CFD-Modeling of the Multistage Gasifier Capacity of 30 KW

Science.gov (United States)

Levin, A. A.; Kozlov, A. N.; Svishchev, D. A.; Donskoy, I. G.

2017-11-01

Single-stage fuel gasification processes have been developed and widely studied in Russia and abroad throughout the 20th century. They are fundamental to the creation and design of modern gas generator equipment. Many studies have shown that single-stage gasification process, have already reached the limit of perfection, which was a significant improvement in their performance becomes impossible and unprofitable. The most fully meet modern technical requirements of multistage gasification technology. In the first step of the process, is organized allothermic biomass pyrolysis using heat of exhaust gas and generating power plant. At this stage, the yield of volatile products (gas and tar) of fuel. In the second step, the layer of fuel is, the tar is decomposed by the action of hot air and steam, steam-gas mixture is formed further reacts with the charcoal in the third process stage. The paper presents a model developed by the authors of the multi-stage gasifier for wood chips. The model is made with the use of CFD-modeling software package (COMSOL Multiphisics). To describe the kinetics of wood pyrolysis and gasification of charcoal studies were carried out using a set of simultaneous thermal analysis. For this complex developed original methods of interpretation of measurements, including methods of technical analysis of fuels and determine the parameters of the detailed kinetics and mechanism of pyrolysis.

Multi-Stage Transportation Problem With Capacity Limit

OpenAIRE

I. Brezina; Z. Čičková; J. Pekár; M. Reiff

2010-01-01

The classical transportation problem can be applied in a more general way in practice. Related problems as Multi-commodity transportation problem, Transportation problems with different kind of vehicles, Multi-stage transportation problems, Transportation problem with capacity limit is an extension of the classical transportation problem considering the additional special condition. For solving such problems many optimization techniques (dynamic programming, linear programming, special algor...

Multi-stage wake-field accelerator

International Nuclear Information System (INIS)

Gai, Wei.

1989-01-01

In this paper we propose a multi-stage wake field acceleration scheme to overcome the low transformer ratio problem and still provide high accelerating gradients. The idea is very simple. We use a train of several electron bunches from a linear accelerator (main linac) with well defined separations between the bunches (tens of ns) to drive wake field devices. Here we have made the assumption that the wake field devices are available, whether plasma, iris-loaded metallic or dielectric wake field structures. 10 refs

Linearly Adjustable International Portfolios

International Nuclear Information System (INIS)

Fonseca, R. J.; Kuhn, D.; Rustem, B.

2010-01-01

We present an approach to multi-stage international portfolio optimization based on the imposition of a linear structure on the recourse decisions. Multiperiod decision problems are traditionally formulated as stochastic programs. Scenario tree based solutions however can become intractable as the number of stages increases. By restricting the space of decision policies to linear rules, we obtain a conservative tractable approximation to the original problem. Local asset prices and foreign exchange rates are modelled separately, which allows for a direct measure of their impact on the final portfolio value.

Linearly Adjustable International Portfolios

Science.gov (United States)

Fonseca, R. J.; Kuhn, D.; Rustem, B.

2010-09-01

We present an approach to multi-stage international portfolio optimization based on the imposition of a linear structure on the recourse decisions. Multiperiod decision problems are traditionally formulated as stochastic programs. Scenario tree based solutions however can become intractable as the number of stages increases. By restricting the space of decision policies to linear rules, we obtain a conservative tractable approximation to the original problem. Local asset prices and foreign exchange rates are modelled separately, which allows for a direct measure of their impact on the final portfolio value.

Modelling and Control of the Multi-Stage Cable Pulley-Driven Flexible-Joint Robot

Directory of Open Access Journals (Sweden)

Phongsaen Pitakwatchara

2014-07-01

Full Text Available This work is concerned with the task space impedance control of a robot driven through a multi-stage nonlinear flexible transmission system. Specifically, a two degrees-of-freedom cable pulley-driven flexible-joint robot is considered. Realistic modelling of the system is developed within the bond graph modelling framework. The model captures the nonlinear compliance behaviour of the multi-stage cable pulley transmission system, the spring effect of the augmented counterbalancing mechanism, the major loss throughout the system elements, and the typical inertial dynamics of the robot. Next, a task space impedance controller based on limited information about the angle and the current of the motors is designed. The motor current is used to infer the transmitted torque, by which the motor inertia may be modulated. The motor angle is employed to estimate the stationary distal robot link angle and the robot joint velocity. They are used in the controller to generate the desired damping force and to shape the potential energy of the flexible joint robot system to the desired configuration. Simulation and experimental results of the controlled system signify the competency of the proposed control law.

A Predictive Model of Multi-Stage Production Planning for Fixed Time Orders

Directory of Open Access Journals (Sweden)

Kozłowski Edward

2014-09-01

Full Text Available The traditional production planning model based upon a deterministic approach is well described in the literature. Due to the uncertain nature of manufacturing processes, such model can however incorrectly represent actual situations on the shop floor. This study develops a mathematical modeling framework for generating production plans in a multistage manufacturing process. The devised model takes into account the stochastic model for predicting the occurrence of faulty products. The aim of the control model is to determine the number of products which should be manufactured in each planning period to minimize both manufacturing costs and potential financial penalties for failing to fulfill the order completely.

Multistage centrifugal extractor of E92 model

International Nuclear Information System (INIS)

Wang Houheng; Xing Zhifu; Liu Xiangyan; Liu Shi; Wan Yi; Liang Kui; Hu Benyue

1987-01-01

The E92 Model multistage centrifugal extractor has been developed for the recovery of uranium and plutonium from spent nuclear reactor fuel. It offers the following advantages: shorter residence time, low hlod-up, less space required, and simplified startup and shutdown procedures, etc. Experiments on performaces of hydraulics, mass-transfer and crud discharging have proved that this unit provides a wide range of operation. The total flow rate can very from 300 to 450 L/h at organic to aqueous flow ratio of 1 to 5. The unit is designed for ratio of oranic to aqueous phase densities at a range of 0.75 to 0.85. Overall extraction and back-extraction efficiencies which is great than 99.99% were achieved using natural uranium as feed. Experiments showed that mechanical assembling and disassembling of the unit could be rapidly carried out. A run continuning up to 500 hours was stable

Interconnected Levels of Multi-Stage Marketing

DEFF Research Database (Denmark)

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. The results from the case study indicate that multi-stage marketing exists on different levels. Thus, managers...... must not only decide in general on the merits of multi-stage marketing for their firm, but must also decide on which level they will engage in multi-stage marketing. The triadic perspective enables a rich and multi-dimensional understanding of how different business relationships influence each other...... in a multi-stage marketing context. This understanding assists managers in assessing and balancing different aspects of multi- stage marketing. The triadic perspective also offers avenues for further research....

Nonlinear dynamics modelling of multistage micro-planetary gear transmission

Directory of Open Access Journals (Sweden)

Li Jianying

2018-01-01

Full Text Available The transmission structure of a 2K-H multistage micro-planetary gear transmission reducer is described in detail, and three assumptions are supposed in dynamic modelling. On basis of these assumptions, a three stages 2K-H micro-planetary gear transmission dynamic model is established, in which the relative displacement each meshing gear pairs can be obtained after including the comprehensive transmission error. According to gear kinematics, the friction arms between the sun gear, the ring gear and the nth planet are also obtained, and the friction coefficient in the mixed elastohydrodynamic lubrication is considered, the transmission system motion differential equations are obtained, including above factors and the time-varying meshing stiffness, damping and backlash, inter-stage coupling stiffness, it can be provided an theoretical foundation for further analysing the parameter sensitivity, dynamic stability and designing.

Coupling methods for multistage sampling

OpenAIRE

Chauvet, Guillaume

2015-01-01

Multistage sampling is commonly used for household surveys when there exists no sampling frame, or when the population is scattered over a wide area. Multistage sampling usually introduces a complex dependence in the selection of the final units, which makes asymptotic results quite difficult to prove. In this work, we consider multistage sampling with simple random without replacement sampling at the first stage, and with an arbitrary sampling design for further stages. We consider coupling ...

Using linear programming to analyze and optimize stochastic flow lines

DEFF Research Database (Denmark)

Helber, Stefan; Schimmelpfeng, Katja; Stolletz, Raik

2011-01-01

This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time...... programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines....

Multistage Effort and the Equity Structure of Venture Investment Based on Reciprocity Motivation

Directory of Open Access Journals (Sweden)

Chuan Ding

2015-01-01

Full Text Available For venture capitals, it is a long process from an entry to its exit. In this paper, the activity of venture investment will be divided into multistages. And, according to the effort level entrepreneurs will choose, the venture capitalists will provide an equity structure at the very beginning. As a benchmark for comparison, we will establish two game models on multistage investment under perfect rationality: a cooperative game model and a noncooperative one. Further, as a cause of pervasive psychological preference behavior, reciprocity motivation will influence the behavior of the decision-makers. Given this situation, Rabin’s reciprocity motivation theory will be applied to the multistage game model of the venture investment, and multistage behavior game model will be established as well, based on the reciprocity motivation. By looking into the theoretical derivations and simulation studies, we find that if venture capitalists and entrepreneurs both have reciprocity preferences, their utility would have been Pareto improvement compared with those under perfect rationality.

Multistage Stochastic Programming and its Applications in Energy Systems Modeling and Optimization

Science.gov (United States)

Golari, Mehdi

Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue

Foundations of linear and generalized linear models

CERN Document Server

Agresti, Alan

2015-01-01

A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,

Multi area and multistage expansion-planning of electricity supply with sustainable energy development criteria: a multi objective model

Energy Technology Data Exchange (ETDEWEB)

Unsihuay-Vila, Clodomiro; Marangon-Lima, J.W.; Souza, A.C Zambroni de [Universidade Federal de Itajuba (UNIFEI), MG (Brazil)], emails: clodomirounsihuayvila @gmail.com, marangon@unifei.edu.br, zambroni@unifei.edu.br; Perez-Arriaga, I.J. [Universidad Pontificia Comillas, Madrid (Spain)], email: ipa@mit.edu

2010-07-01

A novel multi objective, multi area and multistage model to long-term expansion-planning of integrated generation and transmission corridors incorporating sustainable energy developing is presented in this paper. The proposed MESEDES model is a multi-regional multi-objective and 'bottom-up' energy model which considers the electricity generation/transmission value-chain, i.e., power generation alternatives including renewable, nuclear and traditional thermal generation along with transmission corridors. The model decides the optimal location and timing of the electricity generation/transmission abroad the multistage planning horizon. The MESEDES model considers three objectives belonging to sustainable energy development criteria such as: a) the minimization of investments and operation costs of : power generation, transmission corridors, energy efficiency (demand side management (DSM) programs) considering CO2 capture technologies; b) minimization of Life Cycle Greenhouse Gas Emissions (LC GHG); c) maximization of the diversification of electricity generation mix. The proposed model consider aspects of the carbon abatement policy under the CDM - Clean Development Mechanism or European Union Greenhouse Gas Emission Trading Scheme. A case study is used to illustrate the proposed framework. (author)

Optimization of organic Rankine cycle power systems considering multistage axial turbine design

DEFF Research Database (Denmark)

Meroni, Andrea; Andreasen, Jesper Graa; Persico, Giacomo

2018-01-01

Organic Rankine cycle power systems represent a viable and efficient solution for the exploitation of medium-to-low temperature heat sources. Despite the large number of commissioned units, there is limited literature on the design and optimization of organic Rankine cycle power systems considering...... multistage turbine design. This work presents a preliminary design methodology and working fluid selection for organic Rankine cycle units featuring multistage axial turbines. The method is then applied to the case of waste heat recovery from a large marine diesel engine. A multistage axial turbine model...

Optimization of organic Rankine cycle power systems considering multistage axial turbine design

DEFF Research Database (Denmark)

Meroni, Andrea; Andreasen, Jesper Graa; Persico, Giacomo

2017-01-01

Organic Rankine cycle power systems represent a viable and efficient solution for the exploitation of medium-to-low temperature heat sources. Despite the large number of commissioned units, there is limited literature on the design and optimization of organic Rankine cycle power systems considering...... multistage turbine design. This work presents a preliminary design methodology and working fluid selection for organic Rankine cycle units featuring multistage axial turbines. The method is then applied to the case of waste heat recovery from a large marine diesel engine. A multistage axial turbine model...

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

Science.gov (United States)

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

A multi-stage color model revisited: implications for a gene therapy cure for red-green colorblindness.

Science.gov (United States)

Mancuso, Katherine; Mauck, Matthew C; Kuchenbecker, James A; Neitz, Maureen; Neitz, Jay

2010-01-01

In 1993, DeValois and DeValois proposed a 'multi-stage color model' to explain how the cortex is ultimately able to deconfound the responses of neurons receiving input from three cone types in order to produce separate red-green and blue-yellow systems, as well as segregate luminance percepts (black-white) from color. This model extended the biological implementation of Hurvich and Jameson's Opponent-Process Theory of color vision, a two-stage model encompassing the three cone types combined in a later opponent organization, which has been the accepted dogma in color vision. DeValois' model attempts to satisfy the long-remaining question of how the visual system separates luminance information from color, but what are the cellular mechanisms that establish the complicated neural wiring and higher-order operations required by the Multi-stage Model? During the last decade and a half, results from molecular biology have shed new light on the evolution of primate color vision, thus constraining the possibilities for the visual circuits. The evolutionary constraints allow for an extension of DeValois' model that is more explicit about the biology of color vision circuitry, and it predicts that human red-green colorblindness can be cured using a retinal gene therapy approach to add the missing photopigment, without any additional changes to the post-synaptic circuitry.

Investments in the LNG Value Chain: A Multistage Stochastic Optimization Model focusing on Floating Liquefaction Units

OpenAIRE

Røstad, Lars Dybsjord; Erichsen, Jeanette Christine

2012-01-01

In this thesis, we have developed a strategic optimization model of investments in infrastructure in the LNG value chain. The focus is on floating LNG production units: when they are a viable solution and what value they add to the LNG value chain. First a deterministic model is presented with focus on describing the value chain, before it is expanded to a multistage stochastic model with uncertain field sizes and gas prices. The objective is to maximize expected discounted profits through op...

Influence of perforation erosion on multiple growing hydraulic fractures in multi-stage fracturing

Directory of Open Access Journals (Sweden)

Yongming Li

2018-02-01

Full Text Available In multi-stage hydraulic fracturing, the limited-entry method is widely used to promote uniform growth of multiple fractures. However, this method's effectiveness may be lost because the perforations will be eroded gradually during the fracturing period. In order to study the influence of perforation erosion on multiple growing hydraulic fractures, we combined the solid–fluid coupled model of hydraulic fracture growth with an empirical model of perforation erosion to implement numerical simulation. The simulations show clearly that the erosion of perforation will significantly deteriorate the non-uniform growth of multiple fractures. Based on the numerical model, we also studied the influences of proppant concentration and injection rates on perforation erosion in multi-stage hydraulic fracturing. The results indicate that the initial erosion rates become higher with the rising proppant concentration, but the growth of multiple hydraulic fractures is not sensitive to the varied proppant concentration. In addition, higher injection rates are beneficial significantly to the limited-entry design, leading to more uniform growth of fractures. Thus, in multi-stage hydraulic fracturing enough high injection rates are proposed to keep uniform growths. Keywords: Unconventional oil and gas reservoir, Horizontal well, Perforation friction, Perforation erosion, Multi-stage hydraulic fracturing, Numerical simulation, Mathematic model, Uniform growth of fractures

Multistage Effort and the Equity Structure of Venture Investment Based on Reciprocity Motivation

OpenAIRE

Ding, Chuan; Chen, Jiacheng; Liu, Xin; Zheng, Junjun

2015-01-01

For venture capitals, it is a long process from an entry to its exit. In this paper, the activity of venture investment will be divided into multistages. And, according to the effort level entrepreneurs will choose, the venture capitalists will provide an equity structure at the very beginning. As a benchmark for comparison, we will establish two game models on multistage investment under perfect rationality: a cooperative game model and a noncooperative one. Further, as a cause of pervasive ...

Modelling female fertility traits in beef cattle using linear and non-linear models.

Science.gov (United States)

Naya, H; Peñagaricano, F; Urioste, J I

2017-06-01

Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2  linear models; h 2  > 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.

Interconnected levels of multi-stage marketing: A triadic approach

OpenAIRE

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

Multi-stage marketing gains increasing attention as knowledge of and influence on the customer's customer become more critical for the firm's success. Despite this increasing managerial relevance, systematic approaches for analyzing multi-stage marketing are still missing. This paper conceptualizes different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. ...

A new method in predicting productivity of multi-stage fractured horizontal well in tight gas reservoirs

Directory of Open Access Journals (Sweden)

Yunsheng Wei

2016-10-01

Full Text Available The generally accomplished technique for horizontal wells in tight gas reservoirs is by multi-stage hydraulic fracturing, not to mention, the flow characteristics of a horizontal well with multiple transverse fractures are very intricate. Conventional methods, well as an evaluation unit, are difficult to accurately predict production capacity of each fracture and productivity differences between wells with a different number of fractures. Thus, a single fracture sets the minimum evaluation unit, matrix, fractures, and lateral wellbore model that are then combined integrally to approximate horizontal well with multiple transverse hydraulic fractures in tight gas reservoirs. This paper presents a new semi-analytical methodology for predicting the production capacity of a horizontal well with multiple transverse hydraulic fractures in tight gas reservoirs. Firstly, a mathematical flow model used as a medium, which is disturbed by finite conductivity vertical fractures and rectangular shaped boundaries, is established and explained by the Fourier integral transform. Then the idea of a single stage fracture analysis is incorporated to establish linear flow model within a single fracture with a variable rate. The Fredholm integral numerical solution is applicable for the fracture conductivity function. Finally, the pipe flow model along the lateral wellbore is adapted to couple multi-stages fracture mathematical models, and the equation group of predicting productivity of a multi-stage fractured horizontal well. The whole flow process from the matrix to bottom-hole and production interference between adjacent fractures is also established. Meanwhile, the corresponding iterative algorithm of the equations is given. In this case analysis, the productions of each well and fracture are calculated under the different bottom-hole flowing pressure, and this method also contributes to obtaining the distribution of pressure drop and production for every

A multi-stage stochastic transmission expansion planning method

International Nuclear Information System (INIS)

Akbari, Tohid; Rahimikian, Ashkan; Kazemi, Ahad

2011-01-01

Highlights: → We model a multi-stage stochastic transmission expansion planning problem. → We include available transfer capability (ATC) in our model. → Involving this criterion will increase the ATC between source and sink points. → Power system reliability will be increased and more money can be saved. - Abstract: This paper presents a multi-stage stochastic model for short-term transmission expansion planning considering the available transfer capability (ATC). The ATC can have a huge impact on the power market outcomes and the power system reliability. The transmission expansion planning (TEP) studies deal with many uncertainties, such as system load uncertainties that are considered in this paper. The Monte Carlo simulation method has been applied for generating different scenarios. A scenario reduction technique is used for reducing the number of scenarios. The objective is to minimize the sum of investment costs (IC) and the expected operation costs (OC). The solution technique is based on the benders decomposition algorithm. The N-1 contingency analysis is also done for the TEP problem. The proposed model is applied to the IEEE 24 bus reliability test system and the results are efficient and promising.

