-01
We outline a simple and efficient approach to generating molecular models of amorphous polymer networks. Similar to established techniques of preparing woven polymer networks from quenching high-temperature molecular simulation runs, we use a molecular dynamics simulations of a generic melt as starting points. This generic melt is however only used to describe parts of the polymers, namely the cross-linker units which positions are adopted from particle positions of the quenched melt. Specific degrees of network connectivity are tuned by geometric criteria for linker-linker connections and by suitable multi-body interaction potentials applied to the generic melt simulations. Using this technique we demonstrate adjusting fourfold linker coordination in amorphous polymer networks comprising 10-20% under-coordinated linkers. Graphical Abstract Molecular modeling of amorphous, non-woven polymer networks.
Modeling pedestrian's conformity violation behavior: a complex network based approach.
Zhou, Zhuping; Hu, Qizhou; Wang, Wei
2014-01-01
Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases.
A graph model for opportunistic network coding
Sorour, Sameh
2015-08-12
© 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.
Determining Application Runtimes Using Queueing Network Modeling
Energy Technology Data Exchange (ETDEWEB)
Elliott, Michael L. [Univ. of San Francisco, CA (United States)
2006-12-14
Determination of application times-to-solution for large-scale clustered computers continues to be a difficult problem in high-end computing, which will only become more challenging as multi-core consumer machines become more prevalent in the market. Both researchers and consumers of these multi-core systems desire reasonable estimates of how long their programs will take to run (time-to-solution, or TTS), and how many resources will be consumed in the execution. Currently there are few methods of determining these values, and those that do exist are either overly simplistic in their assumptions or require great amounts of effort to parameterize and understand. One previously untried method is queuing network modeling (QNM), which is easy to parameterize and solve, and produces results that typically fall within 10 to 30% of the actual TTS for our test cases. Using characteristics of the computer network (bandwidth, latency) and communication patterns (number of messages, message length, time spent in communication), the QNM model of the NAS-PB CG application was applied to MCR and ALC, supercomputers at LLNL, and the Keck Cluster at USF, with average errors of 2.41%, 3.61%, and -10.73%, respectively, compared to the actual TTS observed. While additional work is necessary to improve the predictive capabilities of QNM, current results show that QNM has a great deal of promise for determining application TTS for multi-processor computer systems.
Stochastic S-system modeling of gene regulatory network.
Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob
2015-10-01
Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.
ICA model order selection of task co-activation networks.
Ray, Kimberly L; McKay, D Reese; Fox, Peter M; Riedel, Michael C; Uecker, Angela M; Beckmann, Christian F; Smith, Stephen M; Fox, Peter T; Laird, Angela R
2013-01-01
Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.
Settings in Social Networks : a Measurement Model
Schweinberger, Michael; Snijders, Tom A.B.
2003-01-01
A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive
Settings in social networks : A measurement model
Schweinberger, M; Snijders, TAB
2003-01-01
A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive
Spinal Cord Injury Model System Information Network
... the UAB-SCIMS Contact the UAB-SCIMS UAB Spinal Cord Injury Model System Newly Injured Health Daily Living Consumer ... Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network ...
Radio Channel Modeling in Body Area Networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2009-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to de- tect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation
Radio channel modeling in body area networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2010-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to detect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation in
Network interconnections: an architectural reference model
Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.
1985-01-01
One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for
Performance modeling of network data services
Energy Technology Data Exchange (ETDEWEB)
Haynes, R.A.; Pierson, L.G.
1997-01-01
Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.
Multi-input Multi-output Beta Wavelet Network: Modeling of Acoustic Units for Speech Recognition
Chokri Ben Amar; Mourad Zaied; Ridha Ejbali
2012-01-01
In this paper, we propose a novel architecture of wavelet network called Multi-input Multi-output Wavelet Network MIMOWN as a generalization of the old architecture of wavelet network. This newel prototype was applied to speech recognition application especially to model acoustic unit of speech. The originality of our work is the proposal of MIMOWN to model acoustic unit of speech. This approach was proposed to overcome limitation of old wavelet network model. The use of the multi-input multi...
Learning Bayesian Network Model Structure from Data
National Research Council Canada - National Science Library
Margaritis, Dimitris
2003-01-01
In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...
NC truck network model development research.
2008-09-01
This research develops a validated prototype truck traffic network model for North Carolina. The model : includes all counties and metropolitan areas of North Carolina and major economic areas throughout the : U.S. Geographic boundaries, population a...
Modelling the structure of complex networks
DEFF Research Database (Denmark)
Herlau, Tue
networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...
A Network Formation Model Based on Subgraphs
Chandrasekhar, Arun
2016-01-01
We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.
Gossip spread in social network Models
Johansson, Tobias
2017-04-01
Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.
Synergistic effects in threshold models on networks
Juul, Jonas S.; Porter, Mason A.
2018-01-01
Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.
Learning to Apply Models of Materials While Explaining Their Properties
Karpin, Tiia; Juuti, Kalle; Lavonen, Jari
2014-01-01
Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…
Quebec mental health services networks: models and implementation
Directory of Open Access Journals (Sweden)
Marie-Josée Fleury
2005-06-01
Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.
Valente, Thomas W; Pitts, Stephanie R
2017-03-20
The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.
A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
A.J.G. da Cruz
1997-12-01
Full Text Available The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data
A Constructive Neural-Network Approach to Modeling Psychological Development
Shultz, Thomas R.
2012-01-01
This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…
Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
Energy Technology Data Exchange (ETDEWEB)
Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anad?n, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C. -M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D. A. Martinez; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; S?ldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.
2017-03-01
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
Characterization and Modeling of Network Traffic
DEFF Research Database (Denmark)
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur
2011-01-01
This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values....... The model investigates the traffic generation mechanisms, and grouping traffic into flows and applications....
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply....... The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases
2016-01-01
Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures. PMID:26824351
A network model of the interbank market
Li, Shouwei; He, Jianmin; Zhuang, Yaming
2010-12-01
This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.
Model for Microcirculation Transportation Network Design
Directory of Open Access Journals (Sweden)
Qun Chen
2012-01-01
Full Text Available The idea of microcirculation transportation was proposed to shunt heavy traffic on arterial roads through branch roads. The optimization model for designing micro-circulation transportation network was developed to pick out branch roads as traffic-shunting channels and determine their required capacity, trying to minimize the total reconstruction expense and land occupancy subject to saturation and reconstruction space constraints, while accounting for the route choice behaviour of network users. Since micro-circulation transportation network design problem includes both discrete and continuous variables, a discretization method was developed to convert two groups of variables (discrete variables and continuous variables into one group of new discrete variables, transforming the mixed network design problem into a new kind of discrete network design problem with multiple values. The genetic algorithm was proposed to solve the new discrete network design problem. Finally a numerical example demonstrated the efficiency of the model and algorithm.
Extending a configuration model to find communities in complex networks
Jin, Di; He, Dongxiao; Hu, Qinghua; Baquero, Carlos; Yang, Bo
2013-09-01
Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.
Modelling of virtual production networks
Directory of Open Access Journals (Sweden)
2011-03-01
Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.
Modeling Epidemics Spreading on Social Contact Networks.
Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua
2015-09-01
Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.
Random graph models for dynamic networks
Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.
2017-10-01
Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.
Modeling the interdependent network based on two-mode networks
An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian
2017-10-01
Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
The Private Lives of Minerals: Social Network Analysis Applied to Mineralogy and Petrology
Hazen, R. M.; Morrison, S. M.; Fox, P. A.; Golden, J. J.; Downs, R. T.; Eleish, A.; Prabhu, A.; Li, C.; Liu, C.
2016-12-01
Comprehensive databases of mineral species (rruff.info/ima) and their geographic localities and co-existing mineral assemblages (mindat.org) reveal patterns of mineral association and distribution that mimic social networks, as commonly applied to such varied topics as social media interactions, the spread of disease, terrorism networks, and research collaborations. Applying social network analysis (SNA) to common assemblages of rock-forming igneous and regional metamorphic mineral species, we find patterns of cohesion, segregation, density, and cliques that are similar to those of human social networks. These patterns highlight classic trends in lithologic evolution and are illustrated with sociograms, in which mineral species are the "nodes" and co-existing species form "links." Filters based on chemistry, age, structural group, and other parameters highlight visually both familiar and new aspects of mineralogy and petrology. We quantify sociograms with SNA metrics, including connectivity (based on the frequency of co-occurrence of mineral pairs), homophily (the extent to which co-existing mineral species share compositional and other characteristics), network closure (based on the degree of network interconnectivity), and segmentation (as revealed by isolated "cliques" of mineral species). Exploitation of large and growing mineral data resources with SNA offers promising avenues for discovering previously hidden trends in mineral diversity-distribution systematics, as well as providing new pedagogical approaches to teaching mineralogy and petrology.
An endogenous model of the credit network
He, Jianmin; Sui, Xin; Li, Shouwei
2016-01-01
In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.
Tensor network models of multiboundary wormholes
Peach, Alex; Ross, Simon F.
2017-05-01
We study the entanglement structure of states dual to multiboundary wormhole geometries using tensor network models. Perfect and random tensor networks tiling the hyperbolic plane have been shown to provide good models of the entanglement structure in holography. We extend this by quotienting the plane by discrete isometries to obtain models of the multiboundary states. We show that there are networks where the entanglement structure is purely bipartite, extending results obtained in the large temperature limit. We analyse the entanglement structure in a range of examples.
WDM Systems and Networks Modeling, Simulation, Design and Engineering
Ellinas, Georgios; Roudas, Ioannis
2012-01-01
WDM Systems and Networks: Modeling, Simulation, Design and Engineering provides readers with the basic skills, concepts, and design techniques used to begin design and engineering of optical communication systems and networks at various layers. The latest semi-analytical system simulation techniques are applied to optical WDM systems and networks, and a review of the various current areas of optical communications is presented. Simulation is mixed with experimental verification and engineering to present the industry as well as state-of-the-art research. This contributed volume is divided into three parts, accommodating different readers interested in various types of networks and applications. The first part of the book presents modeling approaches and simulation tools mainly for the physical layer including transmission effects, devices, subsystems, and systems), whereas the second part features more engineering/design issues for various types of optical systems including ULH, access, and in-building system...
A source-controlled data center network model.
Directory of Open Access Journals (Sweden)
Yang Yu
Full Text Available The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1 The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2 Vector switches (VS developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3 The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4 We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.
A source-controlled data center network model.
Yu, Yang; Liang, Mangui; Wang, Zhe
2017-01-01
The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.
A source-controlled data center network model
Yu, Yang; Liang, Mangui; Wang, Zhe
2017-01-01
The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS. PMID:28328925
Designing Network-based Business Model Ontology
DEFF Research Database (Denmark)
Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz
2015-01-01
Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....
Queueing Models for Mobile Ad Hoc Networks
de Haan, Roland
2009-01-01
This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of
Modelling traffic congestion using queuing networks
Indian Academy of Sciences (India)
Traffic Flow-Density diagrams are obtained using simple Jackson queuing network analysis. Such simple analytical models can be used to capture the effect of non- homogenous traffic. Keywords. Flow-density curves; uninterrupted traffic; Jackson networks. 1. Introduction. Traffic management has become very essential in ...
Miao, Yongwu; Sloep, Peter; Hummel, Hans; Koper, Rob
2009-01-01
Miao, Y., Sloep, P. B., Hummel, H., & Koper, R. (2009). Improving the Unreliability of Competence Information: an Argumentation to Apply Information Fusion in Learning Networks [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), 19(4/5/6),
Albert, Cécile H; Rayfield, Bronwyn; Dumitru, Maria; Gonzalez, Andrew
2017-12-01
Designing connected landscapes is among the most widespread strategies for achieving biodiversity conservation targets. The challenge lies in simultaneously satisfying the connectivity needs of multiple species at multiple spatial scales under uncertain climate and land-use change. To evaluate the contribution of remnant habitat fragments to the connectivity of regional habitat networks, we developed a method to integrate uncertainty in climate and land-use change projections with the latest developments in network-connectivity research and spatial, multipurpose conservation prioritization. We used land-use change simulations to explore robustness of species' habitat networks to alternative development scenarios. We applied our method to 14 vertebrate focal species of periurban Montreal, Canada. Accounting for connectivity in spatial prioritization strongly modified conservation priorities and the modified priorities were robust to uncertain climate change. Setting conservation priorities based on habitat quality and connectivity maintained a large proportion of the region's connectivity, despite anticipated habitat loss due to climate and land-use change. The application of connectivity criteria alongside habitat-quality criteria for protected-area design was efficient with respect to the amount of area that needs protection and did not necessarily amplify trade-offs among conservation criteria. Our approach and results are being applied in and around Montreal and are well suited to the design of ecological networks and green infrastructure for the conservation of biodiversity and ecosystem services in other regions, in particular regions around large cities, where connectivity is critically low. © 2017 Society for Conservation Biology.
Ant colony optimization and neural networks applied to nuclear power plant monitoring
Energy Technology Data Exchange (ETDEWEB)
Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2015-07-01
A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)
Classification of brain compartments and head injury lesions by neural networks applied to MRI
Energy Technology Data Exchange (ETDEWEB)
Kischell, E.R. [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Kehtarnavaz, N. [Dept. of Electrical Engineering, Texas A and M Univ., College Station, TX (United States); Hillman, G.R. [Dept. of Pharmacology, Univ. of Texas Medical Branch, Galveston, TX (United States); Levin, H. [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Lilly, M. [Dept. of Neurosurgery, Univ. of Texas Medical Branch, Galveston, TX (United States); Kent, T.A. [Dept. of Neurology and Psychiatry, Univ. of Texas Medical Branch, Galveston, TX (United States)
1995-10-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and `unknown`. A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classifications of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician`s report used to train the neural network. (orig.)
Classification of brain compartments and head injury lesions by neural networks applied to MRI.
Kischell, E R; Kehtarnavaz, N; Hillman, G R; Levin, H; Lilly, M; Kent, T A
1995-10-01
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images. The classes of interest were background, gray matter, white matter, cerebrospinal fluid, macrocystic encephalomalacia, gliosis, and "unknown." A comprehensive feature vector was chosen to discriminate these classes. The BPN combined with feature conditioning, multiple discriminant analysis followed by Hotelling transform, produced the most accurate and consistent classification results. Classification of normal brain compartments were generally in agreement with expert interpretation of the images. Macrocystic encephalomalacia and gliosis were recognized and, except around the periphery, classified in agreement with the clinician's report used to train the neural network.
Using consensus bayesian network to model the reactive oxygen species regulatory pathway.
Directory of Open Access Journals (Sweden)
Liangdong Hu
Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.
Using consensus bayesian network to model the reactive oxygen species regulatory pathway.
Hu, Liangdong; Wang, Limin
2013-01-01
Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.
Integrating network ecology with applied conservation: a synthesis and guide to implementation.