An integrated multi-stage supply chain inventory model with imperfect production process

Directory of Open Access Journals (Sweden)

Soumita Kundu

2015-09-01

Full Text Available This paper deals with an integrated multi-stage supply chain inventory model with the objective of cost minimization by synchronizing the replenishment decisions for procurement, production and delivery activities. The supply chain structure examined here consists of a single manufacturer with multi-buyer where manufacturer orders a fixed quantity of raw material from outside suppliers, processes the materials and delivers the finished products in unequal shipments to each customer. In this paper, we consider an imperfect production system, which produces defective items randomly and assumes that all defective items could be reworked. A simple algorithm is developed to obtain an optimal production policy, which minimizes the expected average total cost of the integrated production-inventory system.

Linear models with R

CERN Document Server

Faraway, Julian J

2014-01-01

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.New to the Second EditionReorganiz

Multi-stage internal gear/turbine fuel pump

Energy Technology Data Exchange (ETDEWEB)

Maier, Eugen; Raney, Michael Raymond

2004-07-06

A multi-stage internal gear/turbine fuel pump for a vehicle includes a housing having an inlet and an outlet and a motor disposed in the housing. The multi-stage internal gear/turbine fuel pump also includes a shaft extending axially and disposed in the housing. The multi-stage internal gear/turbine fuel pump further includes a plurality of pumping modules disposed axially along the shaft. One of the pumping modules is a turbine pumping module and another of the pumping modules is a gerotor pumping module for rotation by the motor to pump fuel from the inlet to the outlet.

Fuzzy-like multiple objective multistage decision making

CERN Document Server

Xu, Jiuping

2014-01-01

Decision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like un...

Interconnected levels of Multi-Stage Marketing – A Triadic approach

DEFF Research Database (Denmark)

Vedel, Mette; Geersbro, Jens; Ritter, Thomas

2012-01-01

must not only decide in general on the merits of multi-stage marketing for their firm, but must also decide on which level they will engage in multi-stage marketing. The triadic perspective enables a rich and multi-dimensional understanding of how different business relationships influence each other......Multi-stage marketing gains increasing attention as knowledge of and influence on the customer's customer become more critical for the firm's success. Despite this increasing managerial relevance, systematic approaches for analyzing multi-stage marketing are still missing. This paper conceptualizes...... different levels of multi-stage marketing and illustrates these stages with a case study. In addition, a triadic perspective is introduced as an analytical tool for multi-stage marketing research. The results from the case study indicate that multi-stage marketing exists on different levels. Thus, managers...

A free-knot spline modeling framework for piecewise linear logistic regression in complex samples with body mass index and mortality as an example

Directory of Open Access Journals (Sweden)

Scott W. Keith

2014-09-01

Full Text Available This paper details the design, evaluation, and implementation of a framework for detecting and modeling nonlinearity between a binary outcome and a continuous predictor variable adjusted for covariates in complex samples. The framework provides familiar-looking parameterizations of output in terms of linear slope coefficients and odds ratios. Estimation methods focus on maximum likelihood optimization of piecewise linear free-knot splines formulated as B-splines. Correctly specifying the optimal number and positions of the knots improves the model, but is marked by computational intensity and numerical instability. Our inference methods utilize both parametric and nonparametric bootstrapping. Unlike other nonlinear modeling packages, this framework is designed to incorporate multistage survey sample designs common to nationally representative datasets. We illustrate the approach and evaluate its performance in specifying the correct number of knots under various conditions with an example using body mass index (BMI; kg/m2 and the complex multi-stage sampling design from the Third National Health and Nutrition Examination Survey to simulate binary mortality outcomes data having realistic nonlinear sample-weighted risk associations with BMI. BMI and mortality data provide a particularly apt example and area of application since BMI is commonly recorded in large health surveys with complex designs, often categorized for modeling, and nonlinearly related to mortality. When complex sample design considerations were ignored, our method was generally similar to or more accurate than two common model selection procedures, Schwarz’s Bayesian Information Criterion (BIC and Akaike’s Information Criterion (AIC, in terms of correctly selecting the correct number of knots. Our approach provided accurate knot selections when complex sampling weights were incorporated, while AIC and BIC were not effective under these conditions.

Make or buy decision model with multi-stage manufacturing process and supplier imperfect quality

Science.gov (United States)

Pratama, Mega Aria; Rosyidi, Cucuk Nur

2017-11-01

This research develops an make or buy decision model considering supplier imperfect quality. This model can be used to help companies make the right decision in case of make or buy component with the best quality and the least cost in multistage manufacturing process. The imperfect quality is one of the cost component that must be minimizing in this model. Component with imperfect quality, not necessarily defective. It still can be rework and used for assembly. This research also provide a numerical example and sensitivity analysis to show how the model work. We use simulation and help by crystal ball to solve the numerical problem. The sensitivity analysis result show that percentage of imperfect generally not affect to the model significantly, and the model is not sensitive to changes in these parameters. This is because the imperfect cost are smaller than overall total cost components.

Performance prediction method for a multi-stage Knudsen pump

Science.gov (United States)

Kugimoto, K.; Hirota, Y.; Kizaki, Y.; Yamaguchi, H.; Niimi, T.

2017-12-01

In this study, the novel method to predict the performance of a multi-stage Knudsen pump is proposed. The performance prediction method is carried out in two steps numerically with the assistance of a simple experimental result. In the first step, the performance of a single-stage Knudsen pump was measured experimentally under various pressure conditions, and the relationship of the mass flow rate was obtained with respect to the average pressure between the inlet and outlet of the pump and the pressure difference between them. In the second step, the performance of a multi-stage pump was analyzed by a one-dimensional model derived from the mass conservation law. The performances predicted by the 1D-model of 1-stage, 2-stage, 3-stage, and 4-stage pumps were validated by the experimental results for the corresponding number of stages. It was concluded that the proposed prediction method works properly.

Linear and Generalized Linear Mixed Models and Their Applications

CERN Document Server

Jiang, Jiming

2007-01-01

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested

Experiments for Multi-Stage Processes

DEFF Research Database (Denmark)

Tyssedal, John; Kulahci, Murat

2015-01-01

Multi-stage processes are very common in both process and manufacturing industries. In this article we present a methodology for designing experiments for multi-stage processes. Typically in these situations the design is expected to involve many factors from different stages. To minimize...... the required number of experimental runs, we suggest using mirror image pairs of experiments at each stage following the first. As the design criterion, we consider their projectivity and mainly focus on projectivity 3 designs. We provide the methodology for generating these designs for processes with any...

A simple multistage closed-(box+reservoir model of chemical evolution

Directory of Open Access Journals (Sweden)

Caimmi R.

2011-01-01

Full Text Available Simple closed-box (CB models of chemical evolution are extended on two respects, namely (i simple closed-(box+reservoir (CBR models allowing gas outflow from the box into the reservoir (Hartwick 1976 or gas inflow into the box from the reservoir (Caimmi 2007 with rate proportional to the star formation rate, and (ii simple multistage closed-(box+reservoir (MCBR models allowing different stages of evolution characterized by different inflow or outflow rates. The theoretical differential oxygen abundance distribution (TDOD predicted by the model maintains close to a continuous broken straight line. An application is made where a fictitious sample is built up from two distinct samples of halo stars and taken as representative of the inner Galactic halo. The related empirical differential oxygen abundance distribution (EDOD is represented, to an acceptable extent, as a continuous broken line for two viable [O/H]-[Fe/H] empirical relations. The slopes and the intercepts of the regression lines are determined, and then used as input parameters to MCBR models. Within the errors (-+σ, regression line slopes correspond to a large inflow during the earlier stage of evolution and to low or moderate outflow during the subsequent stages. A possible inner halo - outer (metal-poor bulge connection is also briefly discussed. Quantitative results cannot be considered for applications to the inner Galactic halo, unless selection effects and disk contamination are removed from halo samples, and discrepancies between different oxygen abundance determination methods are explained.

Split-plot designs for multistage experimentation

DEFF Research Database (Denmark)

Kulahci, Murat; Tyssedal, John

2016-01-01

at the same time will be more efficient. However, there have been only a few attempts in the literature to provide an adequate and easy-to-use approach for this problem. In this paper, we present a novel methodology for constructing two-level split-plot and multistage experiments. The methodology is based...... be accommodated in each stage. Furthermore, split-plot designs for multistage experiments with good projective properties are also provided....

PENENTUAN PRODUCTION LOT SIZES DAN TRANSFER BATCH SIZES DENGAN PENDEKATAN MULTISTAGE

Directory of Open Access Journals (Sweden)

Purnawan Adi W

2012-02-01

optimal lot size in a system of production with several types. Analysis of production batch (production lot using hybrid analytic simulation is one kind of research about optimal lot size. That research uses single-stage system approach where there are not relationships between processes in every stage or in other word; one process is independent to other process. Using the same research object with one before, this research then take up problem how to determine production lot size with multi-stage approach. First, determining optimal production lot size by linear program using the same data with previous research. Then, production lot size is used as simulation input to determine transfer batch size. Average of queue length and waiting time as performance measurement are used as reference in determining transfer batch size from several alternatives.In this research, it shows that production lot size is same with demand each period. Determination result of transfer batch size by using simulation then implemented on model. The result is descent of inventory of connector product at 76.35% and 50.59% for box connector product, as compared to inventory using single-stage approach. Keywords : multistage, production lot, transfer batch

Thermal modelling of the multi-stage heating system with variable boundary conditions in the wafer based precision glass moulding process

DEFF Research Database (Denmark)

Sarhadi, Ali; Hattel, Jesper Henri; Hansen, Hans Nørgaard

2012-01-01

pressures. Finally, the three-dimensional modelling of the multi-stage heating system in the wafer based glass moulding process is simulated with the FEM software ABAQUS for a particular industrial application for mobile phone camera lenses to obtain the temperature distribution in the glass wafer...

The application of the consecutive-Woehler-curve-concept in computation of the life values for multi-stage creep

International Nuclear Information System (INIS)

Schott, G.

1991-01-01

It is known that at multi-stage creep load there cannot be calculated any reliable life values by means of linear damage accumulation hypotheses. A practicable non-linear statement was proposed by Pantelakis. Besides the one-stage creep life curve, results from two-stage tests are required for determining the damage exponent. With this exponent, which is a function of temperature and stress in the load stage applied first, the life values can be calculated only for two-stage sequences whose stress stages have to be identical to those of the two-stage tests. For the application of the consecutive Woehler curve concept described in the following there is required the knowledge of the one-stage creep life curve and of the creep function for increasing and decreasing stress sequences derived from two-stage tests. Then, the life values can be calculated for the most different multi-stage loads. The stress stages should lie within the stress range used in the two-stage tests. (orig.) [de

A development of time-resolved emulsion detector by multi-stage shifter

International Nuclear Information System (INIS)

Takahashi, Satoru; Aoki, Shigeki

2017-01-01

Nuclear emulsion is a powerful tracking device that can record the three-dimensional trajectory of charged particles within 1 μm spatial resolution. We are promoting GRAINE project which is 10 MeV-100 GeV cosmic γ-ray observations with a precise (0.08deg at 1-2 GeV) and polarization-sensitive large-aperture-area (∼10 m 2 ) emulsion telescope by repeating long duration balloon flights. We are developing multi-stage shifter which allows us to give a timing information to emulsion tracks with ∼seconds or below. The multi-stage shifter opened feasibilities of precise cosmic γ-ray observations, GRAINE, as well as precise measurements of ν-N interactions, J-PARC T60. ∼Millisecond time resolution in a balloon-borne experiment, ∼second time resolution for 126.7 days in an accelerator ν experiment and ∼10 6 time-resolved numbers are being achieved. New model of multi-stage shifter is also being developed for future experiments. (author)

Introduction to generalized linear models

CERN Document Server

Dobson, Annette J

2008-01-01

Introduction Background Scope Notation Distributions Related to the Normal Distribution Quadratic Forms Estimation Model Fitting Introduction Examples Some Principles of Statistical Modeling Notation and Coding for Explanatory Variables Exponential Family and Generalized Linear Models Introduction Exponential Family of Distributions Properties of Distributions in the Exponential Family Generalized Linear Models Examples Estimation Introduction Example: Failure Times for Pressure Vessels Maximum Likelihood Estimation Poisson Regression Example Inference Introduction Sampling Distribution for Score Statistics Taylor Series Approximations Sampling Distribution for MLEs Log-Likelihood Ratio Statistic Sampling Distribution for the Deviance Hypothesis Testing Normal Linear Models Introduction Basic Results Multiple Linear Regression Analysis of Variance Analysis of Covariance General Linear Models Binary Variables and Logistic Regression Probability Distributions ...

Dimension of linear models

DEFF Research Database (Denmark)

Høskuldsson, Agnar

1996-01-01

Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four of these cri......Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

CERN Document Server

Faraway, Julian J

2005-01-01

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

A primer on linear models

CERN Document Server

Monahan, John F

2008-01-01

Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F

A semi-analytical modelling of multistage bunch compression with collective effects

International Nuclear Information System (INIS)

Zagorodnov, Igor; Dohlus, Martin

2010-07-01

In this paper we introduce an analytical solution (up to the third order) for a multistage bunch compression and acceleration system without collective effects. The solution for the system with collective effects is found by an iterative procedure based on this analytical result. The developed formalism is applied to the FLASH facility at DESY. Analytical estimations of RF tolerances are given. (orig.)

A semi-analytical modelling of multistage bunch compression with collective effects

Energy Technology Data Exchange (ETDEWEB)

Zagorodnov, Igor; Dohlus, Martin

2010-07-15

In this paper we introduce an analytical solution (up to the third order) for a multistage bunch compression and acceleration system without collective effects. The solution for the system with collective effects is found by an iterative procedure based on this analytical result. The developed formalism is applied to the FLASH facility at DESY. Analytical estimations of RF tolerances are given. (orig.)

Bio-inspired approach to multistage image processing

Science.gov (United States)

Timchenko, Leonid I.; Pavlov, Sergii V.; Kokryatskaya, Natalia I.; Poplavska, Anna A.; Kobylyanska, Iryna M.; Burdenyuk, Iryna I.; Wójcik, Waldemar; Uvaysova, Svetlana; Orazbekov, Zhassulan; Kashaganova, Gulzhan

2017-08-01

Multistage integration of visual information in the brain allows people to respond quickly to most significant stimuli while preserving the ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing, described in this paper, comprises main types of cortical multistage convergence. One of these types occurs within each visual pathway and the other between the pathways. This approach maps input images into a flexible hierarchy which reflects the complexity of the image data. The procedures of temporal image decomposition and hierarchy formation are described in mathematical terms. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image which encapsulates, in a computer manner, structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a very quick response from the system. The result is represented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match.

Exposure Control Using Adaptive Multi-Stage Item Bundles.

Science.gov (United States)

Luecht, Richard M.

This paper presents a multistage adaptive testing test development paradigm that promises to handle content balancing and other test development needs, psychometric reliability concerns, and item exposure. The bundled multistage adaptive testing (BMAT) framework is a modification of the computer-adaptive sequential testing framework introduced by…

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

Science.gov (United States)

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

Multi-stage crypto ransomware attacks: A new emerging cyber threat to critical infrastructure and industrial control systems

OpenAIRE

Aaron Zimba; Zhaoshun Wang; Hongsong Chen

2018-01-01

The inevitable integration of critical infrastructure to public networks has exposed the underlying industrial control systems to various attack vectors. In this paper, we model multi-stage crypto ransomware attacks, which are today an emerging cyber threat to critical infrastructure. We evaluate our modeling approach using multi-stage attacks by the infamous WannaCry ransomware. The static malware analysis results uncover the techniques employed by the ransomware to discover vulnerable nodes...

Changes in Cartilage Biomarker Levels During a Transcontinental Multistage Footrace Over 4486 km.

Science.gov (United States)

Mündermann, Annegret; Klenk, Christopher; Billich, Christian; Nüesch, Corina; Pagenstert, Geert; Schmidt-Trucksäss, Arno; Schütz, Uwe

2017-09-01

Cartilage turnover and load-induced tissue changes are frequently assessed by quantifying concentrations of cartilage biomarkers in serum. To date, information on the effects of ultramarathon running on articular cartilage is scarce. Serum concentrations of cartilage oligomeric matrix protein (COMP), matrix metalloproteinase (MMP)-1, MMP-3, MMP-9, COL2-3/4C long mono (C2C), procollagen type II C-terminal propeptide (CPII), and C2C:CPII will increase throughout a multistage ultramarathon. Descriptive laboratory study. Blood samples were collected from 36 runners (4 female; mean age, 49.0 ± 10.7 years; mean body mass index, 23.1 ± 2.3 kg/m 2 [start] and 21.4 ± 1.9 kg/m 2 [finish]) before (t 0 ) and during (t 1 : 1002 km; t 2 : 2132 km; t 3 : 3234 km; t 4 : 4039 km) a 4486-km multistage ultramarathon. Serum COMP, MMP-1, MMP-3, MMP-9, C2C, and CPII levels were assessed using commercial enzyme-linked immunosorbent assays. Linear mixed models were used to detect significant changes in serum biomarker levels over time with the time-varying covariates of body weight, running speed, and daily running time. Serum concentrations of COMP, MMP-9, and MMP-3 changed significantly throughout the multistage ultramarathon. On average, concentrations increased during the first measurement interval (MI1: t 1 -t 0 ) by 22.5% for COMP (95% CI, 0.29-0.71 ng/mL), 22.3% for MMP-3 (95% CI, 0.24-15.37 ng/mL), and 95.6% for MMP-9 (95% CI, 81.7-414.5 ng/mL) and remained stable throughout MI2, MI3, and MI4. Serum concentrations of MMP-1, C2C, CPII, and C2C:CPII did not change significantly throughout the multistage ultramarathon. Changes in MMP-3 were statistically associated with changes in COMP throughout the ultramarathon race (MMP-3: Wald Z = 3.476, P = .001). Elevated COMP levels indicate increased COMP turnover in response to extreme running, and the association between load-induced changes in MMP-3 and changes in COMP suggests the possibility that MMP-3 may be involved in the

Integral performance optimum design for multistage solid propellant rocket motors

Energy Technology Data Exchange (ETDEWEB)

Zhang, Hongtao (Shaanxi Power Machinery Institute (China))

1989-04-01

A mathematical model for integral performance optimization of multistage solid propellant rocket motors is presented. A calculation on a three-stage, volume-fixed, solid propellant rocket motor is used as an example. It is shown that the velocity at burnout of intermediate-range or long-range ballistic missile calculated using this model is four percent greater than that using the usual empirical method.

Multistage linear electron acceleration using pulsed transmission lines

International Nuclear Information System (INIS)

Miller, R.B.; Prestwich, K.R.; Poukey, J.W.; Epstein, B.G.; Freeman, J.R.; Sharpe, A.W.; Tucker, W.K.; Shope, S.L.

1981-01-01

A four-stage linear electron accelerator is described which uses pulsed radial transmission lines as the basic accelerating units. An annular electron beam produced by a foilless diode is guided through the accelerator by a strong axial magnetic field. Synchronous firing of the injector and the acccelerating modules is accomplished with self-breaking oil switches. The device has accelerated beam currents of 25 kA to kinetic energies of 9 MV, with 90% current transport efficiency. The average accelerating gradient is 3 MV/m

From spiking neuron models to linear-nonlinear models.

Science.gov (United States)

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Multistage switched inductor boost converter for renewable energy application

DEFF Research Database (Denmark)

Maroti, Pandav Kiran; Padmanaban, Sanjeevikumar; Bhaskar, Mahajan Sagar

2017-01-01

In this paper Multistage Switched Inductor Boost Converter (Multistage SIBC) is uttered for renewable energy applications. The projected converter is derived from an amalgamation of the conventional step-up converter and inductor stack. The number of inductor and duty ratio decides the overall...

Novel methodology for wide-ranged multistage morphing waverider based on conical theory

Science.gov (United States)

Liu, Zhen; Liu, Jun; Ding, Feng; Xia, Zhixun

2017-11-01

This study proposes the wide-ranged multistage morphing waverider design method. The flow field structure and aerodynamic characteristics of multistage waveriders are also analyzed. In this method, the multistage waverider is generated in the same conical flowfield, which contains a free-stream surface and different compression-stream surfaces. The obtained results show that the introduction of the multistage waverider design method can solve the problem of aerodynamic performance deterioration in the off-design state and allow the vehicle to always maintain the optimal flight state. The multistage waverider design method, combined with transfiguration flight strategy, can lead to greater design flexibility and the optimization of hypersonic wide-ranged waverider vehicles.

Multistage model for the action of cytotoxic T lymphocytes in multicellular conjugates

International Nuclear Information System (INIS)

Macken, C.A.; Perelson, A.S.

1984-01-01

The authors propose a multistage stochastic model to explain data on the kinetics of target cell lysis by cytotoxic T lymphocytes in multicellular conjugates. A novel feature of this model is that the authors explicitly consider both the lethal hitting stage and the target cell disintegration stage of the cytolytic process. Further, the authors allow for the possibility that target cell disintegration is itself a complex process composed of many events. The comparison of this model with the data of other investigators suggests that cytotoxic T cells deliver lethal hits at random to undamage target cells. Having received a lethal hit, the target cell disintegrates over a variable length of time. The disintegration times of target cells from different conjugates appear to be randomly distributed and to be consistent with a model in which disintegration occurs by at least two major, sequential, rate-limiting events. For conjugates containing one lymphocyte and multiple target cells, the mean rate at which a lethally hit target cell disintegrates is found to be independent of the total number of target cells in the conjugate. This model predicts that in such multicellular conjugates, individual target cells lyse one by one, on average at approximately 30-min intervals, thus agreeing closely with previously reported experimental observations. 35 references, 3 figures, 2 tables

Linear Models

CERN Document Server

Searle, Shayle R

2012-01-01

This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

Planning under uncertainty solving large-scale stochastic linear programs

Energy Technology Data Exchange (ETDEWEB)

Infanger, G. [Stanford Univ., CA (United States). Dept. of Operations Research]|[Technische Univ., Vienna (Austria). Inst. fuer Energiewirtschaft

1992-12-01

For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.

Dynamic Linear Models with R

CERN Document Server

Campagnoli, Patrizia; Petris, Giovanni

2009-01-01

State space models have gained tremendous popularity in as disparate fields as engineering, economics, genetics and ecology. Introducing general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. It illustrates the fundamental steps needed to use dynamic linear models in practice, using R package.

Lung cancer from radon and smoking: a multistage model for the WISMUT uranium miners

International Nuclear Information System (INIS)

Dillen, Teun van; Bijwaard, Harmen; Schnelzer, Maria; Kreuzer, Michaela; Grosche, Bernd

2008-01-01

Full text: In the world's third-largest uranium-mining province located in areas of Saxony and Thuringia in the former German Democratic Republic, the WISMUT Company conducted extensive uranium mining starting in 1946. Up to 1990, when mining activities were discontinued, most of the 400,000 employees had been exposed to uranium ore dust and radon and its progeny. It is well established that, besides smoking, such exposures are associated with an increased risk of lung cancer. From about 130,000 known miners a huge cohort of 59,000 miners has been formed and in an epidemiological analysis lung cancer risks have been evaluated (Grosche et al., 2006). We will present an alternative approach using a biologically-based multistage carcinogenesis model quantifying the lung-cancer risk related to both the exposure to radon and smoking habits. This mechanistic technique allows for extrapolation to the low exposures that are important for present-day radiation protection purposes and the transfer of risk across populations. The model is applied to a sub-cohort of about 35,000 persons who were employed at WISMUT after 1955, with known annual exposures estimated from the job-exposure matrix (Lehmann et al., 2004). Unfortunately, detailed information on smoking is missing for most miners. However, this information has been retrieved in two case-control studies, one of which was nested in the cohort while the other was not (Brueske-Hohlfeld et al., 2006). For these studies, the relevant smoking parameters are assembled in so-called smoking spectra that are next projected onto the entire cohort using a Monte-Carlo sampling method. Individual smoking habits that are randomly assigned to the cohort members, together with the information on annual exposure to radon, is used as an input for the multistage model. Model parameters related to radon and tobacco exposure are fitted with a maximum-likelihood technique. We will show results of the observed and expected lung

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

Science.gov (United States)

de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

2018-03-01

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models, and a knowledge-based scoring system

Science.gov (United States)

Anantha M. Prasad; Louis R. Iverson; Stephen N. Matthews; Matthew P. Peters

2016-01-01

Context. No single model can capture the complex species range dynamics under changing climates--hence the need for a combination approach that addresses management concerns. Objective. A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model--DISTRIB II, a species colonization model--SHIFT, and...

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

Science.gov (United States)

Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Multi-stage IT project evaluation: The flexibility value obtained by implementing and resolving Berk, Green and Naik (2004) model

Science.gov (United States)

Abid, Fathi; Guermazi, Dorra

2009-11-01

In this paper, we evaluate a multi-stage information technology investment project, by implementing and resolving Berk, Green and Naik's (2004) model, which takes into account specific features of IT projects and considers the real option to suspend investment at each stage. We present a particular case of the model where the project value is the solution of an optimal control problem with a single state variable. In this case, the model is more intuitive and tractable. The case study confirms the practical potential of the model and highlights the importance of the real-option approach compared to classical discounted cash flow techniques in the valuation of IT projects.

Various multistage ensembles for prediction of heating energy consumption

Directory of Open Access Journals (Sweden)

Radisa Jovanovic

2015-04-01

Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

Study on multi-stage hydropyrolysis of coal in fixed-bed reactor

Energy Technology Data Exchange (ETDEWEB)

Wang, N.; Li, W.; Li, B.-Q. [Chinese Academy of Sciences, Taiyuan (China). State Key Lab of Coal Conversion

1999-07-01

The composition and quantity of the oil in hydropyrolysis (HyPy) and multi-stage HyPy with high and slow heating rate were compared and the effect of multistage HyPy process on desulfurization was investigated. Multistage HyPy of lignite and high sulphur coal were investigated and the effects of residence time, heating rate and pressure on product yields were studied. 6 refs., 4 figs., 2 tabs.

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Directory of Open Access Journals (Sweden)

Liogienė Tatjana

2016-07-01

Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models

NARCIS (Netherlands)

Boulaksil, Y.; Fransoo, J.C.; van Halm, E.N.G.

2009-01-01

This paper considers the problem of determining safety stocks in multi-item multi-stage inventory systems that face demand uncertainties. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to (demand) uncertainties and to achieve

Dimension of linear models

DEFF Research Database (Denmark)

Høskuldsson, Agnar

1996-01-01

Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....... of these criteria are widely used ones, while the remaining four are ones derived from the H-principle of mathematical modeling. Many examples from practice show that the criteria derived from the H-principle function better than the known and popular criteria for the number of components. We shall briefly review...

Dynamic solar-powered multi-stage direct contact membrane distillation system: Concept design, modeling and simulation

KAUST Repository

Lee, Jung Gil; Kim, Woo-Seung; Choi, June-Seok; Ghaffour, NorEddine; Kim, Young-Deuk

2017-01-01

This paper presents a theoretical analysis of the monthly average daily and hourly performances of a solar-powered multi-stage direct contact membrane distillation (SMDCMD) system with an energy recovery scheme and dynamic operating system. Mid

Ordinal Log-Linear Models for Contingency Tables

Directory of Open Access Journals (Sweden)

Brzezińska Justyna

2016-12-01

Full Text Available A log-linear analysis is a method providing a comprehensive scheme to describe the association for categorical variables in a contingency table. The log-linear model specifies how the expected counts depend on the levels of the categorical variables for these cells and provide detailed information on the associations. The aim of this paper is to present theoretical, as well as empirical, aspects of ordinal log-linear models used for contingency tables with ordinal variables. We introduce log-linear models for ordinal variables: linear-by-linear association, row effect model, column effect model and RC Goodman’s model. Algorithm, advantages and disadvantages will be discussed in the paper. An empirical analysis will be conducted with the use of R.

Parameterized Linear Longitudinal Airship Model

Science.gov (United States)

Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph

2010-01-01

A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics

Linear and non-linear autoregressive models for short-term wind speed forecasting

International Nuclear Information System (INIS)

Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.

2016-01-01

Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.

Analysis of multistage chains in public transport: The case of Quito, Ecuador

Energy Technology Data Exchange (ETDEWEB)

Bastidas Zelaya, E.

2016-07-01

Because of the growth of cities in size and population, people get used to perform several stage trips involving transfers due to advantages such as time or price paid, being multistage trips more attractive compared to single stage trips. In Quito, Ecuador, nowadays multistage trips represent one third of total daily trips. This paper seeks to identify main characteristics of multistage trips as well as find relationships and inferences that allow recommendations regarding best practices to policy makers and transport managers. The information used belong to the data collected in the Household Survey Mobility held in Quito in 2011. Based on these data, the present work starts using an analysis with descriptive statistics. The next phase of this research involves the search for a methodology in order to identify correlations between demographic, socioeconomic and transport variables related with traveler´s choice for making or not a transfer. Best methodology found was the use of Binary Logistic Regression (Logit) and specific computer software, with which different statistic's models were performed to find the strongest correlation. The paper ends with conclusions and recommendations as well as suggestions for future research. (Author)

Correlations and Non-Linear Probability Models

DEFF Research Database (Denmark)

Breen, Richard; Holm, Anders; Karlson, Kristian Bernt

2014-01-01

the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....

Generalized, Linear, and Mixed Models

CERN Document Server

McCulloch, Charles E; Neuhaus, John M

2011-01-01

An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m

Multivariate generalized linear mixed models using R

CERN Document Server

Berridge, Damon Mark

2011-01-01

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...

Modeling patterns in data using linear and related models

International Nuclear Information System (INIS)

Engelhardt, M.E.

1996-06-01

This report considers the use of linear models for analyzing data related to reliability and safety issues of the type usually associated with nuclear power plants. The report discusses some of the general results of linear regression analysis, such as the model assumptions and properties of the estimators of the parameters. The results are motivated with examples of operational data. Results about the important case of a linear regression model with one covariate are covered in detail. This case includes analysis of time trends. The analysis is applied with two different sets of time trend data. Diagnostic procedures and tests for the adequacy of the model are discussed. Some related methods such as weighted regression and nonlinear models are also considered. A discussion of the general linear model is also included. Appendix A gives some basic SAS programs and outputs for some of the analyses discussed in the body of the report. Appendix B is a review of some of the matrix theoretic results which are useful in the development of linear models

Numerical simulation and performance prediction in multi-stage submersible centrifugal pump

International Nuclear Information System (INIS)

Wang, W J; Li, G D; Wang, Y; Cui, Y R; Yin, G; Peng, S

2013-01-01

In order to study the inner flow field of multi-stage submersible centrifugal pump, the model named QD3-60/4-1.1 was selected. Steady turbulence characteristics of impellers, diffusers and return channel were calculated by Fluent software, the SIMPLEC algorithm and RNG κ-ε turbulence model with sliding mesh technology. Then, the distributions of pressure, velocity and Turbulence kinetic energy was obtained and the distributions of velocity field of a channel were analysed. The results show that the static pressure in impeller is increasing with the increasing of radius. The circumferential component of relative velocity is in the opposite direction of impeller rotating. At the same radius, the component value of pressure surface is larger than suction surface. With the increasing of flow rate, absolute velocity and relative velocity flow angle are becoming small, in opposite of the relative velocity and absolute velocity flow angle. The high turbulent zone of impeller is located in the gap of impellers and diffusers. Flow similarity and structure similarity of the multi-stage submersible pump are confirmed

The influence of the "cage" effect on the mechanism of reversible bimolecular multistage chemical reactions proceeding from different sites in solutions.

Science.gov (United States)

Doktorov, Alexander B

2016-08-28

Manifestations of the "cage" effect at the encounters of reactants have been theoretically treated on the example of multistage reactions (including bimolecular exchange reactions as elementary stages) proceeding from different active sites in liquid solutions. It is shown that for reactions occurring near the contact of reactants, consistent consideration of quasi-stationary kinetics of such multistage reactions (possible in the framework of the encounter theory only) can be made on the basis of chemical concepts of the "cage complex," just as in the case of one-site model described in the literature. Exactly as in the one-site model, the presence of the "cage" effect gives rise to new channels of reactant transformation that cannot result from elementary event of chemical conversion for the given reaction mechanism. Besides, the multisite model demonstrates new (as compared to one-site model) features of multistage reaction course.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

Science.gov (United States)

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh

The design of programme-controlled gain and linear pulse amplifier

International Nuclear Information System (INIS)

Guan Xuemei; Chen Chunkai; Northeast Normal Univ., Changchun; Qiao Shuang; Zhou Chuansheng

2006-01-01

The authors have designed a kind of new-style programme-controlled gain and linear pulse amplifier with accurate gausses of CR-RC-CR shaping circuit structure. The use of non-volatile digital electric potential device and accurate operational amplifier makes the circuit structure simple greatly, makes the ability stronger that resists assault. It can realize multistage gain in succession and make the drift of temperature low and make the linearity of pulse well. (authors)

Core seismic behaviour: linear and non-linear models

International Nuclear Information System (INIS)

Bernard, M.; Van Dorsselaere, M.; Gauvain, M.; Jenapierre-Gantenbein, M.

1981-08-01

The usual methodology for the core seismic behaviour analysis leads to a double complementary approach: to define a core model to be included in the reactor-block seismic response analysis, simple enough but representative of basic movements (diagrid or slab), to define a finer core model, with basic data issued from the first model. This paper presents the history of the different models of both kinds. The inert mass model (IMM) yielded a first rough diagrid movement. The direct linear model (DLM), without shocks and with sodium as an added mass, let to two different ones: DLM 1 with independent movements of the fuel and radial blanket subassemblies, and DLM 2 with a core combined movement. The non-linear (NLM) ''CORALIE'' uses the same basic modelization (Finite Element Beams) but accounts for shocks. It studies the response of a diameter on flats and takes into account the fluid coupling and the wrapper tube flexibility at the pad level. Damping consists of one modal part of 2% and one part due to shocks. Finally, ''CORALIE'' yields the time-history of the displacements and efforts on the supports, but damping (probably greater than 2%) and fluid-structures interaction are still to be precised. The validation experiments were performed on a RAPSODIE core mock-up on scale 1, in similitude of 1/3 as to SPX 1. The equivalent linear model (ELM) was developed for the SPX 1 reactor-block response analysis and a specified seismic level (SB or SM). It is composed of several oscillators fixed to the diagrid and yields the same maximum displacements and efforts than the NLM. The SPX 1 core seismic analysis with a diagrid input spectrum which corresponds to a 0,1 g group acceleration, has been carried out with these models: some aspects of these calculations are presented here

Multi-stage decoding for multi-level block modulation codes

Science.gov (United States)

Lin, Shu

1991-01-01

In this paper, we investigate various types of multi-stage decoding for multi-level block modulation codes, in which the decoding of a component code at each stage can be either soft-decision or hard-decision, maximum likelihood or bounded-distance. Error performance of codes is analyzed for a memoryless additive channel based on various types of multi-stage decoding, and upper bounds on the probability of an incorrect decoding are derived. Based on our study and computation results, we find that, if component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. In particular, we find that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum decoding of the overall code is very small: only a fraction of dB loss in SNR at the probability of an incorrect decoding for a block of 10(exp -6). Multi-stage decoding of multi-level modulation codes really offers a way to achieve the best of three worlds, bandwidth efficiency, coding gain, and decoding complexity.

Linear Logistic Test Modeling with R

Science.gov (United States)

Baghaei, Purya; Kubinger, Klaus D.

2015-01-01

The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…

Explorative methods in linear models

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2004-01-01

The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different feat...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....

Sparse Linear Identifiable Multivariate Modeling

DEFF Research Database (Denmark)

Henao, Ricardo; Winther, Ole

2011-01-01

and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...

Immunogenicity and in vitro Protective Efficacy of a Recombinant Multistage Plasmodium falciparum Candidate Vaccine

Science.gov (United States)

Shi, Ya Ping; Hasnain, Seyed E.; Sacci, John B.; Holloway, Brian P.; Fujioka, Hisashi; Kumar, Nirbhay; Wohlhueter, Robert; Hoffman, Stephen L.; Collins, William E.; Lal, Altaf A.

1999-02-01

Compared with a single-stage antigen-based vaccine, a multistage and multivalent Plasmodium falciparum vaccine would be more efficacious by inducing "multiple layers" of immunity. We have constructed a synthetic gene that encodes for 12 B cell, 6 T cell proliferative, and 3 cytotoxic T lymphocyte epitopes derived from 9 stage-specific P. falciparum antigens corresponding to the sporozoite, liver, erythrocytic asexual, and sexual stages. The gene was expressed in the baculovirus system, and a 41-kDa antigen, termed CDC/NIIMALVAC-1, was purified. Immunization in rabbits with the purified protein in the presence of different adjuvants generated antibody responses that recognized vaccine antigen, linear peptides contained in the vaccine, and all stages of P. falciparum. In vitro assays of protection revealed that the vaccine-elicited antibodies strongly inhibited sporozoite invasion of hepatoma cells and growth of blood-stage parasites in the presence of monocytes. These observations demonstrate that a multicomponent, multistage malaria vaccine can induce immune responses that inhibit parasite development at multiple stages. The rationale and approach used in the development of a multicomponent P. falciparum vaccine will be useful in the development of a multispecies human malaria vaccine and vaccines against other infectious diseases.

Latent log-linear models for handwritten digit classification.

Science.gov (United States)

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Equivalent linear damping characterization in linear and nonlinear force-stiffness muscle models.