Kaiser-Bunbury, Christopher N; Blüthgen, Nico
2015-07-10
Ecological networks are a useful tool to study the complexity of biotic interactions at a community level. Advances in the understanding of network patterns encourage the application of a network approach in other disciplines than theoretical ecology, such as biodiversity conservation. So far, however, practical applications have been meagre. Here we present a framework for network analysis to be harnessed to advance conservation management by using plant-pollinator networks and islands as model systems. Conservation practitioners require indicators to monitor and assess management effectiveness and validate overall conservation goals. By distinguishing between two network attributes, the 'diversity' and 'distribution' of interactions, on three hierarchical levels (species, guild/group and network) we identify seven quantitative metrics to describe changes in network patterns that have implications for conservation. Diversity metrics are partner diversity, vulnerability/generality, interaction diversity and interaction evenness, and distribution metrics are the specialization indices d' and [Formula: see text] and modularity. Distribution metrics account for sampling bias and may therefore be suitable indicators to detect human-induced changes to plant-pollinator communities, thus indirectly assessing the structural and functional robustness and integrity of ecosystems. We propose an implementation pathway that outlines the stages that are required to successfully embed a network approach in biodiversity conservation. Most importantly, only if conservation action and study design are aligned by practitioners and ecologists through joint experiments, are the findings of a conservation network approach equally beneficial for advancing adaptive management and ecological network theory. We list potential obstacles to the framework, highlight the shortfall in empirical, mostly experimental, network data and discuss possible solutions. Published by Oxford University
Liu, Zugang
Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New
A mixture copula Bayesian network model for multimodal genomic data
Directory of Open Access Journals (Sweden)
Qingyang Zhang
2017-04-01
Full Text Available Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation–maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.
A mixture copula Bayesian network model for multimodal genomic data.
Zhang, Qingyang; Shi, Xuan
2017-01-01
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation-maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.
Non-consensus Opinion Models on Complex Networks
Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo
2013-04-01
only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erdős Rényi networks.
Nonlinear Eddy Viscosity Models applied to Wind Turbine Wakes
DEFF Research Database (Denmark)
Laan, van der, Paul Maarten; Sørensen, Niels N.; Réthoré, Pierre-Elouan
2013-01-01
The linear k−ε eddy viscosity model and modified versions of two existing nonlinear eddy viscosity models are applied to single wind turbine wake simulations using a Reynolds Averaged Navier-Stokes code. Results are compared with field wake measurements. The nonlinear models give better results...
Apply Functional Modelling to Consequence Analysis in Supervision Systems
DEFF Research Database (Denmark)
Zhang, Xinxin; Lind, Morten; Gola, Giulio
2013-01-01
This paper will first present the purpose and goals of applying functional modelling approach to consequence analysis by adopting Multilevel Flow Modelling (MFM). MFM Models describe a complex system in multiple abstraction levels in both means-end dimension and whole-part dimension. It contains...
Teaching students to apply multiple physical modeling methods
Wiegers, T.; Verlinden, J.C.; Vergeest, J.S.M.
2014-01-01
Design students should be able to explore a variety of shapes before elaborating one particular shape. Current modelling courses don’t address this issue. We developed the course Rapid Modelling, which teaches students to explore multiple shape models in a short time, applying different methods and
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Modeling trust context in networks
Adali, Sibel
2013-01-01
We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Applied Bounded Model Checking for Interlocking System Designs
DEFF Research Database (Denmark)
Haxthausen, Anne Elisabeth; Peleska, Jan; Pinger, Ralf
2014-01-01
of behavioural (operational) semantics. The former checks that the plant model – that is, the software components reflecting the physical components of the interlocking system – has been set up in an adequate way. The latter investigates trains moving through the network, with the objective to uncover potential...
A Markov model for evaluating resource sharing policies for DASH assisting network elements
J.W.M. Kleinrouweler (Jan Willem); S. Cabrero Barros (Sergio); R.D. van der Mei (Rob); P.S. Cesar Garcia (Pablo Santiago)
2016-01-01
textabstractIn this paper, we present a model for evaluating bandwidth sharing policies, that can be applied to networks that handle both video streaming traffic, as well as other traffic. Video streaming is a demanding network application. In crowded networks, resources need to be properly divided
A Markov model for evaluating resource sharing policies for DASH assisting network elements
J.W.M. Kleinrouweler (Jan Willem); S. Cabrero Barros (Sergio); R.D. van der Mei (Rob); P.S. Cesar Garcia (Pablo Santiago)
2017-01-01
textabstractIn this paper, we present a model for evaluating bandwidth sharing policies, that can be applied to networks that handle both video streaming traffic, as well as other traffic. Video streaming is a demanding network application. In crowded networks, resources need to be properly divided
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Gene Regulation Networks for Modeling Drosophila Development
Mjolsness, E.
1999-01-01
This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.
Graphical Model Theory for Wireless Sensor Networks
Energy Technology Data Exchange (ETDEWEB)
Davis, William B.
2002-12-08
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
Mitigating risk during strategic supply network modeling
Müssigmann, Nikolaus
2006-01-01
Mitigating risk during strategic supply network modeling. - In: Managing risks in supply chains / ed. by Wolfgang Kersten ... - Berlin : Schmidt, 2006. - S. 213-226. - (Operations and technology management ; 1)
Onset to First Alcohol Use in Early Adolescence : A Network Diffusion Model
Light, John M.; Greenan, Charlotte C.; Rusby, Julie C.; Nies, Kimberley M.; Snijders, Tom A. B.
A novel version of Snijders's stochastic actor-based modeling (SABM) framework is applied to model the diffusion of first alcohol use through middle school-wide longitudinal networks of early adolescents, aged approximately 11-14years. Models couple a standard SABM for friendship network evolution
Road maintenance planning using network flow modelling
Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John
2015-01-01
This paper presents a road maintenance planning model that can be used to balance out maintenance cost and road user cost, since performing road maintenance at night can be convenient for road users but costly for highway agency. Based on the platform of the network traffic flow modelling, the traffic through the worksite and its adjacent road links is evaluated. Thus, maintenance arrangements at a worksite can be optimized considering the overall network performance. In addition, genetic alg...
A model reduction method for biochemical reaction networks.
Rao, Shodhan; van der Schaft, Arjan; van Eunen, Karen; Bakker, Barbara M; Jayawardhana, Bayu
2014-05-03
In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse
Comparison of two multiaxial fatigue models applied to dental implants
Directory of Open Access Journals (Sweden)
JM. Ayllon
2015-07-01
Full Text Available This paper presents two multiaxial fatigue life prediction models applied to a commercial dental implant. One model is called Variable Initiation Length Model and takes into account both the crack initiation and propagation phases. The second model combines the Theory of Critical Distance with a critical plane damage model to characterise the initiation and initial propagation of micro/meso cracks in the material. This paper discusses which material properties are necessary for the implementation of these models and how to obtain them in the laboratory from simple test specimens. It also describes the FE models developed for the stress/strain and stress intensity factor characterisation in the implant. The results of applying both life prediction models are compared with experimental results arising from the application of ISO-14801 standard to a commercial dental implant.
Applying artificial vision models to human scene understanding.
Aminoff, Elissa M; Toneva, Mariya; Shrivastava, Abhinav; Chen, Xinlei; Misra, Ishan; Gupta, Abhinav; Tarr, Michael J
2015-01-01
How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective-the parahippocampal/lingual region (PPA), the retrosplenial complex (RSC), and the occipital place area (TOS)-have typically focused on single visual dimensions (e.g., size), rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: (1) BOLD activity in scene-selective brain regions; (2) behavioral measured judgments of visually-perceived scene similarity; and (3) several different computer vision models. These correlations revealed: (1) models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; (2) NEIL and SUN-the models that best accounted for the patterns obtained from PPA and TOS-were different from the GIST model that best accounted for the pattern obtained from RSC; (3) The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method-NEIL ("Never-Ending-Image-Learner"), which incorporates visual features learned as statistical regularities across web-scale numbers of scenes-showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.
Applying artificial vision models to human scene understanding
Directory of Open Access Journals (Sweden)
Elissa Michele Aminoff
2015-02-01
Full Text Available How do we understand the complex patterns of neural responses that underlie scene understanding? Studies of the network of brain regions held to be scene-selective – the parahippocampal/lingual region (PPA, the retrosplenial complex (RSC, and the occipital place area (TOS – have typically focused on single visual dimensions (e.g., size, rather than the high-dimensional feature space in which scenes are likely to be neurally represented. Here we leverage well-specified artificial vision systems to explicate a more complex understanding of how scenes are encoded in this functional network. We correlated similarity matrices within three different scene-spaces arising from: 1 BOLD activity in scene-selective brain regions; 2 behavioral measured judgments of visually-perceived scene similarity; and 3 several different computer vision models. These correlations revealed: 1 models that relied on mid- and high-level scene attributes showed the highest correlations with the patterns of neural activity within the scene-selective network; 2 NEIL and SUN – the models that best accounted for the patterns obtained from PPA and TOS – were different from the GIST model that best accounted for the pattern obtained from RSC; 3 The best performing models outperformed behaviorally-measured judgments of scene similarity in accounting for neural data. One computer vision method – NEIL (Never-Ending-Image-Learner, which incorporates visual features learned as statistical regularities across web-scale numbers of scenes – showed significant correlations with neural activity in all three scene-selective regions and was one of the two models best able to account for variance in the PPA and TOS. We suggest that these results are a promising first step in explicating more fine-grained models of neural scene understanding, including developing a clearer picture of the division of labor among the components of the functional scene-selective brain network.
[Applying a social network for the practice and learning of psychiatry].
Mondin, Estefanía; Matusevich, Daniel
2014-01-01
Social networking is a virtual space in which people relate and build their identity, share information, publish content and intervene on the content posted by others. We will describe an experiment carried out in the psychiatry service of Italian Hospital in Buenos Aires, in which we use Whatsapp Social Network applied to the development of clinical work and teaching task. From these new ways of relating between professional, emerge a new way to work, participate in groups or try to evaluate various options for dealing with a patient. We analyze the usefulness of this virtual platform as a working tool.
A simple model for studying interacting networks
Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.
2011-03-01
Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.
Modeling gene regulatory network motifs using Statecharts.
Fioravanti, Fabio; Helmer-Citterich, Manuela; Nardelli, Enrico
2012-03-28
Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks.For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal.We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.
A quantum-implementable neural network model
Chen, Jialin; Wang, Lingli; Charbon, Edoardo
2017-10-01
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
Telestroke network business model strategies.
Fanale, Christopher V; Demaerschalk, Bart M
2012-10-01
Our objective is to summarize the evidence that supports the reliability of telemedicine for diagnosis and efficacy in acute stroke treatment, identify strategies for funding the development of a telestroke network, and to present issues with respect to economic sustainability, cost effectiveness, and the status of reimbursement for telestroke. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Modeling and Simulation of Handover Scheme in Integrated EPON-WiMAX Networks
DEFF Research Database (Denmark)
Yan, Ying; Dittmann, Lars
2011-01-01
In this paper, we tackle the seamless handover problem in integrated optical wireless networks. Our model applies for the convergence network of EPON and WiMAX and a mobilityaware signaling protocol is proposed. The proposed handover scheme, Integrated Mobility Management Scheme (IMMS), is assisted...... by enhancing the traditional MPCP signaling protocol, which cooperatively collects mobility information from the front-end wireless network and makes centralized bandwidth allocation decisions in the backhaul optical network. The integrated network architecture and the joint handover scheme are simulated using...... OPNET modeler. Results show validation of the protocol, i.e., integrated handover scheme gains better network performances....
Applying Object Oriented Bayesian Networks to Large Medical Decision Support Systems
DEFF Research Database (Denmark)
Bangsø, Olav; Olesen, Kristian Grønborg
2003-01-01
in the construction of such models. The other application is the MUNIN system for diagnosis of perioheral muscle and nerve diseases, that is characterized by a number of (almost) identical anatomical structures. The modeling of such structures benefit drom inheritance properties of object oriented Bayesian networks...
Complex networks under dynamic repair model
Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao
2018-01-01
Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.
Markov State Models of gene regulatory networks.
Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L
2017-02-06
Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
Modeling acquaintance networks based on balance theory
Directory of Open Access Journals (Sweden)
Vukašinović Vida
2014-09-01
Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models
CIME course on Modelling and Optimisation of Flows on Networks
Ambrosio, Luigi; Helbing, Dirk; Klar, Axel; Zuazua, Enrique
2013-01-01
In recent years flows in networks have attracted the interest of many researchers from different areas, e.g. applied mathematicians, engineers, physicists, economists. The main reason for this ubiquity is the wide and diverse range of applications, such as vehicular traffic, supply chains, blood flow, irrigation channels, data networks and others. This book presents an extensive set of notes by world leaders on the main mathematical techniques used to address such problems, together with investigations into specific applications. The main focus is on partial differential equations in networks, but ordinary differential equations and optimal transport are also included. Moreover, the modeling is completed by analysis, numerics, control and optimization of flows in networks. The book will be a valuable resource for every researcher or student interested in the subject.
Büttner, Kathrin; Salau, Jennifer; Krieter, Joachim
2016-01-01
The average topological overlap of two graphs of two consecutive time steps measures the amount of changes in the edge configuration between the two snapshots. This value has to be zero if the edge configuration changes completely and one if the two consecutive graphs are identical. Current methods depend on the number of nodes in the network or on the maximal number of connected nodes in the consecutive time steps. In the first case, this methodology breaks down if there are nodes with no edges. In the second case, it fails if the maximal number of active nodes is larger than the maximal number of connected nodes. In the following, an adaption of the calculation of the temporal correlation coefficient and of the topological overlap of the graph between two consecutive time steps is presented, which shows the expected behaviour mentioned above. The newly proposed adaption uses the maximal number of active nodes, i.e. the number of nodes with at least one edge, for the calculation of the topological overlap. The three methods were compared with the help of vivid example networks to reveal the differences between the proposed notations. Furthermore, these three calculation methods were applied to a real-world network of animal movements in order to detect influences of the network structure on the outcome of the different methods.
The HPT Model Applied to a Kayak Company's Registration Process
Martin, Florence; Hall, Herman A., IV; Blakely, Amanda; Gayford, Matthew C.; Gunter, Erin
2009-01-01
This case study describes the step-by-step application of the traditional human performance technology (HPT) model at a premier kayak company located on the coast of North Carolina. The HPT model was applied to address lost revenues related to three specific business issues: misinformed customers, dissatisfied customers, and guides not showing up…
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Network motif identification and structure detection with exponential random graph models
Directory of Open Access Journals (Sweden)
Munni Begum
2014-12-01
Full Text Available Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p* models, also known as Exponential Random Graph Models (ERGMs, to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network and k-istar (or incoming star structures, for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models.
Computer-Supported Modelling of Multi modal Transportation Networks Rationalization
Directory of Open Access Journals (Sweden)
Ratko Zelenika
2007-09-01
Full Text Available This paper deals with issues of shaping and functioning ofcomputer programs in the modelling and solving of multimoda Itransportation network problems. A methodology of an integrateduse of a programming language for mathematical modellingis defined, as well as spreadsheets for the solving of complexmultimodal transportation network problems. The papercontains a comparison of the partial and integral methods ofsolving multimodal transportation networks. The basic hypothesisset forth in this paper is that the integral method results inbetter multimodal transportation network rationalization effects,whereas a multimodal transportation network modelbased on the integral method, once built, can be used as the basisfor all kinds of transportation problems within multimodaltransport. As opposed to linear transport problems, multimodaltransport network can assume very complex shapes. This papercontains a comparison of the partial and integral approach totransp01tation network solving. In the partial approach, astraightforward model of a transp01tation network, which canbe solved through the use of the Solver computer tool within theExcel spreadsheet inteiface, is quite sufficient. In the solving ofa multimodal transportation problem through the integralmethod, it is necessmy to apply sophisticated mathematicalmodelling programming languages which supp01t the use ofcomplex matrix functions and the processing of a vast amountof variables and limitations. The LINGO programming languageis more abstract than the Excel spreadsheet, and it requiresa certain programming knowledge. The definition andpresentation of a problem logic within Excel, in a manner whichis acceptable to computer software, is an ideal basis for modellingin the LINGO programming language, as well as a fasterand more effective implementation of the mathematical model.This paper provides proof for the fact that it is more rational tosolve the problem of multimodal transportation networks by
A Very Large Area Network (VLAN) knowledge-base applied to space communication problems
Zander, Carol S.
1988-01-01
This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.
Dynamical complex network theory applied to the therapeutics of brain malignancies
Meyer-Bäse, Anke; Fratte, Daniel; Barbu, Adrian; Pinker-Domenig, Katja
2015-05-01
An important problem in modern therapeutics at the metabolomic, transcriptomic or phosphoproteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical frameworks provides us with the minimal number of "driver nodes" necessary to determine the full control over the obtained graph network in order to provide a change in the network's dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.
LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD
Energy Technology Data Exchange (ETDEWEB)
VERSPOOR, KARIN [Los Alamos National Laboratory; LIN, SHOU-DE [Los Alamos National Laboratory
2007-01-29
An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learned without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.
Modeling in applied sciences a kinetic theory approach
Pulvirenti, Mario
2000-01-01
Modeling complex biological, chemical, and physical systems, in the context of spatially heterogeneous mediums, is a challenging task for scientists and engineers using traditional methods of analysis Modeling in Applied Sciences is a comprehensive survey of modeling large systems using kinetic equations, and in particular the Boltzmann equation and its generalizations An interdisciplinary group of leading authorities carefully develop the foundations of kinetic models and discuss the connections and interactions between model theories, qualitative and computational analysis and real-world applications This book provides a thoroughly accessible and lucid overview of the different aspects, models, computations, and methodology for the kinetic-theory modeling process Topics and Features * Integrated modeling perspective utilized in all chapters * Fluid dynamics of reacting gases * Self-contained introduction to kinetic models * Becker–Doring equations * Nonlinear kinetic models with chemical reactions * Kinet...
Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems.
Etxaniz, Josu; Aranguren, Gerardo
2017-04-30
Even though home automation is a well-known research and development area, recent technological improvements in different areas such as context recognition, sensing, wireless communications or embedded systems have boosted wireless smart homes. This paper focuses on some of those areas related to home automation. The paper draws attention to wireless communications issues on embedded systems. Specifically, the paper discusses the multi-hop networking together with Bluetooth technology and latency, as a quality of service (QoS) metric. Bluetooth is a worldwide standard that provides low power multi-hop networking. It is a radio license free technology and establishes point-to-point and point-to-multipoint links, known as piconets, or multi-hop networks, known as scatternets. This way, many Bluetooth nodes can be interconnected to deploy ambient intelligent networks. This paper introduces the research on multi-hop latency done with park and sniff low power modes of Bluetooth over the test platform developed. Besides, an empirical model is obtained to calculate the latency of Bluetooth multi-hop communications over asynchronous links when links in scatternets are always in sniff or the park mode. Smart home devices and networks designers would take advantage of the models and the estimation of the delay they provide in communications along Bluetooth multi-hop networks.
Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
Directory of Open Access Journals (Sweden)
Josu Etxaniz
2017-04-01
Full Text Available Even though home automation is a well-known research and development area, recent technological improvements in different areas such as context recognition, sensing, wireless communications or embedded systems have boosted wireless smart homes. This paper focuses on some of those areas related to home automation. The paper draws attention to wireless communications issues on embedded systems. Specifically, the paper discusses the multi-hop networking together with Bluetooth technology and latency, as a quality of service (QoS metric. Bluetooth is a worldwide standard that provides low power multi-hop networking. It is a radio license free technology and establishes point-to-point and point-to-multipoint links, known as piconets, or multi-hop networks, known as scatternets. This way, many Bluetooth nodes can be interconnected to deploy ambient intelligent networks. This paper introduces the research on multi-hop latency done with park and sniff low power modes of Bluetooth over the test platform developed. Besides, an empirical model is obtained to calculate the latency of Bluetooth multi-hop communications over asynchronous links when links in scatternets are always in sniff or the park mode. Smart home devices and networks designers would take advantage of the models and the estimation of the delay they provide in communications along Bluetooth multi-hop networks.
Bluetooth Low Power Modes Applied to the Data Transportation Network in Home Automation Systems
Etxaniz, Josu; Aranguren, Gerardo
2017-01-01
Even though home automation is a well-known research and development area, recent technological improvements in different areas such as context recognition, sensing, wireless communications or embedded systems have boosted wireless smart homes. This paper focuses on some of those areas related to home automation. The paper draws attention to wireless communications issues on embedded systems. Specifically, the paper discusses the multi-hop networking together with Bluetooth technology and latency, as a quality of service (QoS) metric. Bluetooth is a worldwide standard that provides low power multi-hop networking. It is a radio license free technology and establishes point-to-point and point-to-multipoint links, known as piconets, or multi-hop networks, known as scatternets. This way, many Bluetooth nodes can be interconnected to deploy ambient intelligent networks. This paper introduces the research on multi-hop latency done with park and sniff low power modes of Bluetooth over the test platform developed. Besides, an empirical model is obtained to calculate the latency of Bluetooth multi-hop communications over asynchronous links when links in scatternets are always in sniff or the park mode. Smart home devices and networks designers would take advantage of the models and the estimation of the delay they provide in communications along Bluetooth multi-hop networks. PMID:28468294
An Intuitive Dominant Test Algorithm of CP-nets Applied on Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Liu Zhaowei
2014-07-01
Full Text Available A wireless sensor network is of spatially distributed with autonomous sensors, just like a multi-Agent system with single Agent. Conditional Preference networks is a qualitative tool for representing ceteris paribus (all other things being equal preference statements, it has been a research hotspot in artificial intelligence recently. But the algorithm and complexity of strong dominant test with respect to binary-valued structure CP-nets have not been solved, and few researchers address the application to other domain. In this paper, strong dominant test and application of CP-nets are studied in detail. Firstly, by constructing induced graph of CP-nets and studying its properties, we make a conclusion that the problem of strong dominant test on binary-valued CP-nets is single source shortest path problem essentially, so strong dominant test problem can be solved by improved Dijkstra’s algorithm. Secondly, we apply the algorithm above mentioned to the completeness of wireless sensor network, and design a completeness judging algorithm based on strong dominant test. Thirdly, we apply the algorithm on wireless sensor network to solve routing problem. In the end, we point out some interesting work in the future.
de la Mata, Tamara; Llano, Carlos
2013-07-01
Recent literature on border effect has fostered research on informal barriers to trade and the role played by network dependencies. In relation to social networks, it has been shown that intensity of trade in goods is positively correlated with migration flows between pairs of countries/regions. In this article, we investigate whether such a relation also holds for interregional trade of services. We also consider whether interregional trade flows in services linked with tourism exhibit spatial and/or social network dependence. Conventional empirical gravity models assume the magnitude of bilateral flows between regions is independent of flows to/from regions located nearby in space, or flows to/from regions related through social/cultural/ethic network connections. With this aim, we provide estimates from a set of gravity models showing evidence of statistically significant spatial and network (demographic) dependence in the bilateral flows of the trade of services considered. The analysis has been applied to the Spanish intra- and interregional monetary flows of services from the accommodation, restaurants and travel agencies for the period 2000-2009, using alternative datasets for the migration stocks and definitions of network effects.
Applied Knowledge Management to Mitigate Cognitive Load in Network-Enabled Mission Command
2017-11-22
system performance is often referred to as the “brittleness problem of automata” (Hollnagel and Woods 2005). These cautions from the flight management ... perspectives . IEEE Int Sys. 2006;21(4):70–73. Leistner F. Managing organizational knowledge flow: how to make knowledge sharing work. Hoboken (NJ...ARL-TN-0859 ● NOV 2017 US Army Research Laboratory Applied Knowledge Management to Mitigate Cognitive Load in Network-Enabled
Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE
Energy Technology Data Exchange (ETDEWEB)
Correa, R. [Universidad Tecnologica Metropolitana, Departamento de Fisica, Av. Jose Pedro Alessandri 1242, Nunoa, Santiago (Chile)]. E-mail: rcorrea@utem.cl; Chesta, M.A. [Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia y Fisica, Medina Allende s/n Ciudad Universitaria, 5000 Cordoba (Argentina)]. E-mail: chesta@famaf.unc.edu.ar; Morales, J.R. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: rmorales@uchile.cl; Dinator, M.I. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: mdinator@uchile.cl; Requena, I. [Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Artificial, Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Vila, I. [Universidad de Chile, Facultad de Ciencias, Departamento de Ecologia, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: limnolog@uchile.cl
2006-08-15
An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.
2016-12-07
and Afghanistan in newly relevant operating environments is to invite failure. In today’s globalized world , clear dividing lines between...PRISM 6, no. 3 FEATURES | 49 Regional Understanding and Unity of Effort Applying the Global SOF Network in Future Operating Environments ...the challenges and social diversity characteristic of today’s global operating environment . In con- trast, FAOs often lack tactical experience rela
Modelling complex networks by random hierarchical graphs
Directory of Open Access Journals (Sweden)
M.Wróbel
2008-06-01
Full Text Available Numerous complex networks contain special patterns, called network motifs. These are specific subgraphs, which occur oftener than in randomized networks of Erdős-Rényi type. We choose one of them, the triangle, and build a family of random hierarchical graphs, being Sierpiński gasket-based graphs with random "decorations". We calculate the important characteristics of these graphs - average degree, average shortest path length, small-world graph family characteristics. They depend on probability of decorations. We analyze the Ising model on our graphs and describe its critical properties using a renormalization-group technique.
A Network Model of Credit Risk Contagion
Directory of Open Access Journals (Sweden)
Ting-Qiang Chen
2012-01-01
Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.
Deep space network software cost estimation model
Tausworthe, R. C.
1981-01-01
A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.
Continuum Modeling of Biological Network Formation
Albi, Giacomo
2017-04-10
We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.
Stochastic modeling and analysis of telecoms networks
Decreusefond, Laurent
2012-01-01
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an
Neural networks as models of psychopathology.
Aakerlund, L; Hemmingsen, R
1998-04-01
Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.
Decomposed Implicit Models of Piecewise - Linear Networks
Directory of Open Access Journals (Sweden)
J. Brzobohaty
1992-05-01
Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.
Network modelling of fluid retention behaviour in unsaturated soils
Directory of Open Access Journals (Sweden)
Athanasiadis Ignatios
2016-01-01
Full Text Available The paper describes discrete modelling of the retention behaviour of unsaturated porous materials. A network approach is used within a statistical volume element (SVE, suitable for subsequent use in hydro-mechanical analysis and incorporation within multi-scale numerical modelling. The soil pore structure is modelled by a network of cylindrical pipes connecting spheres, with the spheres representing soil voids and the pipes representing inter-connecting throats. The locations of pipes and spheres are determined by a Voronoi tessellation of the domain. Original aspects of the modelling include a form of periodic boundary condition implementation applied for the first time to this type of network, a new pore volume scaling technique to provide more realistic modelling and a new procedure for initiating drying or wetting paths in a network model employing periodic boundary conditions. Model simulations, employing two linear cumulative probability distributions to represent the distributions of sphere and pipe radii, are presented for the retention behaviour reported from a mercury porosimetry test on a sandstone.
Green Network Planning Model for Optical Backbones
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael
2010-01-01
on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define...
Empirical generalization assessment of neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1995-01-01
This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...
Evaluation of EOR Processes Using Network Models
DEFF Research Database (Denmark)
Larsen, Jens Kjell; Krogsbøll, Anette
1998-01-01
The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels...
Learning to apply models of materials while explaining their properties
Karpin, Tiia; Juuti, Kalle; Lavonen, Jari
2014-09-01
Background:Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose:This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials. Sample:An experimental group is 27 Finnish upper secondary school students and control group included 18 students from the same school. Design and methods:In quasi-experimental setting, students were guided through predict, observe, explain activities in four practical work situations. It was intended that the structural models would encourage students to learn how to identify and apply appropriate models when predicting and explaining situations. The lessons, organised over a one-week period, began with a teacher's demonstration and continued with student experiments in which they described the properties and behaviours of six household products representing three different materials. Results:Most students in the experimental group learned to apply the models correctly, as demonstrated by post-test scores that were significantly higher than pre-test scores. The control group showed no significant difference between pre- and post-test scores. Conclusions:The findings indicate that the intervention where students engage in predict, observe, explain activities while several materials and models are confronted at the same time, had a positive effect on learning outcomes.
Applying a realistic evaluation model to occupational safety interventions
DEFF Research Database (Denmark)
Pedersen, Louise Møller
2018-01-01
Background: Recent literature characterizes occupational safety interventions as complex social activities, applied in complex and dynamic social systems. Hence, the actual outcomes of an intervention will vary, depending on the intervention, the implementation process, context, personal...... and qualitative methods. This revised model has, however, not been applied in a real life context. Method: The model is applied in a controlled, four-component, integrated behaviour-based and safety culture-based safety intervention study (2008-2010) in a medium-sized wood manufacturing company. The interventions...... involve the company’s safety committee, safety manager, safety groups and 130 workers. Results: The model provides a framework for more valid evidence of what works within injury prevention. Affective commitment and role behaviour among key actors are identified as crucial for the implementation...
Phenomenological network models: Lessons for epilepsy surgery.
Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans
2017-10-01
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.