Science.gov (United States)

Ovesy, Marzieh; Nazari, Mohammad Ali; Mahdavian, Mohammad

2016-02-01

In the current research, the muscle equivalent linear damping coefficient which is introduced as the force-velocity relation in a muscle model and the corresponding time constant are investigated. In order to reach this goal, a 1D skeletal muscle model was used. Two characterizations of this model using a linear force-stiffness relationship (Hill-type model) and a nonlinear one have been implemented. The OpenSim platform was used for verification of the model. The isometric activation has been used for the simulation. The equivalent linear damping and the time constant of each model were extracted by using the results obtained from the simulation. The results provide a better insight into the characteristics of each model. It is found that the nonlinear models had a response rate closer to the reality compared to the Hill-type models.

From linear to generalized linear mixed models: A case study in repeated measures

Science.gov (United States)

Compared to traditional linear mixed models, generalized linear mixed models (GLMMs) can offer better correspondence between response variables and explanatory models, yielding more efficient estimates and tests in the analysis of data from designed experiments. Using proportion data from a designed...

Extended Linear Models with Gaussian Priors

DEFF Research Database (Denmark)

Quinonero, Joaquin

2002-01-01

In extended linear models the input space is projected onto a feature space by means of an arbitrary non-linear transformation. A linear model is then applied to the feature space to construct the model output. The dimension of the feature space can be very large, or even infinite, giving the model...... a very big flexibility. Support Vector Machines (SVM's) and Gaussian processes are two examples of such models. In this technical report I present a model in which the dimension of the feature space remains finite, and where a Bayesian approach is used to train the model with Gaussian priors...... on the parameters. The Relevance Vector Machine, introduced by Tipping, is a particular case of such a model. I give the detailed derivations of the expectation-maximisation (EM) algorithm used in the training. These derivations are not found in the literature, and might be helpful for newcomers....

Linear mixed models for longitudinal data

CERN Document Server

Molenberghs, Geert

2000-01-01

This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commerc...

Using multistage models to describe radiation-induced leukaemia

International Nuclear Information System (INIS)

Little, M.P.; Muirhead, C.R.; Boice, J.D. Jr.; Kleinerman, R.A.

1995-01-01

The Armitage-Doll model of carcinogenesis is fitted to data on leukaemia mortality among the Japanese atomic bomb survivors with the DS86 dosimetry and on leukaemia incidence in the International Radiation Study of Cervical Cancer patients. Two different forms of model are fitted: the first postulates up to two radiation-affected stages and the second additionally allows for the presence at birth of a non-trivial population of cells which have already accumulated the first of the mutations leading to malignancy. Among models of the first form, a model with two adjacent radiation-affected stages appears to fit the data better than other models of the first form, including both models with two affected stages in any order and models with only one affected stage. The best fitting model predicts a linear-quadratic dose-response and reductions of relative risk with increasing time after exposure and age at exposure, in agreement with what has previously been observed in the Japanese and cervical cancer data. However, on the whole it does not provide an adequate fit to either dataset. The second form of model appears to provide a rather better fit, but the optimal models have biologically implausible parameters (the number of initiated cells at birth is negative) so that this model must also be regarded as providing an unsatisfactory description of the data. (author)

Multi-stage crypto ransomware attacks: A new emerging cyber threat to critical infrastructure and industrial control systems

Directory of Open Access Journals (Sweden)

Aaron Zimba

2018-03-01

Full Text Available The inevitable integration of critical infrastructure to public networks has exposed the underlying industrial control systems to various attack vectors. In this paper, we model multi-stage crypto ransomware attacks, which are today an emerging cyber threat to critical infrastructure. We evaluate our modeling approach using multi-stage attacks by the infamous WannaCry ransomware. The static malware analysis results uncover the techniques employed by the ransomware to discover vulnerable nodes in different SCADA and production subnets, and for the subsequent network propagation. Based on the uncovered artifacts, we recommend a cascaded network segmentation approach, which prioritizes the security of production network devices. Keywords: Critical infrastructure, Cyber-attack, Industrial control system, Crypto ransomware, Vulnerability

Non-linear finite element modeling

DEFF Research Database (Denmark)

Mikkelsen, Lars Pilgaard

The note is written for courses in "Non-linear finite element method". The note has been used by the author teaching non-linear finite element modeling at Civil Engineering at Aalborg University, Computational Mechanics at Aalborg University Esbjerg, Structural Engineering at the University...

Venture Investment Incentive Mechanisms and Simulation with Venture Entrepreneurs Having Multistage Efforts Based on Fairness Preference Theory

Directory of Open Access Journals (Sweden)

Kaihong Wang

2016-01-01

Full Text Available When venture capital has been invested into venture companies, venture capitalists and venture entrepreneurs form a principal-agent relationship. Take into account the fact that the venture entrepreneur’s effort is a long process, because the effort is not the same at different stage. Therefore, efforts variables are seen as the multistage dynamic variable, and venture investment principal-agent model with venture entrepreneurs having multistage efforts is constructed on the basis of the classic principal-agent theory in the paper. Further, in the later stage effort of venture entrepreneurs is affected by the size of prestage benefit with venture capitalists and venture entrepreneurs; thus the fairness preference model is improved, and venture investment principal-agent model with venture entrepreneurs having multistage efforts is constructed on the basis of fairness preference theory. Both theoretical derivation and simulation have demonstrated that, under the condition of information asymmetry, if the fairness preference of venture entrepreneurs holds, then (1 venture capitalists provide venture entrepreneurs with level higher than that without fairness preference, (2 in every single stage venture entrepreneurs make efforts higher than those without fairness preference, and (3 in two periods both venture investors and venture entrepreneurs gain total real gains higher than those in two periods without fair preference.

Multi-Stage System for Automatic Target Recognition

Science.gov (United States)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

Multi-stage decoding of multi-level modulation codes

Science.gov (United States)

Lin, Shu; Kasami, Tadao; Costello, Daniel J., Jr.

1991-01-01

Various types of multi-stage decoding for multi-level modulation codes are investigated. It is shown that if the component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. Particularly, it is shown that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum soft-decision decoding of the code is very small, only a fraction of dB loss in signal to noise ratio at a bit error rate (BER) of 10(exp -6).

SU-E-T-480: Radiobiological Dose Comparison of Single Fraction SRS, Multi-Fraction SRT and Multi-Stage SRS of Large Target Volumes Using the Linear-Quadratic Formula

International Nuclear Information System (INIS)

Ding, C; Hrycushko, B; Jiang, S; Meyer, J; Timmerman, R

2014-01-01

Purpose: To compare the radiobiological effect on large tumors and surrounding normal tissues from single fraction SRS, multi-fractionated SRT, and multi-staged SRS treatment. Methods: An anthropomorphic head phantom with a centrally located large volume target (18.2 cm 3 ) was scanned using a 16 slice large bore CT simulator. Scans were imported to the Multiplan treatment planning system where a total prescription dose of 20Gy was used for a single, three staged and three fractionated treatment. Cyber Knife treatment plans were inversely optimized for the target volume to achieve at least 95% coverage of the prescription dose. For the multistage plan, the target was segmented into three subtargets having similar volume and shape. Staged plans for individual subtargets were generated based on a planning technique where the beam MUs of the original plan on the total target volume are changed by weighting the MUs based on projected beam lengths within each subtarget. Dose matrices for each plan were export in DICOM format and used to calculate equivalent dose distributions in 2Gy fractions using an alpha beta ratio of 10 for the target and 3 for normal tissue. Results: Singe fraction SRS, multi-stage plan and multi-fractionated SRT plans had an average 2Gy dose equivalent to the target of 62.89Gy, 37.91Gy and 33.68Gy, respectively. The normal tissue within 12Gy physical dose region had an average 2Gy dose equivalent of 29.55Gy, 16.08Gy and 13.93Gy, respectively. Conclusion: The single fraction SRS plan had the largest predicted biological effect for the target and the surrounding normal tissue. The multi-stage treatment provided for a more potent biologically effect on target compared to the multi-fraction SRT treatments with less biological normal tissue than single-fraction SRS treatment

A Methodology for Optimization in Multistage Industrial Processes: A Pilot Study

Directory of Open Access Journals (Sweden)

Piotr Jarosz

2015-01-01

Full Text Available The paper introduces a methodology for optimization in multistage industrial processes with multiple quality criteria. Two ways of formulation of optimization problem and four different approaches to solve the problem are considered. Proposed methodologies were tested first on a virtual process described by benchmark functions and next were applied in optimization of multistage lead refining process.

Optimization of material flow in the nuclear fuel cycle using a cyclic multi-stage production-to-inventory model

International Nuclear Information System (INIS)

DePorter, E.L.

1977-01-01

The nuclear fuel cycle is modelled as a cyclic, multi-stage production-to-inventory system. The objective is to meet a known deterministic demand for energy while minimizing acquisition, production, and inventory holding costs for all stages of the fuel cycle. The model allows for cyclic flow (feedback) of materials, material flow conversion factors at each stage, production lag times at each stage, and for escalating costs of uranium ore. It does not allow shortages to occur in inventories. The model is optimized by the application of the calculus of variations and specifically through recently developed theorems on the solution of functionals constrained by inequalities. The solution is a set of optimal cumulative production trajectories which define the stagewise production rates. Analysis of these production rates reveals the optimal nuclear fuel cycle costs and that inventories (stockpiles) occur in uranium fields, enriched uranium hexafluoride, and fabricated fuel assemblies. An analysis of the sensitivity of the model to variation in three important parameters is performed

linear-quadratic-linear model

Directory of Open Access Journals (Sweden)

Tanwiwat Jaikuna

2017-02-01

Full Text Available Purpose: To develop an in-house software program that is able to calculate and generate the biological dose distribution and biological dose volume histogram by physical dose conversion using the linear-quadratic-linear (LQL model. Material and methods : The Isobio software was developed using MATLAB version 2014b to calculate and generate the biological dose distribution and biological dose volume histograms. The physical dose from each voxel in treatment planning was extracted through Computational Environment for Radiotherapy Research (CERR, and the accuracy was verified by the differentiation between the dose volume histogram from CERR and the treatment planning system. An equivalent dose in 2 Gy fraction (EQD2 was calculated using biological effective dose (BED based on the LQL model. The software calculation and the manual calculation were compared for EQD2 verification with pair t-test statistical analysis using IBM SPSS Statistics version 22 (64-bit. Results: Two and three-dimensional biological dose distribution and biological dose volume histogram were displayed correctly by the Isobio software. Different physical doses were found between CERR and treatment planning system (TPS in Oncentra, with 3.33% in high-risk clinical target volume (HR-CTV determined by D90%, 0.56% in the bladder, 1.74% in the rectum when determined by D2cc, and less than 1% in Pinnacle. The difference in the EQD2 between the software calculation and the manual calculation was not significantly different with 0.00% at p-values 0.820, 0.095, and 0.593 for external beam radiation therapy (EBRT and 0.240, 0.320, and 0.849 for brachytherapy (BT in HR-CTV, bladder, and rectum, respectively. Conclusions : The Isobio software is a feasible tool to generate the biological dose distribution and biological dose volume histogram for treatment plan evaluation in both EBRT and BT.

Statistical Tests for Mixed Linear Models

CERN Document Server

Khuri, André I; Sinha, Bimal K

2011-01-01

An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random effects. This volume covers the most important research of the past decade as well as the latest developments in hypothesis testing. It compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive treatment for these models a

Lymphoma diagnosis in histopathology using a multi-stage visual learning approach

Science.gov (United States)

Codella, Noel; Moradi, Mehdi; Matasar, Matt; Sveda-Mahmood, Tanveer; Smith, John R.

2016-03-01

This work evaluates the performance of a multi-stage image enhancement, segmentation, and classification approach for lymphoma recognition in hematoxylin and eosin (H and E) stained histopathology slides of excised human lymph node tissue. In the first stage, the original histology slide undergoes various image enhancement and segmentation operations, creating an additional 5 images for every slide. These new images emphasize unique aspects of the original slide, including dominant staining, staining segmentations, non-cellular groupings, and cellular groupings. For the resulting 6 total images, a collection of visual features are extracted from 3 different spatial configurations. Visual features include the first fully connected layer (4096 dimensions) of the Caffe convolutional neural network trained from ImageNet data. In total, over 200 resultant visual descriptors are extracted for each slide. Non-linear SVMs are trained over each of the over 200 descriptors, which are then input to a forward stepwise ensemble selection that optimizes a late fusion sum of logistically normalized model outputs using local hill climbing. The approach is evaluated on a public NIH dataset containing 374 images representing 3 lymphoma conditions: chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL). Results demonstrate a 38.4% reduction in residual error over the current state-of-art on this dataset.

Matrix Tricks for Linear Statistical Models

CERN Document Server

Puntanen, Simo; Styan, George PH

2011-01-01

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and

An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

This technical note documents the equations for primal-dual interior-point quadratic programming problem solver used for MPC. The algorithm exploits the special structure of the MPC problem and is able to reduce the computational burden such that the computational burden scales with prediction...... horizon length in a linear way rather than cubic, which would be the case if the structure was not exploited. It is also shown how models used for design of model-based controllers, e.g. linear quadratic and model predictive, can be linearized both at equilibrium and non-equilibrium points, making...

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Applicability of linear and non-linear potential flow models on a Wavestar float

DEFF Research Database (Denmark)

Bozonnet, Pauline; Dupin, Victor; Tona, Paolino

2017-01-01

as a model based on non-linear potential flow theory and weakscatterer hypothesis are successively considered. Simple tests, such as dip tests, decay tests and captive tests enable to highlight the improvements obtained with the introduction of nonlinearities. Float motion under wave actions and without...... control action, limited to small amplitude motion with a single float, is well predicted by the numerical models, including the linear one. Still, float velocity is better predicted by accounting for non-linear hydrostatic and Froude-Krylov forces.......Numerical models based on potential flow theory, including different types of nonlinearities are compared and validated against experimental data for the Wavestar wave energy converter technology. Exact resolution of the rotational motion, non-linear hydrostatic and Froude-Krylov forces as well...

Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-linear models

Directory of Open Access Journals (Sweden)

Masudul Islam

2012-10-01

Full Text Available Prior information about a financial market is very essential for investor to invest money on parches share from the stock market which can strengthen the economy. The study examines the relative ability of various models to forecast daily stock indexes future volatility. The forecasting models that employed from simple to relatively complex ARCH-class models. It is found that among linear models of stock indexes volatility, the moving average model ranks first using root mean square error, mean absolute percent error, Theil-U and Linex loss function  criteria. We also examine five nonlinear models. These models are ARCH, GARCH, EGARCH, TGARCH and restricted GARCH models. We find that nonlinear models failed to dominate linear models utilizing different error measurement criteria and moving average model appears to be the best. Then we forecast the next two months future stock index price volatility by the best (moving average model.

Validity and reliability of an application review process using dedicated reviewers in one stage of a multi-stage admissions model.

Science.gov (United States)

Zeeman, Jacqueline M; McLaughlin, Jacqueline E; Cox, Wendy C

2017-11-01

With increased emphasis placed on non-academic skills in the workplace, a need exists to identify an admissions process that evaluates these skills. This study assessed the validity and reliability of an application review process involving three dedicated application reviewers in a multi-stage admissions model. A multi-stage admissions model was utilized during the 2014-2015 admissions cycle. After advancing through the academic review, each application was independently reviewed by two dedicated application reviewers utilizing a six-construct rubric (written communication, extracurricular and community service activities, leadership experience, pharmacy career appreciation, research experience, and resiliency). Rubric scores were extrapolated to a three-tier ranking to select candidates for on-site interviews. Kappa statistics were used to assess interrater reliability. A three-facet Many-Facet Rasch Model (MFRM) determined reviewer severity, candidate suitability, and rubric construct difficulty. The kappa statistic for candidates' tier rank score (n = 388 candidates) was 0.692 with a perfect agreement frequency of 84.3%. There was substantial interrater reliability between reviewers for the tier ranking (kappa: 0.654-0.710). Highest construct agreement occurred in written communication (kappa: 0.924-0.984). A three-facet MFRM analysis explained 36.9% of variance in the ratings, with 0.06% reflecting application reviewer scoring patterns (i.e., severity or leniency), 22.8% reflecting candidate suitability, and 14.1% reflecting construct difficulty. Utilization of dedicated application reviewers and a defined tiered rubric provided a valid and reliable method to effectively evaluate candidates during the application review process. These analyses provide insight into opportunities for improving the application review process among schools and colleges of pharmacy. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparing linear probability model coefficients across groups

DEFF Research Database (Denmark)

Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt

2015-01-01

of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate......This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....

Influence of spatial beam inhomogeneities on the parameters of a petawatt laser system based on multi-stage parametric amplification

International Nuclear Information System (INIS)

Frolov, S A; Trunov, V I; Pestryakov, Efim V; Leshchenko, V E

2013-01-01

We have developed a technique for investigating the evolution of spatial inhomogeneities in high-power laser systems based on multi-stage parametric amplification. A linearised model of the inhomogeneity development is first devised for parametric amplification with the small-scale self-focusing taken into account. It is shown that the application of this model gives the results consistent (with high accuracy and in a wide range of inhomogeneity parameters) with the calculation without approximations. Using the linearised model, we have analysed the development of spatial inhomogeneities in a petawatt laser system based on multi-stage parametric amplification, developed at the Institute of Laser Physics, Siberian Branch of the Russian Academy of Sciences (ILP SB RAS). (control of laser radiation parameters)

Determining quantitative road safety targets by applying statistical prediction techniques and a multi-stage adjustment procedure.

Science.gov (United States)

Wittenberg, P; Sever, K; Knoth, S; Sahin, N; Bondarenko, J

2013-01-01

Due to substantial progress made in road safety in the last ten years, the European Union (EU) renewed the ambitious agreement of halving the number of persons killed on the roads within the next decade. In this paper we develop a method that aims at finding an optimal target for each nation, in terms of being as achievable as possible, and with the cumulative EU target being reached. Targets as an important component in road safety policy are given as reduction rate or as absolute number of road traffic deaths. Determination of these quantitative road safety targets (QRST) is done by a top-down approach, formalized in a multi-stage adjustment procedure. Different QRST are derived under consideration of recent research. The paper presents a method to break the national target further down to regional targets in case of the German Federal States. Generalized linear models are fitted to data in the period 1991-2010. Our model selection procedure chooses various models for the EU and solely log-linear models for the German Federal States. If the proposed targets for the EU Member States are attained, the sum of fatalities should not exceed the total value of 15,465 per year by 2020. Both, the mean level and the range of mortality rates within the EU could be lowered from 28-113 in 2010 to 17-41 per million inhabitants in 2020. This study provides an alternative to the determination of safety targets by political commitments only, taking the history of road fatalities trends and population into consideration. Copyright © 2012 Elsevier Ltd. All rights reserved.

Handling Imbalanced Data Sets in Multistage Classification

Science.gov (United States)

López, M.

Multistage classification is a logical approach, based on a divide-and-conquer solution, for dealing with problems with a high number of classes. The classification problem is divided into several sequential steps, each one associated to a single classifier that works with subgroups of the original classes. In each level, the current set of classes is split into smaller subgroups of classes until they (the subgroups) are composed of only one class. The resulting chain of classifiers can be represented as a tree, which (1) simplifies the classification process by using fewer categories in each classifier and (2) makes it possible to combine several algorithms or use different attributes in each stage. Most of the classification algorithms can be biased in the sense of selecting the most populated class in overlapping areas of the input space. This can degrade a multistage classifier performance if the training set sample frequencies do not reflect the real prevalence in the population. Several techniques such as applying prior probabilities, assigning weights to the classes, or replicating instances have been developed to overcome this handicap. Most of them are designed for two-class (accept-reject) problems. In this article, we evaluate several of these techniques as applied to multistage classification and analyze how they can be useful for astronomy. We compare the results obtained by classifying a data set based on Hipparcos with and without these methods.

Testing Parametric versus Semiparametric Modelling in Generalized Linear Models

NARCIS (Netherlands)

Härdle, W.K.; Mammen, E.; Müller, M.D.

1996-01-01

We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e.

Information Overload in Multi-Stage Selection Procedures

NARCIS (Netherlands)

S.S. Ficco (Stefano); V.A. Karamychev (Vladimir)

2004-01-01

textabstractThe paper studies information processing imperfections in a fully rational decision-making network. It is shown that imperfect information transmission and imperfect information acquisition in a multi-stage selection game yield information overload. The paper analyses the mechanisms

Configuration of management accounting information system for multi-stage manufacturing

Science.gov (United States)

Mkrtychev, S. V.; Ochepovsky, A. V.; Enik, O. A.

2018-05-01

The article presents an approach to configuration of a management accounting information system (MAIS) that provides automated calculations and the registration of normative production losses in multi-stage manufacturing. The use of MAIS with the proposed configuration at the enterprises of textile and woodworking industries made it possible to increase the accuracy of calculations for normative production losses and to organize accounting thereof with the reference to individual stages of the technological process. Thus, high efficiency of multi-stage manufacturing control is achieved.

Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems

Directory of Open Access Journals (Sweden)

Bambang Riyanto

2005-11-01

Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.

A multistage framework for reliability-based distribution expansion planning considering distributed generations by a self-adaptive global-based harmony search algorithm

International Nuclear Information System (INIS)

Shivaie, Mojtaba; Ameli, Mohammad T.; Sepasian, Mohammad S.; Weinsier, Philip D.; Vahidinasab, Vahid

2015-01-01

In this paper, the authors present a new multistage framework for reliability-based Distribution Expansion Planning (DEP) in which expansion options are a reinforcement and/or installation of substations, feeders, and Distributed Generations (DGs). The proposed framework takes into account not only costs associated with investment, maintenance, and operation, but also expected customer interruption cost in the optimization as four problem objectives. At the same time, operational restrictions, Kirchhoff's laws, radial structure limitation, voltage limits, and capital expenditure budget restriction are considered as problem constraints. The proposed model is a non-convex optimization problem having a non-linear, mixed-integer nature. Hence, a hybrid Self-adaptive Global-based Harmony Search Algorithm (SGHSA) and Optimal Power Flow (OPF) were used and followed by a fuzzy satisfying method in order to obtain the final optimal solution. The SGHSA is a recently developed optimization algorithm which imitates the music improvisation process. In this process, the harmonists improvise their instrument pitches, searching for the perfect state of harmony. The planning methodology was demonstrated on the 27-node, 13.8-kV test system in order to demonstrate the feasibility and capability of the proposed model. Simulation results illustrated the sufficiency and profitableness of the newly developed framework, when compared with other methods. - Highlights: • A new multistage framework is presented for reliability-based DEP problem. • In this paper, DGs are considered as an expansion option to increase the flexibility of the proposed model. • In this paper, effective factors of DEP problem are incorporated as a multi-objective model. • In this paper, three new algorithms HSA, IHSA and SGHSA are proposed. • Results obtained by the proposed SGHSA algorithm are better than others

Experience in North America Tight Oil Reserves Development. Horizontal Wells and Multistage Hydraulic Fracturing

Directory of Open Access Journals (Sweden)

R.R. Ibatullin

2017-09-01

Full Text Available The accelerated development of horizontal drilling technology in combination with the multistage hydraulic fracturing of the reservoir has expanded the geological conditions for commercial oil production from tight reservoirs in North America. Geological and physical characteristics of tight reservoirs in North America are presented, as well as a comparison of the geological and physical properties of the reservoirs of the Western Canadian Sedimentary Basin and the Volga-Ural oil and gas province, in particular, in the territory of Tatarstan. The similarity of these basins is shown in terms of formation and deposition. New drilling technologies for horizontal wells (HW and multistage hydraulic fracturing are considered. The drilling in tight reservoirs is carried out exclusively on hydrocarbon-based muds The multi-stage fracturing technology with the use of sliding sleeves, and also slick water – a low-viscous carrier for proppant is the most effective solution for conditions similar to tight reservoirs in the Devonian formation of Tatarstan. Tax incentives which are actively used for the development of HW and multistage fracturing technologies in Canada are described. wells, multistage fracturing

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

Unsteady Aerodynamics & Aeromechanics of Multi-Stage Turbomachinery Blading

National Research Council Canada - National Science Library

Fleeter, Sanford

2002-01-01

.... A benchmark-standard multistage transonic research compressor was developed by modifying the Purdue High-Speed Axial Compressor to feature new IGV and stator rows representative of modern high pressure compressors...

A multi-stage traveling-wave thermoacoustically-driven refrigeration system operating at liquefied natural gas temperature

Science.gov (United States)

Luo, K.; Sun, D. M.; Zhang, J.; Shen, Q.; Zhang, N.

2017-12-01

This study proposes a multi-stage travelling-wave thermoacoustically refrigeration system (TAD-RS) operating at liquefied natural gas temperature, which consists of two thermoacoustic engines (TAE) and one thermoacoustic refrigerator (TAR) in a closed-loop configuration. Three thermoacoustic units connect each other through a resonance tube of small cross-sectional area, achieving “self-matching” for efficient thermoacoustic conversion. Based on the linear thermoacoustic theory, a model of the proposed system has been built by using DeltaEC program to show the acoustic field characteristics and performance. It is shown that with pressurized 5 MPa helium as working gas, the TAEs are able to build a stable and strong acoustic field with a frequency of about 85 Hz. When hot end temperature reaches 923 K, this system can provide about 1410 W cooling power at 110 K with an overall exergy efficiency of 15.5%. This study indicates a great application prospect of TAD-RS in the field of natural gas liquefaction with a large cooling capacity and simple structure.

Composite Linear Models | Division of Cancer Prevention

Science.gov (United States)

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

Multistage feature extraction for accurate face alignment

NARCIS (Netherlands)

Zuo, F.; With, de P.H.N.

2004-01-01

We propose a novel multistage facial feature extraction approach using a combination of 'global' and 'local' techniques. At the first stage, we use template matching, based on an Edge-Orientation-Map for fast feature position estimation. Using this result, a statistical framework applying the Active

Artificial neural network cardiopulmonary modeling and diagnosis

Science.gov (United States)

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.

Actuarial statistics with generalized linear mixed models

NARCIS (Netherlands)

Antonio, K.; Beirlant, J.

2007-01-01

Over the last decade the use of generalized linear models (GLMs) in actuarial statistics has received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh and Nelder [McCullagh, P., Nelder, J.A., 1989. Generalized linear models. In: Monographs on Statistics

Heterotic sigma models and non-linear strings

International Nuclear Information System (INIS)

Hull, C.M.

1986-01-01

The two-dimensional supersymmetric non-linear sigma models are examined with respect to the heterotic string. The paper was presented at the workshop on :Supersymmetry and its applications', Cambridge, United Kingdom, 1985. The non-linear sigma model with Wess-Zumino-type term, the coupling of the fermionic superfields to the sigma model, super-conformal invariance, and the supersymmetric string, are all discussed. (U.K.)

Comparison of linear and non-linear models for the adsorption of fluoride onto geo-material: limonite.

Science.gov (United States)

Sahin, Rubina; Tapadia, Kavita

2015-01-01

The three widely used isotherms Langmuir, Freundlich and Temkin were examined in an experiment using fluoride (F⁻) ion adsorption on a geo-material (limonite) at four different temperatures by linear and non-linear models. Comparison of linear and non-linear regression models were given in selecting the optimum isotherm for the experimental results. The coefficient of determination, r², was used to select the best theoretical isotherm. The four Langmuir linear equations (1, 2, 3, and 4) are discussed. Langmuir isotherm parameters obtained from the four Langmuir linear equations using the linear model differed but they were the same when using the nonlinear model. Langmuir-2 isotherm is one of the linear forms, and it had the highest coefficient of determination (r² = 0.99) compared to the other Langmuir linear equations (1, 3 and 4) in linear form, whereas, for non-linear, Langmuir-4 fitted best among all the isotherms because it had the highest coefficient of determination (r² = 0.99). The results showed that the non-linear model may be a better way to obtain the parameters. In the present work, the thermodynamic parameters show that the absorption of fluoride onto limonite is both spontaneous (ΔG 0). Scanning electron microscope and X-ray diffraction images also confirm the adsorption of F⁻ ion onto limonite. The isotherm and kinetic study reveals that limonite can be used as an adsorbent for fluoride removal. In future we can develop new technology for fluoride removal in large scale by using limonite which is cost-effective, eco-friendly and is easily available in the study area.

Modeling of changes in particle size distribution of solids in multistage separation systems

Directory of Open Access Journals (Sweden)

Lagereva E.A.

2016-09-01

Full Text Available The presented method of calculation of the separation of solid particles from gas streams to multistage separation sys-tems, consisting of a number of sequentially installed separational devices of various design and principle of operation. It is based on a separate analysis of the sequential processes of capture and transmission of individual fractions of solid particles of a polydisperse structure. The technique provides information about changes in particle size distribution of solids with the passage of the gas flow in the treatment system and allows you to specifically select the effective combination of different types of separators.

Multi-stage volcanic island flank collapses with coeval explosive caldera-forming eruptions.

Science.gov (United States)

Hunt, James E; Cassidy, Michael; Talling, Peter J

2018-01-18

Volcanic flank collapses and explosive eruptions are among the largest and most destructive processes on Earth. Events at Mount St. Helens in May 1980 demonstrated how a relatively small (300 km 3 ), but can also occur in complex multiple stages. Here, we show that multistage retrogressive landslides on Tenerife triggered explosive caldera-forming eruptions, including the Diego Hernandez, Guajara and Ucanca caldera eruptions. Geochemical analyses were performed on volcanic glasses recovered from marine sedimentary deposits, called turbidites, associated with each individual stage of each multistage landslide. These analyses indicate only the lattermost stages of subaerial flank failure contain materials originating from respective coeval explosive eruption, suggesting that initial more voluminous submarine stages of multi-stage flank collapse induce these aforementioned explosive eruption. Furthermore, there are extended time lags identified between the individual stages of multi-stage collapse, and thus an extended time lag between the initial submarine stages of failure and the onset of subsequent explosive eruption. This time lag succeeding landslide-generated static decompression has implications for the response of magmatic systems to un-roofing and poses a significant implication for ocean island volcanism and civil emergency planning.

Non linear viscoelastic models

DEFF Research Database (Denmark)

Agerkvist, Finn T.

2011-01-01

Viscoelastic eects are often present in loudspeaker suspensions, this can be seen in the displacement transfer function which often shows a frequency dependent value below the resonance frequency. In this paper nonlinear versions of the standard linear solid model (SLS) are investigated....... The simulations show that the nonlinear version of the Maxwell SLS model can result in a time dependent small signal stiness while the Kelvin Voight version does not....

Some design aspects of multistage flash distillation process

International Nuclear Information System (INIS)

Ahmad, Mohammad.

1975-01-01

The purpose of this paper is to examine the effect of the design variables of multistage flash (MSF) process on the performance and/or the cost of the desalting plant, and to establish certain design trends

Genetic parameters for racing records in trotters using linear and generalized linear models.

Science.gov (United States)

Suontama, M; van der Werf, J H J; Juga, J; Ojala, M

2012-09-01

Heritability and repeatability and genetic and phenotypic correlations were estimated for trotting race records with linear and generalized linear models using 510,519 records on 17,792 Finnhorses and 513,161 records on 25,536 Standardbred trotters. Heritability and repeatability were estimated for single racing time and earnings traits with linear models, and logarithmic scale was used for racing time and fourth-root scale for earnings to correct for nonnormality. Generalized linear models with a gamma distribution were applied for single racing time and with a multinomial distribution for single earnings traits. In addition, genetic parameters for annual earnings were estimated with linear models on the observed and fourth-root scales. Racing success traits of single placings, winnings, breaking stride, and disqualifications were analyzed using generalized linear models with a binomial distribution. Estimates of heritability were greatest for racing time, which ranged from 0.32 to 0.34. Estimates of heritability were low for single earnings with all distributions, ranging from 0.01 to 0.09. Annual earnings were closer to normal distribution than single earnings. Heritability estimates were moderate for annual earnings on the fourth-root scale, 0.19 for Finnhorses and 0.27 for Standardbred trotters. Heritability estimates for binomial racing success variables ranged from 0.04 to 0.12, being greatest for winnings and least for breaking stride. Genetic correlations among racing traits were high, whereas phenotypic correlations were mainly low to moderate, except correlations between racing time and earnings were high. On the basis of a moderate heritability and moderate to high repeatability for racing time and annual earnings, selection of horses for these traits is effective when based on a few repeated records. Because of high genetic correlations, direct selection for racing time and annual earnings would also result in good genetic response in racing success.

Generalised linear models for correlated pseudo-observations, with applications to multi-state models

DEFF Research Database (Denmark)

Andersen, Per Kragh; Klein, John P.; Rosthøj, Susanne

2003-01-01

Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model......Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model...

Numerical simulations of single and multi-staged injection of H2 in a supersonic scramjet combustor

Directory of Open Access Journals (Sweden)

L. Abu-Farah

2014-12-01

Full Text Available Computational fluid dynamics (CFD simulations of a single staged injection of H2 through a central wedge shaped strut and a multi-staged injection through wall injectors are carried out by using Ansys CFX-12 code. Unstructured tetrahedral grids for narrow channel and quarter geometries of the combustor are generated by using ICEM CFD. Steady three-dimensional (3D Reynolds-averaged Navier-stokes (RANS simulations are carried out in the case of no H2 injection and compared with the simulations of single staged pilot and/or main H2 injections and multistage injection. Shear stress transport (SST based on k-ω turbulent model is adopted. Flow field visualization (complex shock waves interactions and static pressure distribution along the wall of the combustor are predicted and compared with the experimental schlieren images and measured wall static pressures for validation. A good agreement is found between the CFD predicted results and the measured data. The narrow and quarter geometries of the combustor give similar results with very small differences. Multi-staged injections of H2 enhance the turbulent H2/air mixing by forming vortices and additional shock waves (bow shocks.

Solvent-assisted multistage nonequilibrium electron transfer in rigid supramolecular systems: Diabatic free energy surfaces and algorithms for numerical simulations

Science.gov (United States)

Feskov, Serguei V.; Ivanov, Anatoly I.

2018-03-01

An approach to the construction of diabatic free energy surfaces (FESs) for ultrafast electron transfer (ET) in a supramolecule with an arbitrary number of electron localization centers (redox sites) is developed, supposing that the reorganization energies for the charge transfers and shifts between all these centers are known. Dimensionality of the coordinate space required for the description of multistage ET in this supramolecular system is shown to be equal to N - 1, where N is the number of the molecular centers involved in the reaction. The proposed algorithm of FES construction employs metric properties of the coordinate space, namely, relation between the solvent reorganization energy and the distance between the two FES minima. In this space, the ET reaction coordinate zn n' associated with electron transfer between the nth and n'th centers is calculated through the projection to the direction, connecting the FES minima. The energy-gap reaction coordinates zn n' corresponding to different ET processes are not in general orthogonal so that ET between two molecular centers can create nonequilibrium distribution, not only along its own reaction coordinate but along other reaction coordinates too. This results in the influence of the preceding ET steps on the kinetics of the ensuing ET. It is important for the ensuing reaction to be ultrafast to proceed in parallel with relaxation along the ET reaction coordinates. Efficient algorithms for numerical simulation of multistage ET within the stochastic point-transition model are developed. The algorithms are based on the Brownian simulation technique with the recrossing-event detection procedure. The main advantages of the numerical method are (i) its computational complexity is linear with respect to the number of electronic states involved and (ii) calculations can be naturally parallelized up to the level of individual trajectories. The efficiency of the proposed approach is demonstrated for a model

Demand management in Multi-Stage Distribution Chain

NARCIS (Netherlands)

de Kok, T.; Janssen, F.B.S.L.P.

1996-01-01

In this paper we discuss demand management problems in a multi-stage distribution chain.We focus on distribution chains where demand processes have high variability due to a few large customer orders.We give a possible explanation, and suggest two simple procedures that help to smooth demand.It is

Linear causal modeling with structural equations

CERN Document Server

Mulaik, Stanley A

2009-01-01

Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

Science.gov (United States)

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Reducing Delay in Diagnosis: Multistage Recommendation Tracking.

Science.gov (United States)

Wandtke, Ben; Gallagher, Sarah

2017-11-01

The purpose of this study was to determine whether a multistage tracking system could improve communication between health care providers, reducing the risk of delay in diagnosis related to inconsistent communication and tracking of radiology follow-up recommendations. Unconditional recommendations for imaging follow-up of all diagnostic imaging modalities excluding mammography (n = 589) were entered into a database and tracked through a multistage tracking system for 13 months. Tracking interventions were performed for patients for whom completion of recommended follow-up imaging could not be identified 1 month after the recommendation due date. Postintervention compliance with the follow-up recommendation required examination completion or clinical closure (i.e., biopsy, limited life expectancy or death, or subspecialist referral). Baseline radiology information system checks performed 1 month after the recommendation due date revealed timely completion of 43.1% of recommended imaging studies at our institution before intervention. Three separate tracking interventions were studied, showing effectiveness between 29.0% and 57.8%. The multistage tracking system increased the examination completion rate to 70.5% (a 52% increase) and reduced the rate of unknown follow-up compliance and the associated risk of delay in diagnosis to 13.9% (a 74% decrease). Examinations completed after tracking intervention generated revenue of 4.1 times greater than the labor cost. Performing sequential radiology recommendation tracking interventions can substantially reduce the rate of unknown follow-up compliance and add value to the health system. Unknown follow-up compliance is a risk factor for delay in diagnosis, a form of preventable medical error commonly identified in malpractice claims involving radiologists and office-based practitioners.

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

On D-branes from gauged linear sigma models

International Nuclear Information System (INIS)

Govindarajan, S.; Jayaraman, T.; Sarkar, T.