Directory of Open Access Journals (Sweden)
Derric B. Jacobs
2017-03-01
Full Text Available Resilient communities promote trust, have well-developed networks, and can adapt to change. For rural communities in fire-prone landscapes, current resilience strategies may prove insufficient in light of increasing wildfire risks due to climate change. It is argued that, given the complexity of climate change, adaptations are best addressed at local levels where specific social, cultural, political, and economic conditions are matched with local risks and opportunities. Despite the importance of social networks as key attributes of community resilience, research using social network analysis on coupled human and natural systems is scarce. Furthermore, the extent to which local communities in fire-prone areas understand climate change risks, accept the likelihood of potential changes, and have the capacity to develop collaborative mitigation strategies is underexamined, yet these factors are imperative to community resiliency. We apply a social network framework to examine information networks that affect perceptions of wildfire and climate change in Central Oregon. Data were collected using a mailed questionnaire. Analysis focused on the residents' information networks that are used to gain awareness of governmental activities and measures of community social capital. A two-mode network analysis was used to uncover information exchanges. Results suggest that the general public develops perceptions about climate change based on complex social and cultural systems rather than as patrons of scientific inquiry and understanding. It appears that perceptions about climate change itself may not be the limiting factor in these communities' adaptive capacity, but rather how they perceive local risks. We provide a novel methodological approach in understanding rural community adaptation and resilience in fire-prone landscapes and offer a framework for future studies.
INFLUENCE OF APPLYING ADDITIONAL FORCING FANS FOR THE AIR DISTRIBUTION IN VENTILATION NETWORK
Directory of Open Access Journals (Sweden)
Nikodem SZLĄZAK
2016-07-01
Full Text Available Mining progress in underground mines cause the ongoing movement of working areas. Consequently, it becomes neces-sary to adapt the ventilation network of a mine to direct airflow into newly-opened districts. For economic reasons, opening new fields is often achieved via underground workings. Length of primary intake and return routes increases and also increases the total resistance of a complex ventilation network. The development of a subsurface structure can make it necessary to change the air distribution in a ventilation network. Increasing airflow into newly-opened districts is necessary. In mines where extraction does not entail gas-related hazards, there is possibility of implementing a push-pull ventilation system in order to supplement airflows to newly developed mining fields. This is achieved by installing sub-surface fan stations with forcing fans at the bottom of downcast shaft. In push-pull systems with multiple main fans, it is vital to select forcing fans with characteristic curves matching those of the existing exhaust fans to prevent undesirable mutual interaction. In complex ventilation networks it is necessary to calculate distribution of airflow (especially in net-works with a large number of installed fans. In the article the influence of applying additional forcing fans for the air distribution in ventilation network for underground mine were considered. There are also analysed the extent of over-pressure caused by the additional forcing fan in branches of the ventilation network (the operating range of additional forcing fan. Possibilities of increasing airflow rate in working areas were conducted.
Forecast model applied to quality control with autocorrelational data
Directory of Open Access Journals (Sweden)
Adriano Mendonça Souza
2013-11-01
Full Text Available This research approaches the prediction models applied to industrial processes, in order to check the stability of the process by means of control charts, applied to residues from linear modeling. The data used for analysis refers to the moisture content, permeability and compression resistance to the green (RCV, belonging to the casting process of green sand molding in A Company, which operates in the casting and machining, for which dynamic multivariate regression model was set. As the observations were auto-correlated, it was necessary to seek a mathematical model that produces independent and identically distribuibed residues. The models found make possible to understand the variables behavior, assisting in the achievement of the forecasts and in the monitoring of the referred process. Thus, it can be stated that the moisture content is very unstable comparing to the others variables.
Xiong, Qingrong; Baychev, Todor; Jivkov, Andrey
2016-01-01
Pore network models have been applied widely for simulating a variety of different physical and chemical processes, including phase exchange, non-Newtonian displacement, non-Darcy flow, reactive transport and thermodynamically consistent oil layers. The realism of such modelling, i.e. the credibility of their predictions, depends to a large extent on the quality of the correspondence between the pore space of a given medium and the pore network constructed as its representation. The main expe...
Models of network reliability analysis, combinatorics, and Monte Carlo
Gertsbakh, Ilya B
2009-01-01
Unique in its approach, Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo provides a brief introduction to Monte Carlo methods along with a concise exposition of reliability theory ideas. From there, the text investigates a collection of principal network reliability models, such as terminal connectivity for networks with unreliable edges and/or nodes, network lifetime distribution in the process of its destruction, network stationary behavior for renewable components, importance measures of network elements, reliability gradient, and network optimal reliability synthesis
Delay and Disruption Tolerant Networking MACHETE Model
Segui, John S.; Jennings, Esther H.; Gao, Jay L.
2011-01-01
To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity
A comprehensive Network Security Risk Model for process control networks.
Henry, Matthew H; Haimes, Yacov Y
2009-02-01
The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.
Modeling of methane emissions using artificial neural network approach
Directory of Open Access Journals (Sweden)
Stamenković Lidija J.
2015-01-01
Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007
Lessons Learned from Applying Safety Culture Maturity Model in Thailand
Bordin Vongvitayapirom; Punnamee Sachakamol; Hanna Kropsu-Vehkapera; Pekka Kess
2013-01-01
Purpose – The purpose of this paper it to provide practitioner and researcher lessons learned from applying a safety culture maturity model in the oil and gas industry in Thailand. It proposes a roadmap to improve safety culture maturity in an organization. Design/methodology/approach – A safety culture maturity of 5 levels was chosen (Hudson’s model) to be applied in oil and gas company, and a questionnaire survey was conducted with 2,251 employees or 74% of the target group across the compa...
Personalized Learning Network Teaching Model
Feng, Zhou
Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.
Mathematical analysis techniques for modeling the space network activities
Foster, Lisa M.
1992-01-01
The objective of the present work was to explore and identify mathematical analysis techniques, and in particular, the use of linear programming. This topic was then applied to the Tracking and Data Relay Satellite System (TDRSS) in order to understand the space network better. Finally, a small scale version of the system was modeled, variables were identified, data was gathered, and comparisons were made between actual and theoretical data.
Modelling Users` Trust in Online Social Networks
Directory of Open Access Journals (Sweden)
Iacob Cătoiu
2014-02-01
Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.
Wright, Russell W.
2012-01-01
This quantitative study explored the connection between the use of a private social networking website during the graduate admissions process at a law school in the southeastern United States and the decision to matriculate or withdraw from the program. A theoretical model of persistence over time within the graduate admissions process was…
Advancing the State of the Art in Applying Network Science to C2
2014-06-01
models, and to identify which organizational problems benefit best from self-synchronization. In the tenth contribution, Complex Adaptive Information... eCommerce to support CMI) – Offensive cyber operations: • Integrating kinetic & cyber ops – Incorporating network science into C2 theory: • Editing
A generalized mixture model applied to diabetes incidence data.
Zuanetti, Daiane Aparecida; Milan, Luis Aparecido
2017-07-01
We present a generalization of the usual (independent) mixture model to accommodate a Markovian first-order mixing distribution. We propose the data-driven reversible jump, a Markov chain Monte Carlo (MCMC) procedure, for estimating the a posteriori probability for each model in a model selection procedure and estimating the corresponding parameters. Simulated datasets show excellent performance of the proposed method in the convergence, model selection, and precision of parameters estimates. Finally, we apply the proposed method to analyze USA diabetes incidence datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Applied data analysis and modeling for energy engineers and scientists
Reddy, T Agami
2011-01-01
""Applied Data Analysis and Modeling for Energy Engineers and Scientists"" discusses mathematical models, data analysis, and decision analysis in modeling. The approach taken in this volume focuses on the modeling and analysis of thermal systems in an engineering environment, while also covering a number of other critical areas. Other material covered includes the tools that researchers and engineering professionals will need in order to explore different analysis methods, use critical assessment skills and reach sound engineering conclusions. The book also covers process and system design and
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK
Directory of Open Access Journals (Sweden)
R. Hadapiningradja Kusumodestoni
2015-11-01
Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.
Artificial neural network cardiopulmonary modeling and diagnosis
Kangas, Lars J.; Keller, Paul E.
1997-01-01
The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
Modeling Organizational Design - Applying A Formalism Model From Theoretical Physics
Directory of Open Access Journals (Sweden)
Robert Fabac
2008-06-01
Full Text Available Modern organizations are exposed to diverse external environment influences. Currently accepted concepts of organizational design take into account structure, its interaction with strategy, processes, people, etc. Organization design and planning aims to align this key organizational design variables. At the higher conceptual level, however, completely satisfactory formulation for this alignment doesn’t exist. We develop an approach originating from the application of concepts of theoretical physics to social systems. Under this approach, the allocation of organizational resources is analyzed in terms of social entropy, social free energy and social temperature. This allows us to formalize the dynamic relationship between organizational design variables. In this paper we relate this model to Galbraith's Star Model and we also suggest improvements in the procedure of the complex analytical method in organizational design.
Applying a Hybrid MCDM Model for Six Sigma Project Selection
Directory of Open Access Journals (Sweden)
Fu-Kwun Wang
2014-01-01
Full Text Available Six Sigma is a project-driven methodology; the projects that provide the maximum financial benefits and other impacts to the organization must be prioritized. Project selection (PS is a type of multiple criteria decision making (MCDM problem. In this study, we present a hybrid MCDM model combining the decision-making trial and evaluation laboratory (DEMATEL technique, analytic network process (ANP, and the VIKOR method to evaluate and improve Six Sigma projects for reducing performance gaps in each criterion and dimension. We consider the film printing industry of Taiwan as an empirical case. The results show that our study not only can use the best project selection, but can also be used to analyze the gaps between existing performance values and aspiration levels for improving the gaps in each dimension and criterion based on the influential network relation map.
Remarks on orthotropic elastic models applied to wood
Directory of Open Access Journals (Sweden)
Nilson Tadeu Mascia
2006-09-01
Full Text Available Wood is generally considered an anisotropic material. In terms of engineering elastic models, wood is usually treated as an orthotropic material. This paper presents an analysis of two principal anisotropic elastic models that are usually applied to wood. The first one, the linear orthotropic model, where the material axes L (Longitudinal, R( radial and T(tangential are coincident with the Cartesian axes (x, y, z, is more accepted as wood elastic model. The other one, the cylindrical orthotropic model is more adequate of the growth caracteristics of wood but more mathematically complex to be adopted in practical terms. Specifically due to its importance in wood elastic parameters, this paper deals with the fiber orientation influence in these models through adequate transformation of coordinates. As a final result, some examples of the linear model, which show the variation of elastic moduli, i.e., Young´s modulus and shear modulus, with fiber orientation are presented.
Applications of spatial statistical network models to stream data
Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal
2014-01-01
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.
Slow update stochastic simulation algorithms for modeling complex biochemical networks.
Ghosh, Debraj; De, Rajat K
2017-10-30
The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost. In order to reduce the propensity update cost, we proposed two algorithms: slow update exact stochastic simulation algorithm (SUESSA) and slow update exact sorting stochastic simulation algorithm (SUESSSA). We applied cache-based linear search (CBLS) in these two algorithms for improving the search operation for finding reactions to be executed. Data structure used for incorporating CBLS is very simple and the cost of maintaining this during propensity update operation is very low. Hence, time taken during propensity updates, for simulating strongly coupled networks, is very fast; which leads to reduction of total simulation time. SUESSA and SUESSSA are not only restricted to elementary reactions, they support higher order reactions too. We used linear chain model and colloidal aggregation model to perform a comparative analysis of the performances of our methods with the existing algorithms. We also compared the performances of our methods with the existing ones, for large biochemical networks including B cell receptor and FcϵRI signaling networks. Copyright © 2017 Elsevier B.V. All rights reserved.
Applying the knowledge creation model to the management of ...
African Journals Online (AJOL)
user
The contribution of evaluation to socialization and externalization of tacit knowledge: The case of the World Bank. Evaluation, 10(3), 263-283. Mclean, E. R. 2004. Measuring e-commerce success: Applying the DeLone & McLean information systems success model. International Journal of Electronic Commerce, 9(1), 31-47.
The limitations of applying rational decision-making models to ...
African Journals Online (AJOL)
The aim of this paper is to show the limitations of rational decision-making models as applied to child spacing and more specifically to the use of modern methods of contraception. In the light of factors known to influence low uptake of child spacing services in other African countries, suggestions are made to explain the ...
An applied general equilibrium model for Dutch agribusiness policy analysis
Peerlings, J.
1993-01-01
The purpose of this thesis was to develop a basic static applied general equilibrium (AGE) model to analyse the effects of agricultural policy changes on Dutch agribusiness. In particular the effects on inter-industry transactions, factor demand, income, and trade are of
Applying the Flipped Classroom Model to English Language Arts Education
Young, Carl A., Ed.; Moran, Clarice M., Ed.
2017-01-01
The flipped classroom method, particularly when used with digital video, has recently attracted many supporters within the education field. Now more than ever, language arts educators can benefit tremendously from incorporating flipped classroom techniques into their curriculum. "Applying the Flipped Classroom Model to English Language Arts…
Knowledge Growth: Applied Models of General and Individual Knowledge Evolution
Silkina, Galina Iu.; Bakanova, Svetlana A.
2016-01-01
The article considers the mathematical models of the growth and accumulation of scientific and applied knowledge since it is seen as the main potential and key competence of modern companies. The problem is examined on two levels--the growth and evolution of objective knowledge and knowledge evolution of a particular individual. Both processes are…
Spiking modular neural networks: A neural network modeling approach for hydrological processes
National Research Council Canada - National Science Library
Kamban Parasuraman; Amin Elshorbagy; Sean K. Carey
2006-01-01
.... In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer...
Distance-Based Access Modifiers Applied to Safety in Home Networks
DEFF Research Database (Denmark)
Mortensen, Kjeld Høyer; Schougaard, Kari Rye; Schultz, Ulrik Pagh
2004-01-01
Home networks and the interconnection of home appliances is a classical theme in ubiquitous computing research. Security is a recurring concern, but there is a lack of awareness of safety: preventing the computerized house from harming the inhabitants, even in a worst-case scenario where...... be performed within a physical proximity that ensures safety. We use a declarative approach integrated with an IDL language to express location-based restrictions on operations. This model has been implemented in a middleware for home audio-video devices, using infrared communication and a local-area network...
A Bayesian Network View on Nested Effects Models
Directory of Open Access Journals (Sweden)
Fröhlich Holger
2009-01-01
Full Text Available Nested effects models (NEMs are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the /Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.
PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL
Directory of Open Access Journals (Sweden)
S. Munapo
2012-01-01
Full Text Available
ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.
AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.
Algebraic Statistics for Network Models
2014-02-19
use algebra, combinatorics and Markov bases to give a constructing way of answering this question for ERGMs of interest. Question 2: How do we model...for every function. 06/06/13 Petrović. Manuscripts 8, 10. Invited lecture at the Scientific Session on Commutative Algebra and Combinatorics at the
Network Modeling and Simulation (NEMSE)
2013-07-01
Prioritized Packet Fragmentation", IEEE Trans. Multimedia , Oct. 2012. [13 SYSENG] . Defense Acquisition Guidebook, Chapter 4 System Engineering, and...2012 IEEE High Performance Extreme Computing Conference (HPEC) poster session [1 Ross]. Motivation Air Force Research Lab needs o Capability...is virtual. These eight virtualizations were: System-in-the-Loop (SITL) using OPNET Modeler, COPE, Field Programmable Gate Array ( FPGA Physical
Security Modeling on the Supply Chain Networks
Directory of Open Access Journals (Sweden)
Marn-Ling Shing
2007-10-01
Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.
An evolving model of online bipartite networks
Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang
2013-12-01
Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.
Lan Liu; Ryan K. L. Ko; Guangming Ren; Xiaoping Xu
2017-01-01
As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the ne...
Directory of Open Access Journals (Sweden)
Mirjana Maksimović
2014-09-01
Full Text Available The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques… is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical d
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K
2016-01-01
The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Directory of Open Access Journals (Sweden)
Sudip Mandal
2016-01-01
Full Text Available The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.
2016-04-01
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
An autocatalytic network model for stock markets
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-02-01
The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.
Modeling the resilience of critical infrastructure: the role of network dependencies.
Guidotti, Roberto; Chmielewski, Hana; Unnikrishnan, Vipin; Gardoni, Paolo; McAllister, Therese; van de Lindt, John
2016-01-01
Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities' well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure.
Small-World and Scale-Free Network Models for IoT Systems
Directory of Open Access Journals (Sweden)
Insoo Sohn
2017-01-01
Full Text Available It is expected that Internet of Things (IoT revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.
Mechanisms of security based on digital certificates applied in a telemedicine network.
Vivas, T; Zambrano, A; Huerta, M
2008-01-01
The medical patient's information is private and extremely sensitive. This information has to be stored and transmitted carefully because it can be exposed to a series of threats and it is necessary to be protected. This research contemplates the security mechanisms based on digital certificates applied to a pilot telemedicine network, that guarantees the confidentiality, integrity and availability of the medical information of the patients for the hospitable centers involved. The prototype telemedicine network will interconnect hospitals in the municipalities of Baruta and Hatillo Miranda State, in Venezuela by Internet, to provide and support a health care system in inaccessible areas, and the transmission of patient's biomedical signals and the health education to people through secure mechanisms such as digital certificates.
Applied Bounded Model Checking for Interlocking System Designs
DEFF Research Database (Denmark)
Haxthausen, Anne Elisabeth; Peleska, Jan; Pinger, Ralf
2013-01-01
In this article the verification and validation of interlocking systems is investigated. Reviewing both geographical and route-related interlocking, the verification objectives can be structured from a perspective of computer science into (1) verification of static semantics, and (2) verification...... of behavioural (operational) semantics. The former checks that the plant model – that is, the software components reflecting the physical components of the interlocking system – has been set up in an adequate way. The latter investigates trains moving through the network, with the objective to uncover potential...... safety violations. From a formal methods perspective, these verification objectives can be approached by theorem proving, global, or bounded model checking. This article explains the techniques for application of bounded model checking techniques, and discusses their advantages in comparison...
Stability of functions in Boolean models of gene regulatory networks
Rämö, Pauli; Kesseli, Juha; Yli-Harja, Olli
2005-09-01
Boolean networks are used to model large nonlinear systems such as gene regulatory networks. We will present results that can be used to understand how the choice of functions affects the network dynamics. The so called bias-map and its fixed points depict much of the function's dynamical role in the network. We define the concept of stabilizing functions and show that many Post and canalizing functions are also stabilizing functions. Boolean networks constructed using the same type of stabilizing functions are always stable regardless of the average in-degree of network functions. We derive the number of all stabilizing functions and find it to be much larger than the number of Post and canalizing functions. We also discuss the implementation of functions and apply the presented results to biological data that give an approximation of the distribution of regulatory functions in eucaryotic cells. We find that the obtained theoretical results on the number of active genes are biologically plausible. Finally, based on the presented results, we discuss why canalizing and Post regulatory functions seem to be common in cells.
Neural network connectivity and response latency modelled by stochastic processes
DEFF Research Database (Denmark)
Tamborrino, Massimiliano
is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...
Keystone Business Models for Network Security Processors
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Arthur Low
2013-07-01
Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.
A Model of Mental State Transition Network
Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo
Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.
Propagation models for computing biochemical reaction networks
Henzinger, Thomas A; Mateescu, Maria
2011-01-01
We introduce propagation models, a formalism designed to support general and efficient data structures for the transient analysis of biochemical reaction networks. We give two use cases for propagation abstract data types: the uniformization method and numerical integration. We also sketch an implementation of a propagation abstract data type, which uses abstraction to approximate states.
Modelling crime linkage with Bayesian networks
de Zoete, J.; Sjerps, M.; Lagnado, D.; Fenton, N.
2015-01-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model
Lagrangian modeling of switching electrical networks
Scherpen, Jacquelien M.A.; Jeltsema, Dimitri; Klaassens, J. Ben
2003-01-01
In this paper, a general and systematic method is presented to model topologically complete electrical networks, with or without multiple or single switches, within the Euler–Lagrange framework. Apart from the physical insight that can be obtained in this way, the framework has proven to be useful
Computational Modeling of Complex Protein Activity Networks
Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude
2017-01-01
Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a
Modeling Network Transition Constraints with Hypergraphs
DEFF Research Database (Denmark)
Harrod, Steven
2011-01-01
values. A directed hypergraph formulation is derived to address railway network sequencing constraints, and an experimental problem sample solved to estimate the magnitude of objective inflation when interaction effects are ignored. The model is used to demonstrate the value of advance scheduling...
Agrochemical fate models applied in agricultural areas from Colombia
Garcia-Santos, Glenda; Yang, Jing; Andreoli, Romano; Binder, Claudia
2010-05-01
The misuse application of pesticides in mainly agricultural catchments can lead to severe problems for humans and environment. Especially in developing countries where there is often found overuse of agrochemicals and incipient or lack of water quality monitoring at local and regional levels, models are needed for decision making and hot spots identification. However, the complexity of the water cycle contrasts strongly with the scarce data availability, limiting the number of analysis, techniques, and models available to researchers. Therefore there is a strong need for model simplification able to appropriate model complexity and still represent the processes. We have developed a new model so-called Westpa-Pest to improve water quality management of an agricultural catchment located in the highlands of Colombia. Westpa-Pest is based on the fully distributed hydrologic model Wetspa and a fate pesticide module. We have applied a multi-criteria analysis for model selection under the conditions and data availability found in the region and compared with the new developed Westpa-Pest model. Furthermore, both models were empirically calibrated and validated. The following questions were addressed i) what are the strengths and weaknesses of the models?, ii) which are the most sensitive parameters of each model?, iii) what happens with uncertainties in soil parameters?, and iv) how sensitive are the transfer coefficients?
A general diagnostic model applied to language testing data.
von Davier, Matthias
2008-11-01
Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well-known models, such as univariate and multivariate versions of the Rasch model and the two-parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL Internet-based testing.
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks
DEFF Research Database (Denmark)
Hagen, Espen; Dahmen, David; Stavrinou, Maria L
2016-01-01
on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...... model for a ∼1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its...
Propagating semantic information in biochemical network models
Directory of Open Access Journals (Sweden)
Schulz Marvin
2012-01-01
Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....
Network traffic model using GIPP and GIBP
Lee, Yong Duk; Van de Liefvoort, Appie; Wallace, Victor L.
1998-10-01
In telecommunication networks, the correlated nature of teletraffic patterns can have significant impact on queuing measures such as queue length, blocking and delay. There is, however, not yet a good general analytical description which can easily incorporate the correlation effect of the traffic, while at the same time maintaining the ease of modeling. The authors have shown elsewhere, that the covariance structures of the generalized Interrupted Poisson Process (GIPP) and the generalized Interrupted Bernoulli Process (GIBP) have an invariance property which makes them reasonably general, yet algebraically manageable, models for representing correlated network traffic. The GIPP and GIBP have a surprisingly rich sets of parameters, yet these invariance properties enable us to easily incorporate the covariance function as well as the interarrival time distribution into the model to better matchobservations. In this paper, we show an application of GIPP and GIBP for matching an analytical model to observed or experimental data.
Applications of Wavelet Neural Network Model to Building Settlement Prediction: A Case Study
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Qulin TAN
2014-04-01
Full Text Available Deformation monitoring is a significant work for engineering safety, which is performed throughout the entire process of engineering design, construction and operation. Based on the theoretic analysis of wavelet and neural network, we applied the improved BP neural network model, auxiliary wavelet neural network model and embedded wavelet neural network model to the settlement prediction in one practical engineering monitoring project with MATLAB software programming. The cumulative and the interval settlement was predicted and compared with measured data. The overall performances of the three models were analyzed and compared. The results show that the accuracies of two kinds of wavelet neural network models are roughly the same, which prediction errors of monitoring points are less than 1mm, obviously superior to the single BP neural network model.
Directory of Open Access Journals (Sweden)
José Manuel Corujeira Gómez
2014-10-01
Full Text Available The current definition of creativity gives importance to interpersonal communication in innovation strategies, and allows us to question the profiles of professionals –innovation partners– communication skills in the practice session in which they are applied. This text shows shallow results on the application of some of their tactics with a group of students. We tested structural/procedural descriptions of hypothetical effects of communication using indicators proposed by Network Theory in terms topologies provided by the group. Without a conclusive result, we expect this paper helps to the creativity's investigation in the innovation sessions.
APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION
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Khuat Thanh Tung
2016-04-01
Full Text Available Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging.
Modeling Multistandard Wireless Networks in OPNET
DEFF Research Database (Denmark)
Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée
2011-01-01
Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....
Applied systems ecology: models, data, and statistical methods
Energy Technology Data Exchange (ETDEWEB)
Eberhardt, L L
1976-01-01
In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.
Influence of rainfall observation network on model calibration and application
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A. Bárdossy
2008-01-01
Full Text Available The objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. A meso-scale catchment located in southwest Germany has been selected for this study. First, the semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. The performance of the hydrological model is analyzed as a function of the raingauge density. Secondly, the calibrated model is validated using interpolated precipitation from the same raingauge density used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. Lastly, the effect of missing rainfall data is investigated by using a multiple linear regression approach for filling in the missing measurements. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the above described three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need re-calibration of the model parameters, specifically model calibrated on relatively sparse precipitation information might perform well on dense precipitation information while model calibrated on dense precipitation information fails on sparse precipitation information. Also, the model calibrated with the complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as
Modelling dendritic ecological networks in space: anintegrated network perspective
Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.
2013-01-01
Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within
Valdez, André Calero; Schaar, Anne Kathrin; Ziefle, Martina
2012-01-01
In an increasingly faster paced innovative world, maintaining the ability to innovate in spite of an aging work force will become every company's strongest leverage for success. Tapping the latent knowledge resources and creativity of overlooked employees and persisting crucial information for business conduct are promising results for social networking sites (SNS) in a working context. Usability and usefulness are exponential factors in creating a successful SNS. In order to make a SNS usable for a heterogeneous user group, analyses of user diversity in regard to social media need to be done. Furthermore differences in communication medium and frequency in regard to age, content, hierarchy position, departmental thresholds and company size need to be analyzed. For analysis purposes both qualitative and quantitative research methods were applied. Strong effects of age and communication content were found in survey with 194 participants.
Mechanical modeling of interpenetrating polymer network reinforced acrylic elastomer
Schmidt, Arne; Bergamini, Andrea; Kovacs, Gabor; Mazza, Edoardo
2010-04-01
Interpenetrating polymer network reinforced acrylic elastomers (IPN) offer outstanding performance in free-standing contractile dielectric elastomer actuators. This work presents the verification of a recently proposed material model for a VHB 4910 based IPN [1]. The 3D large strain material model was determined from extensive data of multiaxial mechanical experiments and allows to account for the variations in material composition of IPN-membranes. We employed inflation tests to membranes of different material composition to study the materials response in a stress state different from the one that was used to extract the material parameters. By applying the material model to finite element models we successfully validated the material model in a range of material compositions typically used for dielectric elastomer actuator applications. In combination with a characterization of electro-mechanical coupling, this 3D large strain model can be used to model IPN-based dielectric elastomer actuators.
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...
On traffic modelling in GPRS networks
DEFF Research Database (Denmark)
Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee
2005-01-01
Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...
Neural Network Model of memory retrieval
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Stefano eRecanatesi
2015-12-01
Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.
Study of data fusion algorithms applied to unattended ground sensor network
Pannetier, B.; Moras, J.; Dezert, Jean; Sella, G.
2014-06-01
In this paper, data obtained from wireless unattended ground sensor network are used for tracking multiple ground targets (vehicles, pedestrians and animals) moving on and off the road network. The goal of the study is to evaluate several data fusion algorithms to select the best approach to establish the tactical situational awareness. The ground sensor network is composed of heterogeneous sensors (optronic, radar, seismic, acoustic, magnetic sensors) and data fusion nodes. The fusion nodes are small hardware platforms placed on the surveillance area that communicate together. In order to satisfy operational needs and the limited communication bandwidth between the nodes, we study several data fusion algorithms to track and classify targets in real time. A multiple targets tracking (MTT) algorithm is integrated in each data fusion node taking into account embedded constraint. The choice of the MTT algorithm is motivated by the limit of the chosen technology. In the fusion nodes, the distributed MTT algorithm exploits the road network information in order to constrain the multiple dynamic models. Then, a variable structure interacting multiple model (VS-IMM) is adapted with the road network topology. This algorithm is well-known in centralized architecture, but it implies a modification of other data fusion algorithms to preserve the performances of the tracking under constraints. Based on such VS-IMM MTT algorithm, we adapt classical data fusion techniques to make it working in three architectures: centralized, distributed and hierarchical. The sensors measurements are considered asynchronous, but the fusion steps are synchronized on all sensors. Performances of data fusion algorithms are evaluated using simulated data and also validated on real data. The scenarios under analysis contain multiple targets with close and crossing trajectories involving data association uncertainties.
Yang, Shiju; Li, Chuandong; Huang, Tingwen
2016-03-01
The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
A improved Network Security Situation Awareness Model
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Li Fangwei
2015-08-01
Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Fractional Calculus Model of Electrical Impedance Applied to Human Skin
Vosika, Zoran B.; Lazovic, Goran M.; Misevic, Gradimir N.; Simic-Krstic, Jovana B.
2013-01-01
Fractional calculus is a mathematical approach dealing with derivatives and integrals of arbitrary and complex orders. Therefore, it adds a new dimension to understand and describe basic nature and behavior of complex systems in an improved way. Here we use the fractional calculus for modeling electrical properties of biological systems. We derived a new class of generalized models for electrical impedance and applied them to human skin by experimental data fitting. The primary model introduces new generalizations of: 1) Weyl fractional derivative operator, 2) Cole equation, and 3) Constant Phase Element (CPE). These generalizations were described by the novel equation which presented parameter related to remnant memory and corrected four essential parameters We further generalized single generalized element by introducing specific partial sum of Maclaurin series determined by parameters We defined individual primary model elements and their serial combination models by the appropriate equations and electrical schemes. Cole equation is a special case of our generalized class of models for Previous bioimpedance data analyses of living systems using basic Cole and serial Cole models show significant imprecisions. Our new class of models considerably improves the quality of fitting, evaluated by mean square errors, for bioimpedance data obtained from human skin. Our models with new parameters presented in specific partial sum of Maclaurin series also extend representation, understanding and description of complex systems electrical properties in terms of remnant memory effects. PMID:23577065
Weighted correlation network analysis (WGCNA applied to the tomato fruit metabolome.