2001-01-01

We study both A-type and B-type D-branes in the gauged linear sigma model by considering worldsheets with boundary. The boundary conditions on the matter and vector multiplet fields are first considered in the large-volume phase/non-linear sigma model limit of the corresponding Calabi-Yau manifold, where we find that we need to add a contact term on the boundary. These considerations enable to us to derive the boundary conditions in the full gauged linear sigma model, including the addition of the appropriate boundary contact terms, such that these boundary conditions have the correct non-linear sigma model limit. Most of the analysis is for the case of Calabi-Yau manifolds with one Kaehler modulus (including those corresponding to hypersurfaces in weighted projective space), though we comment on possible generalisations

Multi-stage mixing in subduction zone: Application to Merapi volcano, Indonesia

Science.gov (United States)

Debaille, V.; Doucelance, R.; Weis, D.; Schiano, P.

2003-04-01

Basalts sampling subduction zone volcanism (IAB) often show binary mixing relationship in classical Sr-Nd, Pb-Pb, Sr-Pb isotopic diagrams, generally interpreted as reflecting the involvement of two components in their source. However, several authors have highlighted the presence of minimum three components in such a geodynamical context: mantle wedge, subducted and altered oceanic crust and subducted sediments. The overlying continental crust can also contribute by contamination and assimilation in magma chambers and/or during magma ascent. Here we present a multi-stage model to obtain a two end-member mixing from three components (mantle wedge, altered oceanic crust and sediments). The first stage of the model considers the metasomatism of the mantle wedge by fluids and/or melts released by subducted materials (altered oceanic crust and associated sediments), considering mobility and partition coefficient of trace elements in hydrated fluids and silicate melts. This results in the generation of two distinct end-members, reducing the number of components (mantle wedge, oceanic crust, sediments) from three to two. The second stage of the model concerns the binary mixing of the two end-members thus defined: mantle wedge metasomatized by slab-derived fluids and mantle wedge metasomatized by sediment-derived fluids. This model has been applied on a new isotopic data set (Sr, Nd and Pb, analyzed by TIMS and MC-ICP-MS) of Merapi volcano (Java island, Indonesia). Previous studies have suggested three distinct components in the source of indonesian lavas: mantle wedge, subducted sediments and altered oceanic crust. Moreover, it has been shown that crustal contamination does not significantly affect isotopic ratios of lavas. The multi-stage model proposed here is able to reproduce the binary mixing observed in lavas of Merapi, and a set of numerical values of bulk partition coefficient is given that accounts for the genesis of lavas.

Catalytic multi-stage liquefaction of coal at HTI: Bench-scale studies in coal/waste plastics coprocessing

Energy Technology Data Exchange (ETDEWEB)

Pradhan, V.R.; Lee, L.K.; Stalzer, R.H. [Hydrocarbon Technologies, Inc., Lawrenceville, NJ (United States)] [and others

1995-12-31

The development of Catalytic Multi-Stage Liquefaction (CMSL) at HTI has focused on both bituminous and sub-bituminous coals using laboratory, bench and PDU scale operations. The crude oil equivalent cost of liquid fuels from coal has been curtailed to about $30 per barrel, thus achieving over 30% reduction in the price that was evaluated for the liquefaction technologies demonstrated in the late seventies and early eighties. Contrary to the common belief, the new generation of catalytic multistage coal liquefaction process is environmentally very benign and can produce clean, premium distillates with a very low (<10ppm) heteroatoms content. The HTI Staff has been involved over the years in process development and has made significant improvements in the CMSL processing of coals. A 24 month program (extended to September 30, 1995) to study novel concepts, using a continuous bench scale Catalytic Multi-Stage unit (30kg coal/day), has been initiated since December, 1992. This program consists of ten bench-scale operations supported by Laboratory Studies, Modelling, Process Simulation and Economic Assessments. The Catalytic Multi-Stage Liquefaction is a continuation of the second generation yields using a low/high temperature approach. This paper covers work performed between October 1994- August 1995, especially results obtained from the microautoclave support activities and the bench-scale operations for runs CMSL-08 and CMSL-09, during which, coal and the plastic components for municipal solid wastes (MSW) such as high density polyethylene (HDPE)m, polypropylene (PP), polystyrene (PS), and polythylene terphthlate (PET) were coprocessed.

Simulation and analysis of multi-stage centrifugal fractional extraction process of 4-nitrobenzene glycine enantiomers☆

Institute of Scientific and Technical Information of China (English)

Ping Wen; Kewen Tang; Jicheng Zhou; Panliang Zhang

2015-01-01

Based on the interfacial ligand exchange model and the law of conservation of mass, the multi-stage enantioselective liquid–liquid extraction model has been established to analyze and discuss on multi-stage centrifugal fractional extraction process of 4-nitrobenzene glycine (PGL) enantiomers. The influence of phase ratio, extractant concentra-tion, and PF6−concentration on the concentrations of enantiomers in the extract and raffinate was investigated by experiment and simulation. A good agreement between model and experiment was obtained. On this basis, the influence of many parameters such as location of stage, concentration levels, extractant excess, and number of stages on the symmetric separation performance was simulated. The optimal location of feed stage is the middle of fractional extraction equipment. The feed flow must satisfy a restricted relationship on flow ratios and the liquid throughout of centrifugal device. For desired purity specification, the required flow ratios decrease with extractant concentration and increase with PF6−concentration. When the number of stages is 18 stages at extractant excess of 1.0 or 14 stages at extractant excess of 2.0, the eeeq (equal enantiomeric excess) can reach to 99%.

Multistage Stochastic Programming via Autoregressive Sequences

Czech Academy of Sciences Publication Activity Database

Kaňková, Vlasta

2007-01-01

Roč. 15, č. 4 (2007), s. 99-110 ISSN 0572-3043 R&D Projects: GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : Economic proceses * Multistage stochastic programming * autoregressive sequences * individual probability constraints Subject RIV: BB - Applied Statistics, Operational Research

Decomposable log-linear models

DEFF Research Database (Denmark)

Eriksen, Poul Svante

can be characterized by a structured set of conditional independencies between some variables given some other variables. We term the new model class decomposable log-linear models, which is illustrated to be a much richer class than decomposable graphical models.It covers a wide range of non...... The present paper considers discrete probability models with exact computational properties. In relation to contingency tables this means closed form expressions of the maksimum likelihood estimate and its distribution. The model class includes what is known as decomposable graphicalmodels, which......-hierarchical models, models with structural zeroes, models described by quasi independence and models for level merging. Also, they have a very natural interpretation as they may be formulated by a structured set of conditional independencies between two events given some other event. In relation to contingency...

Modeling digital switching circuits with linear algebra

CERN Document Server

Thornton, Mitchell A

2014-01-01

Modeling Digital Switching Circuits with Linear Algebra describes an approach for modeling digital information and circuitry that is an alternative to Boolean algebra. While the Boolean algebraic model has been wildly successful and is responsible for many advances in modern information technology, the approach described in this book offers new insight and different ways of solving problems. Modeling the bit as a vector instead of a scalar value in the set {0, 1} allows digital circuits to be characterized with transfer functions in the form of a linear transformation matrix. The use of transf

Finite Element Analysis and Optimization for the Multi-stage Deep Drawing of Molybdenum Sheet

International Nuclear Information System (INIS)

Kim, Heung-Kyu; Hong, Seok Kwan; Kang, Jeong Jin; Heo, Young-moo; Lee, Jong-Kil; Jeon, Byung-Hee

2005-01-01

Molybdenum, a bcc refractory metal with a melting point of about 2600 deg. C, has a high heat and electrical conductivity. In addition, it remains strong mechanically at high temperatures as well as at low temperatures. Therefore it is a technologically very important material for the applications operating at high temperatures. However, a multi-stage process is required due to the low drawability for making a deep drawn part from the molybdenum sheet. In this study, a multi-stage deep drawing process for a molybdenum circular cup was designed by combining the drawing with the ironing, which was effective for the low drawability materials. A parametric study by FE analysis for the multi-stage deep drawing was conducted for evaluation of the design variables effect. Based on the FE analysis result, the multi-stage deep drawing process was parameterized by the design variables, and an optimum process design was obtained by the process optimization based on the FE simulation at each stage

Non-linear Growth Models in Mplus and SAS

Science.gov (United States)

Grimm, Kevin J.; Ram, Nilam

2013-01-01

Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included. PMID:23882134

Variance Function Partially Linear Single-Index Models1.

Science.gov (United States)

Lian, Heng; Liang, Hua; Carroll, Raymond J

2015-01-01

We consider heteroscedastic regression models where the mean function is a partially linear single index model and the variance function depends upon a generalized partially linear single index model. We do not insist that the variance function depend only upon the mean function, as happens in the classical generalized partially linear single index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric and nonparametric parts of the model is developed. Simulations illustrate the results. An empirical example involving ozone levels is used to further illustrate the results, and is shown to be a case where the variance function does not depend upon the mean function.

Linear latent variable models: the lava-package

DEFF Research Database (Denmark)

Holst, Klaus Kähler; Budtz-Jørgensen, Esben

2013-01-01

are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...

Fault Diagnosis for a Multistage Planetary Gear Set Using Model-Based Simulation and Experimental Investigation

Directory of Open Access Journals (Sweden)

Guoyan Li

2016-01-01

Full Text Available The gear damage will induce modulation effects in vibration signals. A thorough analysis of modulation sidebands spectral structure is necessary for fault diagnosis of planetary gear set. However, the spectral characteristics are complicated in practice, especially for a multistage planetary gear set which contains close frequency components. In this study, a coupled lateral and torsional dynamic model is established to predict the modulation sidebands of a two-stage compound planetary gear set. An improved potential energy method is used to calculate the time-varying mesh stiffness of each gear pair, and the influence of crack propagation on the mesh stiffness is analyzed. The simulated signals of the gear set are obtained by using Runge-Kutta numerical analysis method. Meanwhile, the sidebands characteristics are summarized to exhibit the modulation effects caused by sun gear damage. At the end, the experimental signals collected from an industrial SD16 planetary gearbox are analyzed to verify the theoretical derivations. The results of experiment agree well with the simulated analysis.

Hydrogen enriched gas production in a multi-stage downdraft gasification process

International Nuclear Information System (INIS)

Dutta, A.; Jarungthammachote, S.

2009-01-01

To achieve hydrogen enriched and low-tar producer gas, multi-stage air-blown and air-steam gasification were studied in this research. Results showed that the tar content from multi-stage air-blown and air-steam gasification was lower compared to the average value of that from downdraft gasification. It was also seen that an air-steam gasification process could potentially increase the hydrogen concentration in the producer gas in the expense of carbon monoxide; however, the summation of hydrogen and carbon monoxide in the producer gas was increased. (author)

Optimal testlet pool assembly for multistage testing designs

NARCIS (Netherlands)

Ariel, A.; Veldkamp, Bernard P.; Breithaupt, Krista

2006-01-01

Computerized multistage testing (MST) designs require sets of test questions (testlets) to be assembled to meet strict, often competing criteria. Rules that govern testlet assembly may dictate the number of questions on a particular subject or may describe desirable statistical properties for the

Multi-stage circulating fluidized bed syngas cooling

Science.gov (United States)

Liu, Guohai; Vimalchand, Pannalal; Guan, Xiaofeng; Peng, WanWang

2016-10-11

A method and apparatus for cooling hot gas streams in the temperature range 800.degree. C. to 1600.degree. C. using multi-stage circulating fluid bed (CFB) coolers is disclosed. The invention relates to cooling the hot syngas from coal gasifiers in which the hot syngas entrains substances that foul, erode and corrode heat transfer surfaces upon contact in conventional coolers. The hot syngas is cooled by extracting and indirectly transferring heat to heat transfer surfaces with circulating inert solid particles in CFB syngas coolers. The CFB syngas coolers are staged to facilitate generation of steam at multiple conditions and hot boiler feed water that are necessary for power generation in an IGCC process. The multi-stage syngas cooler can include internally circulating fluid bed coolers, externally circulating fluid bed coolers and hybrid coolers that incorporate features of both internally and externally circulating fluid bed coolers. Higher process efficiencies can be realized as the invention can handle hot syngas from various types of gasifiers without the need for a less efficient precooling step.

Multistage modeling of protein dynamics with monomeric Myc oncoprotein as an example.

Science.gov (United States)

Liu, Jiaojiao; Dai, Jin; He, Jianfeng; Niemi, Antti J; Ilieva, Nevena

2017-03-01

We propose to combine a mean-field approach with all-atom molecular dynamics (MD) into a multistage algorithm that can model protein folding and dynamics over very long time periods yet with atomic-level precision. As an example, we investigate an isolated monomeric Myc oncoprotein that has been implicated in carcinomas including those in colon, breast, and lungs. Under physiological conditions a monomeric Myc is presumed to be an example of intrinsically disordered proteins that pose a serious challenge to existing modeling techniques. We argue that a room-temperature monomeric Myc is in a dynamical state, it oscillates between different conformations that we identify. For this we adopt the Cα backbone of Myc in a crystallographic heteromer as an initial ansatz for the monomeric structure. We construct a multisoliton of the pertinent Landau free energy to describe the Cα profile with ultrahigh precision. We use Glauber dynamics to resolve how the multisoliton responds to repeated increases and decreases in ambient temperature. We confirm that the initial structure is unstable in isolation. We reveal a highly degenerate ground-state landscape, an attractive set towards which Glauber dynamics converges in the limit of vanishing ambient temperature. We analyze the thermal stability of this Glauber attractor using room-temperature molecular dynamics. We identify and scrutinize a particularly stable subset in which the two helical segments of the original multisoliton align in parallel next to each other. During the MD time evolution of a representative structure from this subset, we observe intermittent quasiparticle oscillations along the C-terminal α helix, some of which resemble a translating Davydov's Amide-I soliton. We propose that the presence of oscillatory motion is in line with the expected intrinsically disordered character of Myc.

Thermodynamic analysis of single-stage and multi-stage adsorption refrigeration cycles with activated carbon–ammonia working pair

International Nuclear Information System (INIS)

Xu, S.Z.; Wang, L.W.; Wang, R.Z.

2016-01-01

Highlights: • Activated carbon–ammonia multi-stage adsorption refrigerator was analyzed. • COP, exergetic efficiency and entropy production of cycles were calculated. • Single-stage cycle usually has the advantages of simple structure and high COP. • Multi-stage cycles adapt to critical conditions better than single-stage cycle. • Boundary conditions for choosing optimal cycle were summarized as tables. - Abstract: Activated carbon–ammonia multi-stage adsorption refrigeration cycle was analyzed in this article, which realized deep-freezing for evaporating temperature under −18 °C with heating source temperature much lower than 100 °C. Cycle mathematical models for single, two and three-stage cycles were established on the basis of thorough thermodynamic analysis. According to simulation results of thermodynamic evaluation indicators such as COP (coefficient of performance), exergetic efficiency and cycle entropy production, multi-stage cycle adapts to high condensing temperature, low evaporating temperature and low heating source temperature well. Proposed cycle with selected working pair can theoretically work under very severe conditions, such as −25 °C evaporating temperature, 40 °C condensing temperature, and 70 °C heating source temperature, but under these working conditions it has the drawback of low cycle adsorption quantity. It was found that both COP and exergetic efficiency are of great reference value in the choice of cycle, whereas entropy production is not so useful for cycle stage selection. Finally, the application boundary conditions of single-stage, two-stage, and three-stage cycles were summarized as tables according to the simulation results, which provides reference for choosing optimal cycle under different conditions.

Analysis of multi-stage open shop processing systems

NARCIS (Netherlands)

Eggermont, C.E.J.; Schrijver, A.; Woeginger, G.J.; Schwentick, T.; Dürr, C.

2011-01-01

We study algorithmic problems in multi-stage open shop processing systems that are centered around reachability and deadlock detection questions. We characterize safe and unsafe system states. We show that it is easy to recognize system states that can be reached from the initial state (where the

Linear factor copula models and their properties

KAUST Repository

Krupskii, Pavel; Genton, Marc G.

2018-01-01

We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

Linear factor copula models and their properties

KAUST Repository

Krupskii, Pavel

2018-04-25

We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme‐value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme‐value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

Modelling Loudspeaker Non-Linearities

DEFF Research Database (Denmark)

Agerkvist, Finn T.

2007-01-01

This paper investigates different techniques for modelling the non-linear parameters of the electrodynamic loudspeaker. The methods are tested not only for their accuracy within the range of original data, but also for the ability to work reasonable outside that range, and it is demonstrated...... that polynomial expansions are rather poor at this, whereas an inverse polynomial expansion or localized fitting functions such as the gaussian are better suited for modelling the Bl-factor and compliance. For the inductance the sigmoid function is shown to give very good results. Finally the time varying...

Preliminary study on functional performance of compound type multistage safety injection tank

International Nuclear Information System (INIS)

Bae, Youngmin; Kim, Young In; Kim, Keung Koo

2015-01-01

Highlights: • Functional performance of compound type multistage safety injection tanks is studied. • Effects of key design parameters are scrutinized. • Distinctive flow features in compound type safety injection tanks are explored. - Abstract: A parametric study is carried out to evaluate the functional performance of a compound type multistage safety injection tank that would be considered one of the components for the passive safety injection systems in nuclear power plants. The effects of key design parameters such as the initial volume fraction and charging pressure of gas, tank elevation, vertical location of a sparger, resistance coefficient, and operating condition on the injection flow rate are scrutinized along with a discussion of the relevant flow features. The obtained results indicate that the compound type multistage safety injection tank can effectively control the injection flow rate in a passive manner, by switching the driving force for the safety injection from gas pressure to gravity during the refill and reflood phases, respectively

Novel product ions of 2-aminoanilide and benzimidazole Ag(I) complexes using electrospray ionization with multi-stage tandem mass spectrometry.

Science.gov (United States)

Johnson, Byron S; Burinsky, David J; Burova, Svetlana A; Davis, Roman; Fitzgerald, Russ N; Matsuoka, Richard T

2012-05-15

The 2-aminoaniline scaffold is of significant value to the pharmaceutical industry and is embedded in a number of pharmacophores including 2-aminoanilides and benzimidazoles. A novel application of coordination ion spray mass spectrometry (CIS-MS) for interrogating the silver ion (Ag(+)) complexes of a homologous series of these compounds using multi-stage tandem mass spectrometry is described. Unlike the ubiquitous alkali metal ion complexes, Ag(+) complexes of 2-aminoanilides and benzimidazoles were found to yield [M - H](+) ions in significant abundance via gas-phase elimination of the metal hydride (AgH) resulting in unique product ion cascades. Sample introduction was by liquid chromatography with mass spectrometry analysis performed on a hybrid linear ion trap/orbitrap instrument capable of high-resolution measurements. Rigorous structural characterization by multi-stage tandem mass spectrometry using [M +  H](+), [M - H](-) and [M - H](+) precursor ions derived from ESI and CIS experiments was performed for the homologous series of 2-aminoanilide and benzimidazole compounds. A full tabular comparison of structural information resulting from these product ion cascades was produced. Multi-stage tandem mass spectrometry of [M - H](+) ions resulting from Ag(+) complexes of 2-aminoanilides and benzimidazoles in CIS-MS experiments produced unique product ion cascades that exhibited complementary structural information to that obtained from tandem mass spectrometry of [M  +  H](+) and [M - H](-) ions by electrospray ionization (ESI). These observations may be broadly applicable to other compounds that are observed to form Ag(+) complexes and eliminate AgH. Copyright © 2012 John Wiley & Sons, Ltd.