Directory of Open Access Journals (Sweden)
Matthew V DiLeo
Full Text Available BACKGROUND: Advances in "omics" technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting "big data." Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome. METHODOLOGY: Principal component analysis (PCA, batch learning self-organizing maps (BL-SOM and weighted gene co-expression network analysis (WGCNA were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data. CONCLUSIONS: PCA separated the two major genetic backgrounds (AC and NC, but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined "modules" of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.
Modeling Wireless Sensor Networks for Monitoring in Biological Processes
DEFF Research Database (Denmark)
Nadimi, Esmaeil
signal strength). Fusing the two measured behavioral data resulted in an improvement of the classification results regarding the animal behavior mode (activity/inactivity), compared to the results achieved by only monitoring one of the behavioral parameters. Applying a multiple-model adaptive estimation...... (MMAE) approach to the data resulted in the highest classification success rate, due to the use of precise forth-order mathematical models which relate the feed offer to the pitch angle of the neck. This thesis shows that wireless sensor networks can be successfully employed to monitor the behavior...
Online traffic flow model applying dynamic flow-density relation
Kim, Y
2002-01-01
This dissertation describes a new approach of the online traffic flow modelling based on the hydrodynamic traffic flow model and an online process to adapt the flow-density relation dynamically. The new modelling approach was tested based on the real traffic situations in various homogeneous motorway sections and a motorway section with ramps and gave encouraging simulation results. This work is composed of two parts: first the analysis of traffic flow characteristics and second the development of a new online traffic flow model applying these characteristics. For homogeneous motorway sections traffic flow is classified into six different traffic states with different characteristics. Delimitation criteria were developed to separate these states. The hysteresis phenomena were analysed during the transitions between these traffic states. The traffic states and the transitions are represented on a states diagram with the flow axis and the density axis. For motorway sections with ramps the complicated traffic fl...
Balfagón, A C; Serrano-Hernanz, A; Teixido, J; Tejedor-Estrada, R
2010-10-01
In this work, a comparative study between two methods to acquire relevant information about a cosmetic formulation has been carried out. A Design of Experiments (DOE) has been applied in two stages to a capillary cosmetic cream: first, a Plackett-Burman (PB) design has been used to reduce the number of variables to be studied; second, a complete factorial design has been implemented. With the experimental data collected from the DOE, a Least Mean Square (LMS) algorithm and Artificial Neural Networks (ANN) have been utilized to obtain an equation (or model) that could explain cream viscosity. Calculations have shown that ANN are the best prediction method to fit a model to experimental data, within the interval of concentrations defined by the whole set of experiments.
Producing a Set of Models for the Iron Homeostasis Network
Directory of Open Access Journals (Sweden)
Nicolas Mobilia
2013-08-01
Full Text Available This paper presents a method for modeling biological systems which combines formal techniques on intervals, numerical simulations and satisfaction of Signal Temporal Logic (STL formulas. The main modeling challenge addressed by this approach is the large uncertainty in the values of the parameters due to the experimental difficulties of getting accurate biological data. This method considers intervals for each parameter and a formal description of the expected behavior of the model. In a first step, it produces reduced intervals of possible parameter values. Then by performing a systematic search in these intervals, it defines sets of parameter values used in the next step. This procedure aims at finding a sub-space where the model robustly behaves as expected. We apply this method to the modeling of the cellular iron homeostasis network in erythroid progenitors. The produced model describes explicitly the regulation mechanism which acts at the translational level.
Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
Artificial neural network modeling of p-cresol photodegradation.
Abdollahi, Yadollah; Zakaria, Azmi; Abbasiyannejad, Mina; Masoumi, Hamid Reza Fard; Moghaddam, Mansour Ghaffari; Matori, Khamirul Amin; Jahangirian, Hossein; Keshavarzi, Ashkan
2013-06-03
The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.
A Collaborative Network Model for Agrifood Transactions on Regional Base
Volpentesta, Antonio P.; Ammirato, Salvatore
The paper deals with a collaborative agrifood network in a regional scenario where producers of high typical and quality goods and consumer groups are involved in agrifood transactions as well as information and knowledge exchanges through an e-business platform. While producers are engaged in providing consumers with useful and timely information about healthiness, environmentally friendliness and most importantly, food quality of their products, consumers are engaged in giving prompt and understandable feedbacks to the producers. In this sense, the network is a form of proactive learning community. Starting from some basic socio-economic assumptions on a reference territory, we present an organizational model that can be adopted to foster the development of the regional area where it is applied. An instantiation of the model for a selected territory (the District of High Quality Productions in Sibari, Calabria, Italy) and first results, coming from two pilot tests, have been summarized as well.
Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model
Energy Technology Data Exchange (ETDEWEB)
Rossi, R; Gallagher, B; Neville, J; Henderson, K
2011-11-11
Given a large time-evolving network, how can we model and characterize the temporal behaviors of individual nodes (and network states)? How can we model the behavioral transition patterns of nodes? We propose a temporal behavior model that captures the 'roles' of nodes in the graph and how they evolve over time. The proposed dynamic behavioral mixed-membership model (DBMM) is scalable, fully automatic (no user-defined parameters), non-parametric/data-driven (no specific functional form or parameterization), interpretable (identifies explainable patterns), and flexible (applicable to dynamic and streaming networks). Moreover, the interpretable behavioral roles are generalizable, computationally efficient, and natively supports attributes. We applied our model for (a) identifying patterns and trends of nodes and network states based on the temporal behavior, (b) predicting future structural changes, and (c) detecting unusual temporal behavior transitions. We use eight large real-world datasets from different time-evolving settings (dynamic and streaming). In particular, we model the evolving mixed-memberships and the corresponding behavioral transitions of Twitter, Facebook, IP-Traces, Email (University), Internet AS, Enron, Reality, and IMDB. The experiments demonstrate the scalability, flexibility, and effectiveness of our model for identifying interesting patterns, detecting unusual structural transitions, and predicting the future structural changes of the network and individual nodes.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural
Distributed Global Function Model Finding for Wireless Sensor Network Data
Directory of Open Access Journals (Sweden)
Song Deng
2016-01-01
Full Text Available Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs. With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND. In DGFMF-WSND, on the basis of gene expression programming (GEP, an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.
Modeling and adaptive pinning synchronization control for a chaotic-motion motor in complex network
Zhu, Darui; Liu, Chongxin; Yan, Bingnan
2014-01-01
We introduce a chaos model for a permanent-magnet synchronous motor and construct a coupled chaotic motor in a complex dynamic network using the Newman-Watts small-world network algorithm. We apply adaptive pinning control theory for complex networks to obtain suitable adaptive feedback gain and the number of nodes to be pinned. Nodes of low degree are pinned to realize global asymptotic synchronization in the complex network. The proposed adaptive pinning controller is added to the complex motor network for simulation and verification.
Kinematic Structural Modelling in Bayesian Networks
Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.
2017-04-01
We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In
Dynamic Decision Making for Graphical Models Applied to Oil Exploration
Martinelli, Gabriele; Hauge, Ragnar
2012-01-01
We present a framework for sequential decision making in problems described by graphical models. The setting is given by dependent discrete random variables with associated costs or revenues. In our examples, the dependent variables are the potential outcomes (oil, gas or dry) when drilling a petroleum well. The goal is to develop an optimal selection strategy that incorporates a chosen utility function within an approximated dynamic programming scheme. We propose and compare different approximations, from simple heuristics to more complex iterative schemes, and we discuss their computational properties. We apply our strategies to oil exploration over multiple prospects modeled by a directed acyclic graph, and to a reservoir drilling decision problem modeled by a Markov random field. The results show that the suggested strategies clearly improve the simpler intuitive constructions, and this is useful when selecting exploration policies.
Climate Change and Market Collapse: A Model Applied to Darfur
Directory of Open Access Journals (Sweden)
Ola Olsson
2016-03-01
Full Text Available A recurring argument in the global debate is that climate deterioration is likely to make social conflicts over diminishing natural resources more common in the future. The exact mechanism behind such a development has so far not been successfully characterized in the literature. In this paper, we present a general model of a community populated by farmers and herders who can either divide up land in a market economy or in autarky. The key insight from our model is that decreasing resources can make trade between the two groups collapse, which in turn makes each group’s welfare independent of that of the other. Predictions from the model are then applied to the conflict in Darfur. Our analysis suggests that three decades of drought in the area can at least partially explain the observed disintegration of markets and the subsequent rise of social tensions.
Remote sensing applied to numerical modelling. [water resources pollution
Sengupta, S.; Lee, S. S.; Veziroglu, T. N.; Bland, R.
1975-01-01
Progress and remaining difficulties in the construction of predictive mathematical models of large bodies of water as ecosystems are reviewed. Surface temperature is at present the only variable than can be measured accurately and reliably by remote sensing techniques, but satellite infrared data are of sufficient resolution for macro-scale modeling of oceans and large lakes, and airborne radiometers are useful in meso-scale analysis (of lakes, bays, and thermal plumes). Finite-element and finite-difference techniques applied to the solution of relevant coupled time-dependent nonlinear partial differential equations are compared, and the specific problem of the Biscayne Bay and environs ecosystem is tackled in a finite-differences treatment using the rigid-lid model and a rigid-line grid system.
Systems biology of plant molecular networks: from networks to models
Valentim, F.L.
2015-01-01
Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs
Zhang, Xiaohui; Tokoglu, Fuyuze; Negishi, Michiro; Arora, Jagriti; Winstanley, Scott; Spencer, Dennis D; Constable, R Todd
2011-07-15
Epilepsy is a brain disorder usually associated with abnormal cortical and/or subcortical functional networks. Exploration of the abnormal network properties and localization of the brain regions involved in human epilepsy networks are critical for both the understanding of the epilepsy networks and planning therapeutic strategies. Currently, most localization of seizure networks come from ictal EEG observations. Functional MRI provides high spatial resolution together with more complete anatomical coverage compared with EEG and may have advantages if it can be used to identify the network(s) associated with seizure onset and propagation. Epilepsy networks are believed to be present with detectable abnormal signatures even during the interictal state. In this study, epilepsy networks were investigated using resting-state fMRI acquired with the subjects in the interictal state. We tested the hypothesis that social network theory applied to resting-state fMRI data could reveal abnormal network properties at the group level. Using network data as input to a classification algorithm allowed separation of medial temporal lobe epilepsy (MTLE) patients from normal control subjects indicating the potential value of such network analyses in epilepsy. Five local network properties obtained from 36 anatomically defined ROIs were input as features to the classifier. An iterative feature selection strategy based on the classification efficiency that can avoid 'over-fitting' is proposed to further improve the classification accuracy. An average sensitivity of 77.2% and specificity of 83.86% were achieved via 'leave one out' cross validation. This finding of significantly abnormal network properties in group level data confirmed our initial hypothesis and provides motivation for further investigation of the epilepsy process at the network level. Copyright © 2011 Elsevier B.V. All rights reserved.
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION
Schomaker, Lambert
Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal
NetDiff - Bayesian model selection for differential gene regulatory network inference.
Thorne, Thomas
2016-12-16
Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.
Applying Mechanistic Dam Breach Models to Historic Levee Breaches
Directory of Open Access Journals (Sweden)
Risher Paul
2016-01-01
Full Text Available Hurricane Katrina elevated levee risk in the US national consciousness, motivating agencies to assess and improve their levee risk assessment methodology. Accurate computation of the flood flow magnitude and timing associated with a levee breach remains one of the most difficult and uncertain components of levee risk analysis. Contemporary methods are largely empirical and approximate, introducing substantial uncertainty to the damage and life loss models. Levee breach progressions are often extrapolated to the final width and breach formation time based on limited experience with past breaches or using regression equations developed from a limited data base of dam failures. Physically based embankment erosion models could improve levee breach modeling. However, while several mechanistic embankment breach models are available, they were developed for dams. Several aspects of the levee breach problem are distinct, departing from dam breach assumptions. This study applies three embankments models developed for dam breach analysis (DL Breach, HR BREACH, and WinDAM C to historic levee breaches with observed (or inferred breach rates, assessing the limitations, and applicability of each model to the levee breach problem.
A Log Logistic Survival Model Applied to Hypobaric Decompression Sickness
Conkin, Johnny
2001-01-01
Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.
Neural Networks Modelling of Municipal Real Estate Market Rent Rates
Directory of Open Access Journals (Sweden)
Muczyński Andrzej
2016-12-01
Full Text Available This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.
Development of a pore network simulation model to study nonaqueous phase liquid dissolution
Dillard, Leslie A.; Blunt, Martin J.
2000-01-01
A pore network simulation model was developed to investigate the fundamental physics of nonequilibrium nonaqueous phase liquid (NAPL) dissolution. The network model is a lattice of cubic chambers and rectangular tubes that represent pore bodies and pore throats, respectively. Experimental data obtained by Powers [1992] were used to develop and validate the model. To ensure the network model was representative of a real porous medium, the pore size distribution of the network was calibrated by matching simulated and experimental drainage and imbibition capillary pressure-saturation curves. The predicted network residual styrene blob-size distribution was nearly identical to the observed distribution. The network model reproduced the observed hydraulic conductivity and produced relative permeability curves that were representative of a poorly consolidated sand. Aqueous-phase transport was represented by applying the equation for solute flux to the network tubes and solving for solute concentrations in the network chambers. Complete mixing was found to be an appropriate approximation for calculation of chamber concentrations. Mass transfer from NAPL blobs was represented using a corner diffusion model. Predicted results of solute concentration versus Peclet number and of modified Sherwood number versus Peclet number for the network model compare favorably with experimental data for the case in which NAPL blob dissolution was negligible. Predicted results of normalized effluent concentration versus pore volume for the network were similar to the experimental data for the case in which NAPL blob dissolution occurred with time.
Model of Opinion Spreading in Social Networks
Kanovsky, Igor
2011-01-01
We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.
Inverse geothermal modelling applied to Danish sedimentary basins
Poulsen, Søren E.; Balling, Niels; Bording, Thue S.; Mathiesen, Anders; Nielsen, Søren B.
2017-10-01
This paper presents a numerical procedure for predicting subsurface temperatures and heat-flow distribution in 3-D using inverse calibration methodology. The procedure is based on a modified version of the groundwater code MODFLOW by taking advantage of the mathematical similarity between confined groundwater flow (Darcy's law) and heat conduction (Fourier's law). Thermal conductivity, heat production and exponential porosity-depth relations are specified separately for the individual geological units of the model domain. The steady-state temperature model includes a model-based transient correction for the long-term palaeoclimatic thermal disturbance of the subsurface temperature regime. Variable model parameters are estimated by inversion of measured borehole temperatures with uncertainties reflecting their quality. The procedure facilitates uncertainty estimation for temperature predictions. The modelling procedure is applied to Danish onshore areas containing deep sedimentary basins. A 3-D voxel-based model, with 14 lithological units from surface to 5000 m depth, was built from digital geological maps derived from combined analyses of reflection seismic lines and borehole information. Matrix thermal conductivity of model lithologies was estimated by inversion of all available deep borehole temperature data and applied together with prescribed background heat flow to derive the 3-D subsurface temperature distribution. Modelled temperatures are found to agree very well with observations. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature gradients to depths of 2000-3000 m are generally around 25-30 °C km-1, locally up to about 35 °C km-1. Large regions have geothermal reservoirs with characteristic temperatures
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
Mathematical model for spreading dynamics of social network worms
Sun, Xin; Liu, Yan-Heng; Li, Bin; Li, Jin; Han, Jia-Wei; Liu, Xue-Jie
2012-04-01
In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks.
Modeling regulatory networks with weight matrices
DEFF Research Database (Denmark)
Weaver, D.C.; Workman, Christopher; Stormo, Gary D.
1999-01-01
Systematic gene expression analyses provide comprehensive information about the transcriptional responseto different environmental and developmental conditions. With enough gene expression data points,computational biologists may eventually generate predictive computer models of transcription...... regulation.Such models will require computational methodologies consistent with the behavior of known biologicalsystems that remain tractable. We represent regulatory relationships between genes as linear coefficients orweights, with the "net" regulation influence on a gene's expression being...... the mathematical summation of theindependent regulatory inputs. Test regulatory networks generated with this approach display stable andcyclically stable gene expression levels, consistent with known biological systems. We include variables tomodel the effect of environmental conditions on transcription regulation...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
Modeling social influence through network autocorrelation : constructing the weight matrix
Leenders, Roger Th. A. J.
Many physical and social phenomena are embedded within networks of interdependencies, the so-called 'context' of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models,
Dynamical modeling and analysis of large cellular regulatory networks
Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.
2013-06-01
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Dynamical modeling and analysis of large cellular regulatory networks.
Bérenguier, D; Chaouiya, C; Monteiro, P T; Naldi, A; Remy, E; Thieffry, D; Tichit, L
2013-06-01
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Göb, S; Oliveros, E; Bossmann, S H; Braun, A M; Nascimento, C A; Guardani, R
2001-01-01
Among advanced oxidation processes (AOPs), the photochemically enhanced Fenton reaction may be considered as one of the most efficient for the degradation of contaminants in industrial wastewater. This process involves a series of complex reactions. Therefore, an empirical model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor for the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant. The model describes the evolution of the pollutant concentration during irradiation time as a function of the process conditions. It has been used for simulating the behavior of the reaction system in sensitivity studies aimed at optimizing the amounts of reactants employed in the process, an iron(III) salt and hydrogen peroxide, as well as the temperature. The results show that the process is most sensitive to the concentration of iron(III) salt and temperature, whereas the concentration of hydrogen peroxide has a minor effect.
Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.
Balaur, Irina; Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J; Auffray, Charles
2017-04-01
The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . ibalaur@eisbm.org. Supplementary data are available at Bioinformatics online.
Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels
Directory of Open Access Journals (Sweden)
Petkov T.
2016-03-01
Full Text Available A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC and Gas Chromatography - mass spectrometry (GC-MS were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3 and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.
Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels
Petkov, T.; Mustafa, Z.; Sotirov, S.; Milina, R.; Moskovkina, M.
2016-03-01
A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, "unknown" were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety "unknown" samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.
Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration
Directory of Open Access Journals (Sweden)
Yunshan Sun
2013-01-01
Full Text Available A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI. In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
Applied economic model development algorithm for electronics company
Directory of Open Access Journals (Sweden)
Mikhailov I.
2017-01-01
Full Text Available The purpose of this paper is to report about received experience in the field of creating the actual methods and algorithms that help to simplify development of applied decision support systems. It reports about an algorithm, which is a result of two years research and have more than one-year practical verification. In a case of testing electronic components, the time of the contract conclusion is crucial point to make the greatest managerial mistake. At this stage, it is difficult to achieve a realistic assessment of time-limit and of wage-fund for future work. The creation of estimating model is possible way to solve this problem. In the article is represented an algorithm for creation of those models. The algorithm is based on example of the analytical model development that serves for amount of work estimation. The paper lists the algorithm’s stages and explains their meanings with participants’ goals. The implementation of the algorithm have made possible twofold acceleration of these models development and fulfilment of management’s requirements. The resulting models have made a significant economic effect. A new set of tasks was identified to be further theoretical study.
A Pruning Neural Network Model in Credit Classification Analysis
Directory of Open Access Journals (Sweden)
Yajiao Tang
2018-01-01
Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.
Applying gene regulatory network logic to the evolution of social behavior
Baran, Nicole M.; Patrick T McGrath; Streelman, J Todd
2017-01-01
Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to stu...
Modeling virtualized downlink cellular networks with ultra-dense small cells
Ibrahim, Hazem
2015-09-11
The unrelenting increase in the mobile users\\' populations and traffic demand drive cellular network operators to densify their infrastructure. Network densification increases the spatial frequency reuse efficiency while maintaining the signal-to-interference-plus-noise-ratio (SINR) performance, hence, increases the spatial spectral efficiency and improves the overall network performance. However, control signaling in such dense networks consumes considerable bandwidth and limits the densification gain. Radio access network (RAN) virtualization via control plane (C-plane) and user plane (U-plane) splitting has been recently proposed to lighten the control signaling burden and improve the network throughput. In this paper, we present a tractable analytical model for virtualized downlink cellular networks, using tools from stochastic geometry. We then apply the developed modeling framework to obtain design insights for virtualized RANs and quantify associated performance improvement. © 2015 IEEE.
Hsieh, Chih-Sheng; Lee, Lung fei
2017-01-01
In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives stemming from interaction benefits on certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interac...
Nature preservation acceptance model applied to tanker oil spill simulations
DEFF Research Database (Denmark)
Friis-Hansen, Peter; Ditlevsen, Ove Dalager
2003-01-01
is exemplified by a study of oil spills due to simulated tanker collisions in the Danish straits. It is found that the distribution of the oil spill volume per spill is well represented by an exponential distribution both in Oeresund and in Great Belt. When applied in the Poisson model, a risk profile reasonably...... close to the standard lognormal profile is obtained. Moreover, based on data pairs (volume, cost) for world wide oil spills it is inferred that the conditional distribution of the costs given the spill volume is well modeled by a lognormal distribution. By unconditioning by the exponential distribution...... of the single oil spill, a risk profile for the costs is obtained that is indistinguishable from the standard lognormal risk profile.Finally the question of formulating a public risk acceptance criterion is addressed following Ditlevsen, and it is argued that a Nature Preservation Willingness Index can...
Enhanced pid vs model predictive control applied to bldc motor
Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.
2018-01-01
BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.
Applying the Canonical Text Services Model to the Coptic SCRIPTORIUM
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Bridget Almas
2016-11-01
Full Text Available Coptic SCRIPTORIUM is a platform for interdisciplinary and computational research in Coptic texts and linguistics. The purpose of this project was to research and implement a system of stable identification for the texts and linguistic data objects in Coptic SCRIPTORIUM to facilitate their citation and reuse. We began the project with a preferred solution, the Canonical Text Services URN model, which we validated for suitability for the corpus and compared it to other approaches, including HTTP URLs and Handles. The process of applying the CTS model to Coptic SCRIPTORIUM required an in-depth analysis that took into account the domain-specific scholarly research and citation practices, the structure of the textual data, and the data management workflow.
Learning and Model-checking Networks of I/O Automata
DEFF Research Database (Denmark)
Mao, Hua; Jaeger, Manfred
2012-01-01
We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating nite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can...... learn generic models for network entities in the form of automata templates. As is characteristic for SRL techniques, the abstraction level aorded by learning generic templates enables one to apply the learned model to new domains. A main benet of learning models based on nite automata lies in the fact...
A model of traffic signs recognition with convolutional neural network
Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing
2016-10-01
In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.
Fuzzy stochastic neural network model for structural system identification
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
2017-01-01
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
Challenges on Probabilistic Modeling for Evolving Networks
Ding, Jianguo; Bouvry, Pascal
2013-01-01
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors,...
Aeronautical telecommunications network advances, challenges, and modeling
Musa, Sarhan M
2015-01-01
Addresses the Challenges of Modern-Day Air Traffic Air traffic control (ATC) directs aircraft in the sky and on the ground to safety, while the Aeronautical Telecommunications Network (ATN) comprises all systems and phases that assist in aircraft departure and landing. The Aeronautical Telecommunications Network: Advances, Challenges, and Modeling focuses on the development of ATN and examines the role of the various systems that link aircraft with the ground. The book places special emphasis on ATC-introducing the modern ATC system from the perspective of the user and the developer-and provides a thorough understanding of the operating mechanism of the ATC system. It discusses the evolution of ATC, explaining its structure and how it works; includes design examples; and describes all subsystems of the ATC system. In addition, the book covers relevant tools, techniques, protocols, and architectures in ATN, including MIPv6, air traffic control (ATC), security of air traffic management (ATM), very-high-frequenc...
Neural Network Program Package for Prosody Modeling
Directory of Open Access Journals (Sweden)
J. Santarius
2004-04-01
Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].
Risk matrix model applied to the outsourcing of logistics' activities
Directory of Open Access Journals (Sweden)
Fouad Jawab
2015-09-01
Full Text Available Purpose: This paper proposes the application of the risk matrix model in the field of logistics outsourcing. Such an application can serve as the basis for decision making regarding the conduct of a risk management in the logistics outsourcing process and allow its prevention. Design/methodology/approach: This study is based on the risk management of logistics outsourcing in the field of the retail sector in Morocco. The authors identify all possible risks and then classify and prioritize them using the Risk Matrix Model. Finally, we have come to four possible decisions for the identified risks. The analysis was made possible through interviews and discussions with the heads of departments and agents who are directly involved in each outsourced activity. Findings and Originality/value: It is possible to improve the risk matrix model by proposing more personalized prevention measures according to each company that operates in the mass-market retailing. Originality/value: This study is the only one made in the process of logistics outsourcing in the retail sector in Morocco through Label’vie as a case study. First, we had identified as thorough as we could all possible risks, then we applied the Risk Matrix Model to sort them out in an ascending order of importance and criticality. As a result, we could hand out to the decision-makers the mapping for an effective control of risks and a better guiding of the process of risk management.
Model output statistics applied to wind power prediction
Energy Technology Data Exchange (ETDEWEB)
Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)
1999-03-01
Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.
Contributions and challenges for network models in cognitive neuroscience.
Sporns, Olaf
2014-05-01
The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.
Modeling of regional warehouse network generation
Directory of Open Access Journals (Sweden)
Popov Pavel Vladimirovich
2016-08-01
Full Text Available One of the factors that has a significant impact on the socio-economic development of the Russian Federation’s regions is the logistics infrastructure. It provides integrated transportation and distribution service of material flows. One of the main elements of logistics infrastructure is a storage infrastructure, which includes distribution center, distribution-and-sortout and sortout warehouses. It is the most expedient to place distribution center in the vicinity of the regional center. One of the tasks of the distribution network creation within the regions of the Russian Federation is to determine the location, capacity and number of stores. When determining regional network location of general purpose warehouses methodological approaches to solving the problems of location of production and non-production can be used which depend on various economic factors. The mathematical models for solving relevant problems are the deployment models. However, the existing models focus on the dimensionless power storage. The purpose of the given work is to develop a model to determine the optimal location of general-purpose warehouses on the Russian Federation area. At the first stage of the work, the authors assess the main economic indicators influencing the choice of the location of general purpose warehouses. An algorithm for solving the first stage, based on ABC, discriminant and cluster analysis were proposed by the authors in earlier papers. At the second stage the specific locations of general purpose warehouses and their power is chosen to provide the cost minimization for the construction and subsequent maintenance of warehouses and transportation heterogeneous products. In order to solve this problem the authors developed a mathematical model that takes into account the possibility of delivery in heterogeneous goods from suppliers and manufacturers in the distribution and storage sorting with specified set of capacities. The model allows
Artificial neural network models for biomass gasification in fluidized bed gasifiers
DEFF Research Database (Denmark)
Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles
2013-01-01
Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...
Statistical modelling of neural networks in {gamma}-spectrometry applications
Energy Technology Data Exchange (ETDEWEB)
Vigneron, V.; Martinez, J.M. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. de Mecanique et de Technologie; Morel, J.; Lepy, M.C. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. des Applications et de la Metrologie des Rayonnements Ionisants
1995-12-31
Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio {sup 235} U/({sup 235} U + {sup 236} U + {sup 238} U). The usual method consider a limited number of {Gamma}-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K{sub {alpha}} X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium {sup 235} U and {sup 238} U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs.
Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series.
Rubiolo, Mariano; Milone, Diego H; Stegmayer, Georgina
2015-01-01
Discovering gene regulatory networks from data is one of the most studied topics in recent years. Neural networks can be successfully used to infer an underlying gene network by modeling expression profiles as times series. This work proposes a novel method based on a pool of neural networks for obtaining a gene regulatory network from a gene expression dataset. They are used for modeling each possible interaction between pairs of genes in the dataset, and a set of mining rules is applied to accurately detect the subjacent relations among genes. The results obtained on artificial and real datasets confirm the method effectiveness for discovering regulatory networks from a proper modeling of the temporal dynamics of gene expression profiles.
Applying the model of excellence in dental healthcare
Directory of Open Access Journals (Sweden)
Tekić Jasmina
2015-01-01
Full Text Available Introduction. Models of excellence are considered a practical tool in the field of management that should help a variety of organizations, including dental, to carry out the measurement of the quality of provided services, and so define their position in relation to excellence. The quality of healthcare implies the degree within which the system of healthcare and health services increases the likelihood of positive treatment outcome. Objective. The aim of the present study was to define a model of excellence in the field of dental healthcare (DHC in the Republic of Serbia and suggest the model of DHC whose services will have the characteristics of outstanding service in the dental practice. Methods. In this study a specially designed questionnaire was used for the assessment of the maturity level of applied management regarding quality in healthcare organizations of the Republic of Serbia. The questionnaire consists of 13 units and a total of 240 questions. Results. The results of the study were discussed involving four areas: (1 defining the main criteria and sub-criteria, (2 the elements of excellence of DHC in the Republic of Serbia, (3 the quality of DHC in the Republic of Serbia, and (4 defining the framework of the model of excellence for the DHC in the Republic of Serbia. The main criteria which defined the framework and implementation model of excellence in the field of DHC in Serbia were: leadership, management, human resources, policy and strategy, other resources, processes, patients’ satisfaction, employee’s satisfaction, impact on society and business results. The model had two main parts: the possibilities for the first five criteria and options for the other four criteria. Conclusion. Excellence in DHC business as well as the excellence of provided dental services are increasingly becoming the norm and good practice, and progressively less the exception.