An inexact multistage fuzzy-stochastic programming for regional electric power system management constrained by environmental quality.

Science.gov (United States)

Fu, Zhenghui; Wang, Han; Lu, Wentao; Guo, Huaicheng; Li, Wei

2017-12-01

Electric power system involves different fields and disciplines which addressed the economic system, energy system, and environment system. Inner uncertainty of this compound system would be an inevitable problem. Therefore, an inexact multistage fuzzy-stochastic programming (IMFSP) was developed for regional electric power system management constrained by environmental quality. A model which concluded interval-parameter programming, multistage stochastic programming, and fuzzy probability distribution was built to reflect the uncertain information and dynamic variation in the case study, and the scenarios under different credibility degrees were considered. For all scenarios under consideration, corrective actions were allowed to be taken dynamically in accordance with the pre-regulated policies and the uncertainties in reality. The results suggest that the methodology is applicable to handle the uncertainty of regional electric power management systems and help the decision makers to establish an effective development plan.

Log-normal frailty models fitted as Poisson generalized linear mixed models.

Science.gov (United States)

Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

2016-12-01

The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Linearized models for a new magnetic control in MAST

International Nuclear Information System (INIS)

Artaserse, G.; Maviglia, F.; Albanese, R.; McArdle, G.J.; Pangione, L.

2013-01-01

Highlights: ► We applied linearized models for a new magnetic control on MAST tokamak. ► A suite of procedures, conceived to be machine independent, have been used. ► We carried out model-based simulations, taking into account eddy currents effects. ► Comparison with the EFIT flux maps and the experimental magnetic signals are shown. ► A current driven model for the dynamic simulations of the experimental data have been performed. -- Abstract: The aim of this work is to provide reliable linearized models for the design and assessment of a new magnetic control system for MAST (Mega Ampère Spherical Tokamak) using rtEFIT, which can easily be exported to MAST Upgrade. Linearized models for magnetic control have been obtained using the 2D axisymmetric finite element code CREATE L. MAST linearized models include equivalent 2D axisymmetric schematization of poloidal field (PF) coils, vacuum vessel, and other conducting structures. A plasmaless and a double null configuration have been chosen as benchmark cases for the comparison with experimental data and EFIT reconstructions. Good agreement has been found with the EFIT flux map and the experimental signals coming from magnetic probes with only few mismatches probably due to broken sensors. A suite of procedures (equipped with a user friendly interface to be run even remotely) to provide linearized models for magnetic control is now available on the MAST linux machines. A new current driven model has been used to obtain a state space model having the PF coil currents as inputs. Dynamic simulations of experimental data have been carried out using linearized models, including modelling of the effects of the passive structures, showing a fair agreement. The modelling activity has been useful also to reproduce accurately the interaction between plasma current and radial position control loops

Linearized models for a new magnetic control in MAST

Energy Technology Data Exchange (ETDEWEB)

Artaserse, G., E-mail: giovanni.artaserse@enea.it [Associazione Euratom-ENEA sulla Fusione, Via Enrico Fermi 45, I-00044 Frascati (RM) (Italy); Maviglia, F.; Albanese, R. [Associazione Euratom-ENEA-CREATE sulla Fusione, Via Claudio 21, I-80125 Napoli (Italy); McArdle, G.J.; Pangione, L. [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon, OX14 3DB (United Kingdom)

2013-10-15

Highlights: ► We applied linearized models for a new magnetic control on MAST tokamak. ► A suite of procedures, conceived to be machine independent, have been used. ► We carried out model-based simulations, taking into account eddy currents effects. ► Comparison with the EFIT flux maps and the experimental magnetic signals are shown. ► A current driven model for the dynamic simulations of the experimental data have been performed. -- Abstract: The aim of this work is to provide reliable linearized models for the design and assessment of a new magnetic control system for MAST (Mega Ampère Spherical Tokamak) using rtEFIT, which can easily be exported to MAST Upgrade. Linearized models for magnetic control have been obtained using the 2D axisymmetric finite element code CREATE L. MAST linearized models include equivalent 2D axisymmetric schematization of poloidal field (PF) coils, vacuum vessel, and other conducting structures. A plasmaless and a double null configuration have been chosen as benchmark cases for the comparison with experimental data and EFIT reconstructions. Good agreement has been found with the EFIT flux map and the experimental signals coming from magnetic probes with only few mismatches probably due to broken sensors. A suite of procedures (equipped with a user friendly interface to be run even remotely) to provide linearized models for magnetic control is now available on the MAST linux machines. A new current driven model has been used to obtain a state space model having the PF coil currents as inputs. Dynamic simulations of experimental data have been carried out using linearized models, including modelling of the effects of the passive structures, showing a fair agreement. The modelling activity has been useful also to reproduce accurately the interaction between plasma current and radial position control loops.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Directory of Open Access Journals (Sweden)

Idil Isikli Esener

2017-01-01

Full Text Available A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

Direct Numerical Simulation of Turbulent Multi-Stage Autoignition Relevant to Engine Conditions

Science.gov (United States)

Chen, Jacqueline

2017-11-01

Due to the unrivaled energy density of liquid hydrocarbon fuels combustion will continue to provide over 80% of the world's energy for at least the next fifty years. Hence, combustion needs to be understood and controlled to optimize combustion systems for efficiency to prevent further climate change, to reduce emissions and to ensure U.S. energy security. In this talk I will discuss recent progress in direct numerical simulations of turbulent combustion focused on providing fundamental insights into key `turbulence-chemistry' interactions that underpin the development of next generation fuel efficient, fuel flexible engines for transportation and power generation. Petascale direct numerical simulation (DNS) of multi-stage mixed-mode turbulent combustion in canonical configurations have elucidated key physics that govern autoignition and flame stabilization in engines and provide benchmark data for combustion model development under the conditions of advanced engines which operate near combustion limits to maximize efficiency and minimize emissions. Mixed-mode combustion refers to premixed or partially-premixed flames propagating into stratified autoignitive mixtures. Multi-stage ignition refers to hydrocarbon fuels with negative temperature coefficient behavior that undergo sequential low- and high-temperature autoignition. Key issues that will be discussed include: 1) the role of mixing in shear driven turbulence on the dynamics of multi-stage autoignition and cool flame propagation in diesel environments, 2) the role of thermal and composition stratification on the evolution of the balance of mixed combustion modes - flame propagation versus spontaneous ignition - which determines the overall combustion rate in autoignition processes, and 3) the role of cool flames on lifted flame stabilization. Finally prospects for DNS of turbulent combustion at the exascale will be discussed in the context of anticipated heterogeneous machine architectures. sponsored by DOE

Modeling Non-Linear Material Properties in Composite Materials

Science.gov (United States)

2016-06-28

Technical Report ARWSB-TR-16013 MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS Michael F. Macri Andrew G...REPORT TYPE Technical 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE MODELING NON-LINEAR MATERIAL PROPERTIES IN COMPOSITE MATERIALS ...systems are increasingly incorporating composite materials into their design. Many of these systems subject the composites to environmental conditions

Multi-stage pulsed laser deposition of aluminum nitride at different temperatures

Energy Technology Data Exchange (ETDEWEB)

Duta, L. [National Institute for Lasers, Plasma, and Radiation Physics, 409 Atomistilor Street, 077125 Magurele (Romania); Stan, G.E. [National Institute of Materials Physics, 105 bis Atomistilor Street, 077125 Magurele (Romania); Stroescu, H.; Gartner, M.; Anastasescu, M. [Institute of Physical Chemistry “Ilie Murgulescu”, Romanian Academy, 202 Splaiul Independentei, 060021 Bucharest (Romania); Fogarassy, Zs. [Research Institute for Technical Physics and Materials Science, Hungarian Academy of Sciences, Konkoly Thege Miklos u. 29-33, H-1121 Budapest (Hungary); Mihailescu, N. [National Institute for Lasers, Plasma, and Radiation Physics, 409 Atomistilor Street, 077125 Magurele (Romania); Szekeres, A., E-mail: szekeres@issp.bas.bg [Institute of Solid State Physics, Bulgarian Academy of Sciences, Tzarigradsko Chaussee 72, Sofia 1784 (Bulgaria); Bakalova, S. [Institute of Solid State Physics, Bulgarian Academy of Sciences, Tzarigradsko Chaussee 72, Sofia 1784 (Bulgaria); Mihailescu, I.N., E-mail: ion.mihailescu@inflpr.ro [National Institute for Lasers, Plasma, and Radiation Physics, 409 Atomistilor Street, 077125 Magurele (Romania)

2016-06-30

Highlights: • Multi-stage pulsed laser deposition of aluminum nitride at different temperatures. • 800 °C seed film boosts the next growth of crystalline structures at lower temperature. • Two-stage deposited AlN samples exhibit randomly oriented wurtzite structures. • Band gap energy values increase with deposition temperature. • Correlation was observed between single- and multi-stage AlN films. - Abstract: We report on multi-stage pulsed laser deposition of aluminum nitride (AlN) on Si (1 0 0) wafers, at different temperatures. The first stage of deposition was carried out at 800 °C, the optimum temperature for AlN crystallization. In the second stage, the deposition was conducted at lower temperatures (room temperature, 350 °C or 450 °C), in ambient Nitrogen, at 0.1 Pa. The synthesized structures were analyzed by grazing incidence X-ray diffraction (GIXRD), transmission electron microscopy (TEM), atomic force microscopy and spectroscopic ellipsometry (SE). GIXRD measurements indicated that the two-stage deposited AlN samples exhibited a randomly oriented wurtzite structure with nanosized crystallites. The peaks were shifted to larger angles, indicative for smaller inter-planar distances. Remarkably, TEM images demonstrated that the high-temperature AlN “seed” layers (800 °C) promoted the growth of poly-crystalline AlN structures at lower deposition temperatures. When increasing the deposition temperature, the surface roughness of the samples exhibited values in the range of 0.4–2.3 nm. SE analyses showed structures which yield band gap values within the range of 4.0–5.7 eV. A correlation between the results of single- and multi-stage AlN depositions was observed.

Non-linear Loudspeaker Unit Modelling

DEFF Research Database (Denmark)

Pedersen, Bo Rohde; Agerkvist, Finn T.

2008-01-01

Simulations of a 6½-inch loudspeaker unit are performed and compared with a displacement measurement. The non-linear loudspeaker model is based on the major nonlinear functions and expanded with time-varying suspension behaviour and flux modulation. The results are presented with FFT plots of thr...... frequencies and different displacement levels. The model errors are discussed and analysed including a test with loudspeaker unit where the diaphragm is removed....

Stage-dependent hierarchy of criteria in multiobjective multistage decision processes

Directory of Open Access Journals (Sweden)

Tadeusz Trzaskalik

2017-01-01

Full Text Available This paper will consider a multiobjective, multistage discrete dynamic process with a changeable, state-dependent hierarchy of stage criteria determined by the decision maker. The goal of this paper is to answer the question of how to control a multistage process while taking into account both the tendency to achieve multiobjective optimization of the entire process and the time-varying hierarchy of stage criteria. We consider in detail possible situations, where the hierarchy of stage criteria changes over time in individual stages and is stage dependent. We present an interactive proposal to solving the problem, where the decision maker actively participates in finding the final realization of the process. The algorithm proposed is illustrated using a numerical example.

Comparison between linear quadratic and early time dose models

International Nuclear Information System (INIS)

Chougule, A.A.; Supe, S.J.

1993-01-01

During the 70s, much interest was focused on fractionation in radiotherapy with the aim of improving tumor control rate without producing unacceptable normal tissue damage. To compare the radiobiological effectiveness of various fractionation schedules, empirical formulae such as Nominal Standard Dose, Time Dose Factor, Cumulative Radiation Effect and Tumour Significant Dose, were introduced and were used despite many shortcomings. It has been claimed that a recent linear quadratic model is able to predict the radiobiological responses of tumours as well as normal tissues more accurately. We compared Time Dose Factor and Tumour Significant Dose models with the linear quadratic model for tumour regression in patients with carcinomas of the cervix. It was observed that the prediction of tumour regression estimated by the Tumour Significant Dose and Time Dose factor concepts varied by 1.6% from that of the linear quadratic model prediction. In view of the lack of knowledge of the precise values of the parameters of the linear quadratic model, it should be applied with caution. One can continue to use the Time Dose Factor concept which has been in use for more than a decade as its results are within ±2% as compared to that predicted by the linear quadratic model. (author). 11 refs., 3 figs., 4 tabs

A Note on the Identifiability of Generalized Linear Mixed Models

DEFF Research Database (Denmark)

Labouriau, Rodrigo

2014-01-01

I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization...

RF power generation for future linear colliders

International Nuclear Information System (INIS)

Fowkes, W.R.; Allen, M.A.; Callin, R.S.; Caryotakis, G.; Eppley, K.R.; Fant, K.S.; Farkas, Z.D.; Feinstein, J.; Ko, K.; Koontz, R.F.; Kroll, N.; Lavine, T.L.; Lee, T.G.; Miller, R.H.; Pearson, C.; Spalek, G.; Vlieks, A.E.; Wilson, P.B.

1990-06-01

The next linear collider will require 200 MW of rf power per meter of linac structure at relatively high frequency to produce an accelerating gradient of about 100 MV/m. The higher frequencies result in a higher breakdown threshold in the accelerating structure hence permit higher accelerating gradients per meter of linac. The lower frequencies have the advantage that high peak power rf sources can be realized. 11.42 GHz appears to be a good compromise and the effort at the Stanford Linear Accelerator Center (SLAC) is being concentrated on rf sources operating at this frequency. The filling time of the accelerating structure for each rf feed is expected to be about 80 ns. Under serious consideration at SLAC is a conventional klystron followed by a multistage rf pulse compression system, and the Crossed-Field Amplifier. These are discussed in this paper

A non-linear state space approach to model groundwater fluctuations

NARCIS (Netherlands)

Berendrecht, W.L.; Heemink, A.W.; Geer, F.C. van; Gehrels, J.C.

2006-01-01

A non-linear state space model is developed for describing groundwater fluctuations. Non-linearity is introduced by modeling the (unobserved) degree of water saturation of the root zone. The non-linear relations are based on physical concepts describing the dependence of both the actual

Effective connectivity between superior temporal gyrus and Heschl's gyrus during white noise listening: linear versus non-linear models.

Science.gov (United States)

Hamid, Ka; Yusoff, An; Rahman, Mza; Mohamad, M; Hamid, Aia

2012-04-01

This fMRI study is about modelling the effective connectivity between Heschl's gyrus (HG) and the superior temporal gyrus (STG) in human primary auditory cortices. MATERIALS #ENTITYSTARTX00026; Ten healthy male participants were required to listen to white noise stimuli during functional magnetic resonance imaging (fMRI) scans. Statistical parametric mapping (SPM) was used to generate individual and group brain activation maps. For input region determination, two intrinsic connectivity models comprising bilateral HG and STG were constructed using dynamic causal modelling (DCM). The models were estimated and inferred using DCM while Bayesian Model Selection (BMS) for group studies was used for model comparison and selection. Based on the winning model, six linear and six non-linear causal models were derived and were again estimated, inferred, and compared to obtain a model that best represents the effective connectivity between HG and the STG, balancing accuracy and complexity. Group results indicated significant asymmetrical activation (p(uncorr) Model comparison results showed strong evidence of STG as the input centre. The winning model is preferred by 6 out of 10 participants. The results were supported by BMS results for group studies with the expected posterior probability, r = 0.7830 and exceedance probability, ϕ = 0.9823. One-sample t-tests performed on connection values obtained from the winning model indicated that the valid connections for the winning model are the unidirectional parallel connections from STG to bilateral HG (p model comparison between linear and non-linear models using BMS prefers non-linear connection (r = 0.9160, ϕ = 1.000) from which the connectivity between STG and the ipsi- and contralateral HG is gated by the activity in STG itself. We are able to demonstrate that the effective connectivity between HG and STG while listening to white noise for the respective participants can be explained by a non-linear dynamic causal model with

Pilot-scale multistage membrane process for the separation of CO2 from LNG-fired flue gas

KAUST Repository

Choi, Seung Hak

2013-06-01

In this study, a multistage pilot-scale membrane plant was constructed and operated for the separation of CO2 from Liquefied Natural Gas (LNG)-fired boiler flue gas of 1000 Nm3/day. The target purity and recovery of CO2 were 99 vol.% and 90%, respectively. For this purpose, asymmetric polyethersulfone (PES) hollow fibers membranes has been developed in our previous work and has evaluated the effects of operating pressure and feed concentration of CO2 on separation performance. The operating and permeation data obtained were also analyzed in relation with the numerical simulation data using countercurrent flow model. Based on these results, in this study, four-staged membrane process including dehumidification process has been designed, installed, and operated to demonstrate the feasibility of multistage membrane systems for removing CO2 from flue gases. The operation results using this plant were compared to the numerical simulation results on multistage membrane process. The experimental results matched well with the numerical simulation data. The concentration and the recovery of CO2 in the permeate stream of final stage were ranged from 95-99 vol.% and 70-95%, respectively, depending on the operating conditions. This study demonstrated the applicability of the membrane-based pilot plant for CO2 recovery from flue gas. © 2013 Elsevier B.V. All rights reserved.

Computational analysis of a multistage axial compressor

Science.gov (United States)

Mamidoju, Chaithanya

Turbomachines are used extensively in Aerospace, Power Generation, and Oil & Gas Industries. Efficiency of these machines is often an important factor and has led to the continuous effort to improve the design to achieve better efficiency. The axial flow compressor is a major component in a gas turbine with the turbine's overall performance depending strongly on compressor performance. Traditional analysis of axial compressors involves throughflow calculations, isolated blade passage analysis, Quasi-3D blade-to-blade analysis, single-stage (rotor-stator) analysis, and multi-stage analysis involving larger design cycles. In the current study, the detailed flow through a 15 stage axial compressor is analyzed using a 3-D Navier Stokes CFD solver in a parallel computing environment. Methodology is described for steady state (frozen rotor stator) analysis of one blade passage per component. Various effects such as mesh type and density, boundary conditions, tip clearance and numerical issues such as turbulence model choice, advection model choice, and parallel processing performance are analyzed. A high sensitivity of the predictions to the above was found. Physical explanation to the flow features observed in the computational study are given. The total pressure rise verses mass flow rate was computed.

Aerodynamics and flow characterisation of multistage rockets

Science.gov (United States)

Srinivas, G.; Prakash, M. V. S.