Optimizing neural network models: motivation and case studies
Harp, S A; T. Samad
2012-01-01
Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally rem...
Approximating Attractors of Boolean Networks by Iterative CTL Model Checking.
Klarner, Hannes; Siebert, Heike
2015-01-01
This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: "faithfulness" which requires that the oscillating variables of all attractors in a trap space correspond to their dimensions, "univocality" which requires that there is a unique attractor in each trap space, and "completeness" which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal, and complete, which suggests that they are in general good approximations for the asymptotics of Boolean networks.
Directory of Open Access Journals (Sweden)
Danielle Taana Smith
2013-06-01
Full Text Available This study develops a framework for systematic examination of information and communication technologies (ICTs usage differences within a group. This framework situates the digital divide and digital inequalities model within a broader conceptual model of digital practice, exemplified by how groups of people use ICTs. I use nationally representative data to examine online activities on social networking sites (SNS for African Americans and other ethnoracial groups. The data for this research comes from the Pew Internet and American Life’s “Spring Tracking Survey 2008”. The results from regression analyses support the digital practice framework which moves discussions of ICT usage beyond social and economic advantages or disadvantages, and addresses individual and group needs in using these technologies.
Investigation of rotated PCA from the perspective of network communities applied to climate data
Hartman, David; Hlinka, Jaroslav; Vejmelka, Martin; Palus, Milan
2013-04-01
Applications of the rotated principal component analysis (RPCA) have a long history in climatology usually due to efforts of finding specific circulation patterns (Barnston and Livezey 1987). Using this approach several well known patterns like the North Atlantic Oscillation (NAO) or the Pacific/North American Pattern (PNA) can be identified (Barnston and Livezey 1987; Feldstein 2000). Applied to the whole globe this method gives several weakly related components that can be suspected of being important modes of climate variability. On the other hand, a relatively new topic in climate research is that of community detection and analysis (Tsonis et al. 2011), although the detection of communities in complex networks is a well established scientific field itself (Fortunato 2010; Girvan and Newman 2002). To analyze community structure one has to consider the climate system as a complex network (Tsonis and Swanson 2012), i.e. as a set of nodes represented by a climate-related variable on specific globe positions and a set of edges mutually connecting these nodes according to chosen measure of coherence (Hlinka et al. preprint). Determination of optimal community structure is well known to be a hard problem and there are several methods excelling in specific situations (Fortunato 2010) and several ways of measuring quality of resulting community structure such as modularity (Newman and Girvan 2004). Following the fact that RPCA gives us a set of components that can be represented as a community structure we investigate the potential of RPCA in community-detection context. For this purpose we use data from global National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis (Kistler et al. 2001), more specifically surface air temperature (SAT) and surface pressure level (SPL). Acknowledgement: This study is supported by the Czech Science Foundation, Project No. P103/11/J068. Barnston, AG; Livezey RE (1987) Classification
Marsman, M; Borsboom, D; Kruis, J; Epskamp, S; van Bork, R; Waldorp, L J; Maas, H L J van der; Maris, G
2017-11-07
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.
Directory of Open Access Journals (Sweden)
Eils Roland
2010-01-01
Full Text Available Abstract Background We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely social, as revealed based on the local sociality parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: a priori knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network. Results We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced social proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. Intrinsically disordered are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity. Conclusions The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.
Modelling the Steady State of Sewage Networks as a Support Tool for Their Planning and Analysis
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Grażyna Petriczek
2015-01-01
Full Text Available Fundamental questions connected with the modelling of communal sewage networks have been considered and formulas used to model the functioning of the basic network have been analyzed. The problem described concerns gravitational sewage networks divided by nodes into branches and sectors. Simulation of the steady state functioning of sewage networks is commonly carried out on the basis of nomograms in the form of charts, in which the relations between network parameters like channel diameters, flow rates, hydraulic slopes and flow velocities are described. In traditional design, the values of such parameters are simply read from such nomogram chart tables. Another way of simulating the functioning of a network is the use of professional software, like SWMM, that models sewage flows along the channels by means of differential equations de-scribing the movement of fluids. In both approaches, the user is a mechanical operator of a "black box" procedure. In this paper, another way of simulating the functioning of sewage net-works has been presented. Numerical solutions of nonlinear equations describing the physical phenomena of sewage flows are applied and explained. The presented algorithms were developed to model the steady state of a sewage network enabling a quick analysis of the network parameters and the possibility of fast, simple and comprehensible network modeling and design. (original abstract
Neural Networks For Electrohydrodynamic Effect Modelling
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Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
Applying Atmospheric Measurements to Constrain Parameters of Terrestrial Source Models
Hyer, E. J.; Kasischke, E. S.; Allen, D. J.
2004-12-01
Quantitative inversions of atmospheric measurements have been widely applied to constrain atmospheric budgets of a range of trace gases. Experiments of this type have revealed persistent discrepancies between 'bottom-up' and 'top-down' estimates of source magnitudes. The most common atmospheric inversion uses the absolute magnitude as the sole parameter for each source, and returns the optimal value of that parameter. In order for atmospheric measurements to be useful for improving 'bottom-up' models of terrestrial sources, information about other properties of the sources must be extracted. As the density and quality of atmospheric trace gas measurements improve, examination of higher-order properties of trace gas sources should become possible. Our model of boreal forest fire emissions is parameterized to permit flexible examination of the key uncertainties in this source. Using output from this model together with the UM CTM, we examined the sensitivity of CO concentration measurements made by the MOPITT instrument to various uncertainties in the boreal source: geographic distribution of burned area, fire type (crown fires vs. surface fires), and fuel consumption in above-ground and ground-layer fuels. Our results indicate that carefully designed inversion experiments have the potential to help constrain not only the absolute magnitudes of terrestrial sources, but also the key uncertainties associated with 'bottom-up' estimates of those sources.
A network-oriented business modeling environment
Bisconti, Cristian; Storelli, Davide; Totaro, Salvatore; Arigliano, Francesco; Savarino, Vincenzo; Vicari, Claudia
The development of formal models related to the organizational aspects of an enterprise is fundamental when these aspects must be re-engineered and digitalized, especially when the enterprise is involved in the dynamics and value flows of a business network. Business modeling provides an opportunity to synthesize and make business processes, business rules and the structural aspects of an organization explicit, allowing business managers to control their complexity and guide an enterprise through effective decisional and strategic activities. This chapter discusses the main results of the TEKNE project in terms of software components that enable enterprises to configure, store, search and share models of any aspects of their business while leveraging standard and business-oriented technologies and languages to bridge the gap between the world of business people and IT experts and to foster effective business-to-business collaborations.
Linear model applied to the evaluation of pharmaceutical stability data
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Renato Cesar Souza
2013-09-01
Full Text Available The expiry date on the packaging of a product gives the consumer the confidence that the product will retain its identity, content, quality and purity throughout the period of validity of the drug. The definition of this term in the pharmaceutical industry is based on stability data obtained during the product registration. By the above, this work aims to apply the linear regression according to the guideline ICH Q1E, 2003, to evaluate some aspects of a product undergoing in a registration phase in Brazil. With this propose, the evaluation was realized with the development center of a multinational company in Brazil, with samples of three different batches composed by two active principal ingredients in two different packages. Based on the preliminary results obtained, it was possible to observe the difference of degradation tendency of the product in two different packages and the relationship between the variables studied, added knowledge so new models of linear equations can be applied and developed for other products.
Applying threshold models to donations to a green electricity fund
Energy Technology Data Exchange (ETDEWEB)
Ito, Nobuyuki; Takeuchi, Kenji [Graduate School of Economics, Kobe University (Japan); Tsuge, Takahiro [Faculty of Economics, Konan University (Japan); Kishimoto, Atsuo [National Institute of Advanced Industrial Science and Technology (Japan)
2010-04-15
This study applies a threshold model proposed by to analyze the diffusion process of donating behavior for renewable energy. We first use a stated preference survey to estimate the determinants of a decision to support the donation scheme under various predicted participation rates. Using the estimated coefficients, we simulate how herd behavior spreads and the participation rate reaches the equilibrium. The participation rate at the equilibrium is estimated as 37.88% when the suggested donation is 500 yen, while it is 17.76% when the suggested amount is 1000 yen. The influence of environmentalism and altruism is also examined, and we find that these motivations increase the participation rate by 31.51% on average. (author)
Continuous Online Sequence Learning with an Unsupervised Neural Network Model.
Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff
2016-09-14
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.
Compartmentalization analysis using discrete fracture network models
Energy Technology Data Exchange (ETDEWEB)
La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)
1997-08-01
This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.
Some queuing network models of computer systems
Herndon, E. S.
1980-01-01
Queuing network models of a computer system operating with a single workload type are presented. Program algorithms are adapted for use on the Texas Instruments SR-52 programmable calculator. By slightly altering the algorithm to process the G and H matrices row by row instead of column by column, six devices and an unlimited job/terminal population could be handled on the SR-52. Techniques are also introduced for handling a simple load dependent server and for studying interactive systems with fixed multiprogramming limits.
Artificial Neural Network versus Linear Models Forecasting Doha Stock Market
Yousif, Adil; Elfaki, Faiz
2017-12-01
The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.
Networks model of the East Turkistan terrorism
Li, Ben-xian; Zhu, Jun-fang; Wang, Shun-guo
2015-02-01
The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.
Fundamentals of complex networks models, structures and dynamics
Chen, Guanrong; Li, Xiang
2014-01-01
Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P. M. A.; Ivanov, S. V.; Boukhanovsky, A. V.; van de Vijver, D. A. M. C.; Boucher, C. A. B.
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
A Search Model with a Quasi-Network
DEFF Research Database (Denmark)
Ejarque, Joao Miguel
This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network c...
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P.M.A.; Ivanov, S.V.; Boukhanovsky, A.V.; van de Vijver, D.A.M.C.; Boucher, C.A.B.
2008-01-01
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
Network evolution model for supply chain with manufactures as the core
Jiang, Dali; Fang, Ling; Yang, Jian; Li, Wu; Zhao, Jing
2018-01-01
Building evolution model of supply chain networks could be helpful to understand its development law. However, specific characteristics and attributes of real supply chains are often neglected in existing evolution models. This work proposes a new evolution model of supply chain with manufactures as the core, based on external market demand and internal competition-cooperation. The evolution model assumes the external market environment is relatively stable, considers several factors, including specific topology of supply chain, external market demand, ecological growth and flow conservation. The simulation results suggest that the networks evolved by our model have similar structures as real supply chains. Meanwhile, the influences of external market demand and internal competition-cooperation to network evolution are analyzed. Additionally, 38 benchmark data sets are applied to validate the rationality of our evolution model, in which, nine manufacturing supply chains match the features of the networks constructed by our model. PMID:29370201
Abnormal quality detection and isolation in water distribution networks using simulation models
Directory of Open Access Journals (Sweden)
F. Nejjari
2012-11-01
Full Text Available This paper proposes a model based detection and localisation method to deal with abnormal quality levels based on the chlorine measurements and chlorine sensitivity analysis in a water distribution network. A fault isolation algorithm which correlates on line the residuals (generated by comparing the available chlorine measurements with their estimations using a model with the fault sensitivity matrix is used. The proposed methodology has been applied to a District Metered Area (DMA in the Barcelona network.
Directory of Open Access Journals (Sweden)
Carolina Cloris Lopes Benassuly
2012-04-01
Full Text Available Knowledge of hydrological phenomena is required in water resources monitoring, in order to structure the water management, focusing on ensuring its multiple uses while allowing that resource´s control and conservation. The effectiveness of monitoring depends on adequate information systems design and proper operation conditions. Data acquisition, treatment and analysis are vital for establishing management strategies, thus monitoring systems and networks shall be conceived according to their main objectives, and be optimized in terms of location of data stations. The generated data shall also model hydrological behavior of the studied basin, so that data interpolation can be applied to the whole basin. The present work aimed to join concepts and methods that guide the structuring of hydrologic monitoring networks of surface waters. For evaluating historical series characteristics as well as work stations redundancy, the entropy method was used. The Macaé River Basin’s importance is related to the public and industrial uses of water in the region that is responsible for more than 80% of Brazilian oil and gas production, what justifies the relevance of the research made. This study concluded that despite of its relatively short extension, the Macaé River Basin should have higher monitoring network density, in order to provide more reliable management data. It also depicted the high relevancy of stations located in its upper course.
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Braz Calderano Filho
2014-12-01
Full Text Available Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+ sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13. Excluding the variable profile curvature (set 12, overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.
VEPCO network model reconciliation of LANL and MZA model data
Energy Technology Data Exchange (ETDEWEB)
NONE
1992-12-15
The LANL DC load flow model of the VEPCO transmission network shows 210 more substations than the AC load flow model produced by MZA utility Consultants. MZA was requested to determine the source of the difference. The AC load flow model used for this study utilizes 2 standard network algorithms (Decoupled or Newton). The solution time of each is affected by the number of substations. The more substations included, the longer the model will take to solve. In addition, the ability of the algorithms to converge to a solution is affected by line loadings and characteristics. Convergence is inhibited by numerous lightly loaded and electrically short lines. The MZA model reduces the total substations to 343 by creating equivalent loads and generation. Most of the omitted substations are lightly loaded and rated at 115 kV. The MZA model includes 16 substations not included in the LANL model. These represent new generation including Non-Utility Generator (NUG) sites, additional substations and an intertie (Wake, to CP and L). This report also contains data from the Italian State AC power flow model and the Duke Power Company AC flow model.
A Model of Genetic Variation in Human Social Networks
Fowler, James H; Christakis, Nicholas A
2008-01-01
Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...
Analysis of deterministic cyclic gene regulatory network models with delays
Ahsen, Mehmet Eren; Niculescu, Silviu-Iulian
2015-01-01
This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then reviewed, in particular Hill functions and Schwarzian derivatives. One chapter is devoted to the analysis of GRNs under negative feedback with time delays and a special case of a homogenous GRN is considered. Asymptotic stability analysis of GRNs under positive feedback is then considered in a separate chapter, in which conditions leading to bi-stability are derived. Graduate and advanced undergraduate students and researchers in control engineering, applied mathematics, systems biology and synthetic biology will find this brief to be a clear and concise introduction to the modeling and analysis of GRNs.
A Bayesian Network View on Nested Effects Models
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Achim Tresch
2009-01-01
Full Text Available Nested effects models (NEMs are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the R/Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.
Frank, Laurence Emmanuelle
2006-01-01
Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor
PageRank model of opinion formation on Ulam networks
Chakhmakhchyan, L.; Shepelyansky, D.
2013-12-01
We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.