2017-05-01

The main objective of this paper is to conduct a systematic flow analysis on single, double and multistage rockets using ANSYS software. Today non-air breathing propulsion is increasing dramatically for the enhancement of space exploration. The rocket propulsion is playing vital role in carrying the payload to the destination. Day to day rocket aerodynamic performance and flow characterization analysis has becoming challenging task to the researchers. Taking this task as motivation a systematic literature is conducted to achieve better aerodynamic and flow characterization on various rocket models. The analyses on rocket models are very little especially in numerical side and experimental area. Each rocket stage analysis conducted for different Mach numbers and having different flow varying angle of attacks for finding the critical efficiency performance parameters like pressure, density and velocity. After successful completion of the analysis the research reveals that flow around the rocket body for Mach number 4 and 5 best suitable for designed payload. Another major objective of this paper is to bring best aerodynamics flow characterizations in both aero and mechanical features. This paper also brings feature prospectus of rocket stage technology in the field of aerodynamic design.

Linear Equating for the NEAT Design: Parameter Substitution Models and Chained Linear Relationship Models

Science.gov (United States)

Kane, Michael T.; Mroch, Andrew A.; Suh, Youngsuk; Ripkey, Douglas R.

2009-01-01

This paper analyzes five linear equating models for the "nonequivalent groups with anchor test" (NEAT) design with internal anchors (i.e., the anchor test is part of the full test). The analysis employs a two-dimensional framework. The first dimension contrasts two general approaches to developing the equating relationship. Under a "parameter…

Recent Updates to the GEOS-5 Linear Model

Science.gov (United States)

Holdaway, Dan; Kim, Jong G.; Errico, Ron; Gelaro, Ronald; Mahajan, Rahul

2014-01-01

Global Modeling and Assimilation Office (GMAO) is close to having a working 4DVAR system and has developed a linearized version of GEOS-5.This talk outlines a series of improvements made to the linearized dynamics, physics and trajectory.Of particular interest is the development of linearized cloud microphysics, which provides the framework for 'all-sky' data assimilation.

Robust modified GA based multi-stage fuzzy LFC

International Nuclear Information System (INIS)

Shayeghi, H.; Jalili, A.; Shayanfar, H.A.

2007-01-01

In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm's convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems

Robust modified GA based multi-stage fuzzy LFC

Energy Technology Data Exchange (ETDEWEB)

Shayeghi, H. [Technical Engineering Department, The University of Mohaghegh Ardebili, Daneshkah St., Ardebil (Iran); Jalili, A. [Electrical Engineering Group, Islamic Azad University, Ardebil Branch, Ardebil (Iran); Shayanfar, H.A. [Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran)

2007-05-15

In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm's convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems. (author)

Non-linear calibration models for near infrared spectroscopy

DEFF Research Database (Denmark)

Ni, Wangdong; Nørgaard, Lars; Mørup, Morten

2014-01-01

by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear...... models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS......-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration...

[From clinical judgment to linear regression model.

Science.gov (United States)

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

Science.gov (United States)

Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

2009-01-01

This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

Phylogenetic mixtures and linear invariants for equal input models.

Science.gov (United States)

Casanellas, Marta; Steel, Mike

2017-04-01

The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).

Multi-stage fuzzy PID power system automatic generation controller in deregulated environments

International Nuclear Information System (INIS)

Shayeghi, H.; Shayanfar, H.A.; Jalili, A.

2006-01-01

In this paper, a multi-stage fuzzy proportional integral derivative (PID) type controller is proposed to solve the automatic generation control (AGC) problem in a deregulated power system that operates under deregulation based on the bilateral policy scheme. In each control area, the effects of the possible contracts are treated as a set of new input signals in a modified traditional dynamical model. The multi-stage controller uses the fuzzy switch to blend a proportional derivative (PD) fuzzy logic controller with an integral fuzzy logic input. The proposed controller operates on fuzzy values passing the consequence of a prior stage on to the next stage as fact. The salient advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter variations and system nonlinearities. This newly developed strategy leads to a flexible controller with simple structure that is easy to implement, and therefore, it can be useful for the real world power systems. The proposed method is tested on a three area power system with different contracted scenarios under various operating conditions. The results of the proposed controller are compared with those of the classical fuzzy PID type controller and classical PID controller through some performance indices to illustrate its robust performance

Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

DEFF Research Database (Denmark)

Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

2004-01-01

This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

Continuous-data diagnostic tests for paratuberculosis as a multistage disease

DEFF Research Database (Denmark)

Toft, Nils; Nielsen, Søren Saxmose; Jørgensen, Erik

2005-01-01

We devised a general method for interpretation of multistage diseases using continuous-data diagnostic tests. As an example, we used paratuberculosis as a multistage infection with 2 stages of infection as well as a noninfected state. Using data from a Danish research project, a fecal culture...... testing scheme was linked to an indirect ELISA and adjusted for covariates (parity, age at first calving, and days in milk). We used the log-transformed optical densities in a Bayesian network to obtain the probabilities for each of the 3 infection stages for a given optical density (adjusted...... for covariates). The strength of this approach was that the uncertainty associated with a test was imposed directly on the individual test result rather than aggregated into the population-based measures of test properties (i.e., sensitivity and specificity)...

A BEHAVIORAL-APPROACH TO LINEAR EXACT MODELING

NARCIS (Netherlands)

ANTOULAS, AC; WILLEMS, JC

1993-01-01

The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The main contribution of this paper is the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both

Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

Science.gov (United States)

Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J

2016-10-03

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

Fractional Multistage Hydrothermal Liquefaction of Biomass and Catalytic Conversion into Hydrocarbons

Energy Technology Data Exchange (ETDEWEB)

Cortright, Randy [Virent, Inc., Madison, WI (United States); Rozmiarek, Robert [Virent, Inc., Madison, WI (United States); Dally, Brice [Virent, Inc., Madison, WI (United States); Holland, Chris [Virent, Inc., Madison, WI (United States)

2017-08-31

The objective of this project was to develop an improved multistage process for the hydrothermal liquefaction (HTL) of biomass to serve as a new front-end, deconstruction process ideally suited to feed Virent’s well-proven catalytic technology, which is already being scaled up. This process produced water soluble, partially de-oxygenated intermediates that are ideally suited for catalytic finishing to fungible distillate hydrocarbons. Through this project, Virent, with its partners, demonstrated the conversion of pine wood chips to drop-in hydrocarbon distillate fuels using a multi-stage fractional conversion system that is integrated with Virent’s BioForming® process. The majority of work was in the liquefaction task and included temperature scoping, solvent optimization, and separations.

Preisach hysteresis model for non-linear 2D heat diffusion

International Nuclear Information System (INIS)

Jancskar, Ildiko; Ivanyi, Amalia

2006-01-01

This paper analyzes a non-linear heat diffusion process when the thermal diffusivity behaviour is a hysteretic function of the temperature. Modelling this temperature dependence, the discrete Preisach algorithm as general hysteresis model has been integrated into a non-linear multigrid solver. The hysteretic diffusion shows a heating-cooling asymmetry in character. The presented type of hysteresis speeds up the thermal processes in the modelled systems by a very interesting non-linear way

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

Science.gov (United States)

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

2018-05-01

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

A theoretical analysis of price elasticity of energy demand in multistage energy conversion systems

International Nuclear Information System (INIS)

Lowe, R.

2003-01-01

The objective of this paper is an analytical exploration of the problem of price elasticity of energy demand in multi-stage energy conversion systems. The paper describes in some detail an analytical model of energy demand in such systems. Under a clearly stated set of assumptions, the model makes it possible to explore both the impacts of the number of sub-systems, and of varying sub-system elasticities on overall system elasticity. The analysis suggests that overall price elasticity of energy demand for such systems will tend asymptotically to unity as the number of sub-systems increases. (author)

On-line validation of linear process models using generalized likelihood ratios

International Nuclear Information System (INIS)

Tylee, J.L.

1981-12-01

A real-time method for testing the validity of linear models of nonlinear processes is described and evaluated. Using generalized likelihood ratios, the model dynamics are continually monitored to see if the process has moved far enough away from the nominal linear model operating point to justify generation of a new linear model. The method is demonstrated using a seventh-order model of a natural circulation steam generator

Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods.

Science.gov (United States)

Ho, Yuh-Shan

2006-01-01

A comparison was made of the linear least-squares method and a trial-and-error non-linear method of the widely used pseudo-second-order kinetic model for the sorption of cadmium onto ground-up tree fern. Four pseudo-second-order kinetic linear equations are discussed. Kinetic parameters obtained from the four kinetic linear equations using the linear method differed but they were the same when using the non-linear method. A type 1 pseudo-second-order linear kinetic model has the highest coefficient of determination. Results show that the non-linear method may be a better way to obtain the desired parameters.

Identification of an Equivalent Linear Model for a Non-Linear Time-Variant RC-Structure

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune

are investigated and compared with ARMAX models used on a running window. The techniques are evaluated using simulated data generated by the non-linear finite element program SARCOF modeling a 10-storey 3-bay concrete structure subjected to amplitude modulated Gaussian white noise filtered through a Kanai......This paper considers estimation of the maximum softening for a RC-structure subjected to earthquake excitation. The so-called Maximum Softening damage indicator relates the global damage state of the RC-structure to the relative decrease of the fundamental eigenfrequency in an equivalent linear...

On-line control models for the Stanford Linear Collider

International Nuclear Information System (INIS)

Sheppard, J.C.; Helm, R.H.; Lee, M.J.; Woodley, M.D.

1983-03-01

Models for computer control of the SLAC three-kilometer linear accelerator and damping rings have been developed as part of the control system for the Stanford Linear Collider. Some of these models have been tested experimentally and implemented in the control program for routine linac operations. This paper will describe the development and implementation of these models, as well as some of the operational results

Vortices, semi-local vortices in gauged linear sigma model

International Nuclear Information System (INIS)

Kim, Namkwon

1998-11-01

We consider the static (2+1)D gauged linear sigma model. By analyzing the governing system of partial differential equations, we investigate various aspects of the model. We show the existence of energy finite vortices under a partially broken symmetry on R 2 with the necessary condition suggested by Y. Yang. We also introduce generalized semi-local vortices and show the existence of energy finite semi-local vortices under a certain condition. The vacuum manifold for the semi-local vortices turns out to be graded. Besides, with a special choice of a representation, we show that the O(3) sigma model of which target space is nonlinear is a singular limit of the gauged linear sigma model of which target space is linear. (author)

Dynamic multi-stage dispatch of isolated wind–diesel power systems

International Nuclear Information System (INIS)

Hu, Yu; Morales, Juan M.; Pineda, Salvador; Sánchez, María Jesús; Solana, Pablo

2015-01-01

Highlights: • Optimal decision-making model for isolated hybrid wind–diesel power system is proposed. • Wind power uncertainty and conditional operating cost are considered. • Battery wear cost of the energy storage system is included in the model. • The results are compared with deterministic dispatch strategies. - Abstract: An optimal dispatch strategy is crucial for an isolated wind–diesel power system to save diesel fuel and maintain the system stability. The uncertainty associated with the stochastic character of the wind is, though, a challenging problem for this optimization. In this paper, a dynamic multi-stage decision-making model is proposed to determine the diesel power output that minimizes the cost of running and maintaining the wind–diesel power system. Optimized operational decisions for each time period are generated dynamically considering the path-dependent nature of the optimal dispatch policy, given the plausible future realizations of the wind power production. A numerical case study is analyzed and it is demonstrated that the proposed stochastic dynamic optimization model significantly outperforms the traditional deterministic dispatch strategies

Simplified Multi-Stage and Per Capita Convergence: an analysis of two climate regimes for differentiation of commitments

NARCIS (Netherlands)

Elzen MGJ den; Berk MM; Lucas P; KMD

2004-01-01

This report describes and analyses in detail two climate regimes for differentiating commitments: the simplified Multi-Stage and Per Capita Convergence approaches. The Multi-Stage approach consists of a system to divide countries into groups with different types of commitments (stages). The Per

Test Information Targeting Strategies for Adaptive Multistage Testing Designs.

Science.gov (United States)

Luecht, Richard M.; Burgin, William

Adaptive multistage testlet (MST) designs appear to be gaining popularity for many large-scale computer-based testing programs. These adaptive MST designs use a modularized configuration of preconstructed testlets and embedded score-routing schemes to prepackage different forms of an adaptive test. The conditional information targeting (CIT)…

Short-Term Wind Electric Power Forecasting Using a Novel Multi-Stage Intelligent Algorithm

Directory of Open Access Journals (Sweden)

Haoran Zhao

2018-03-01

Full Text Available As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N decomposition approach, the Least Square Support Vector Machine (LSSVM, and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA. For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM, and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM, it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.

Multi-stage selective catalytic reduction of NOx in lean burn engine exhaust

Energy Technology Data Exchange (ETDEWEB)

Penetrante, B.M.; Hsaio, M.C.; Merritt, B.T.; Vogtlin, G.E. [Lawrence Livermore National Lab., CA (United States)

1997-12-31

Many studies suggest that the conversion of NO to NO{sub 2} is an important intermediate step in the selective catalytic reduction (SCR) of NO{sub x} to N{sub 2}. Some effort has been devoted to separating the oxidative and reductive functions of the catalyst in a multi-stage system. This method works fine for systems that require hydrocarbon addition. The hydrocarbon has to be injected between the NO oxidation catalyst and the NO{sub 2} reduction catalyst; otherwise, the first-stage oxidation catalyst will also oxidize the hydrocarbon and decrease its effectiveness as a reductant. The multi-stage catalytic scheme is appropriate for diesel engine exhausts since they contain insufficient hydrocarbons for SCR, and the hydrocarbons can be added at the desired location. For lean-burn gasoline engine exhausts, the hydrocarbons already present in the exhausts will make it necessary to find an oxidation catalyst that can oxidize NO to NO{sub 2} but not oxidize the hydrocarbon. A plasma can also be used to oxidize NO to NO{sub 2}. Plasma oxidation has several advantages over catalytic oxidation. Plasma-assisted catalysis can work well for both diesel engine and lean-burn gasoline engine exhausts. This is because the plasma can oxidize NO in the presence of hydrocarbons without degrading the effectiveness of the hydrocarbon as a reductant for SCR. In the plasma, the hydrocarbon enhances the oxidation of NO, minimizes the electrical energy requirement, and prevents the oxidation of SO{sub 2}. This paper discusses the use of multi-stage systems for selective catalytic reduction of NO{sub x}. The multi-stage catalytic scheme is compared to the plasma-assisted catalytic scheme.

Model Selection with the Linear Mixed Model for Longitudinal Data

Science.gov (United States)

Ryoo, Ji Hoon

2011-01-01

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

Application of linearized model to the stability analysis of the pressurized water reactor

International Nuclear Information System (INIS)

Li Haipeng; Huang Xiaojin; Zhang Liangju

2008-01-01

A Linear Time-Invariant model of the Pressurized Water Reactor is formulated through the linearization of the nonlinear model. The model simulation results show that the linearized model agrees well with the nonlinear model under small perturbation. Based upon the Lyapunov's First Method, the linearized model is applied to the stability analysis of the Pressurized Water Reactor. The calculation results show that the methodology of linearization to stability analysis is conveniently feasible. (authors)

Effectiveness of multi-stage scrubbers in reducing emissions of air pollutants from pig houses

OpenAIRE

Zhao, Y.; Aarnink, A.J.A.; Jong, de, M.C.M.; Ogink, N.W.M.; Groot Koerkamp, P.W.G.

2011-01-01

Emissions of air pollutants from livestock houses may raise environmental problems and pose hazards to public health. They can be reduced by scrubbers installed at the air outlets of livestock houses. In this study, three multi-stage scrubbers were evaluated in terms of their effectiveness in reducing emissions of airborne dust, total bacteria, ammonia, and CO2 from pig houses in winter. The three multi-stage scrubbers were one double-stage scrubber (acid stage+ bio-filter), one double-stage ...

A Non-linear Stochastic Model for an Office Building with Air Infiltration

DEFF Research Database (Denmark)

Thavlov, Anders; Madsen, Henrik

2015-01-01

This paper presents a non-linear heat dynamic model for a multi-room office building with air infiltration. Several linear and non-linear models, with and without air infiltration, are investigated and compared. The models are formulated using stochastic differential equations and the model...

Multivariate covariance generalized linear models

DEFF Research Database (Denmark)

Bonat, W. H.; Jørgensen, Bent

2016-01-01

are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions......We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...

A multi-stage noise adaptive switching filter for extremely corrupted images

Science.gov (United States)

Dinh, Hai; Adhami, Reza; Wang, Yi

2015-07-01

A multi-stage noise adaptive switching filter (MSNASF) is proposed for the restoration of images extremely corrupted by impulse and impulse-like noise. The filter consists of two steps: noise detection and noise removal. The proposed extrema-based noise detection scheme utilizes the false contouring effect to get better over detection rate at low noise density. It is adaptive and will detect not only impulse but also impulse-like noise. In the noise removal step, a novel multi-stage filtering scheme is proposed. It replaces corrupted pixel with the nearest uncorrupted median to preserve details. When compared with other methods, MSNASF provides better peak signal to noise ratio (PSNR) and structure similarity index (SSIM). A subjective evaluation carried out online also demonstrates that MSNASF yields higher fidelity.

Separation of a multicomponent mixture by gaseous diffusion: modelization of the enrichment in a capillary - application to a pilot cascade

International Nuclear Information System (INIS)

Doneddu, F.

1982-01-01

Starting from the modelization of gaseous flow in a porous medium (flow in a capillary), we generalize the law of enrichment in an infinite cylindrical capillary, established for an isotropic linear mixture, to a multicomponent mixture. A generalization is given of the notion of separation yields and characteristic pressure classically used for separations of isotropic linear mixtures. We present formulas for diagonalizing the diffusion operator, modelization of a multistage, gaseous diffusion cascade and comparison with the experimental results of a drain cascade (N 2 -SF 6 -UF 6 mixture). [fr

Modelling a linear PM motor including magnetic saturation

NARCIS (Netherlands)

Polinder, H.; Slootweg, J.G.; Compter, J.C.; Hoeijmakers, M.J.

2002-01-01

The use of linear permanent-magnet (PM) actuators increases in a wide variety of applications because of the high force density, robustness and accuracy. The paper describes the modelling of a linear PM motor applied in, for example, wafer steppers, including magnetic saturation. This is important

Genomic prediction based on data from three layer lines using non-linear regression models.

Science.gov (United States)

Huang, Heyun; Windig, Jack J; Vereijken, Addie; Calus, Mario P L

2014-11-06

Most studies on genomic prediction with reference populations that include multiple lines or breeds have used linear models. Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods. In an attempt to alleviate potential discrepancies between assumptions of linear models and multi-population data, two types of alternative models were used: (1) a multi-trait genomic best linear unbiased prediction (GBLUP) model that modelled trait by line combinations as separate but correlated traits and (2) non-linear models based on kernel learning. These models were compared to conventional linear models for genomic prediction for two lines of brown layer hens (B1 and B2) and one line of white hens (W1). The three lines each had 1004 to 1023 training and 238 to 240 validation animals. Prediction accuracy was evaluated by estimating the correlation between observed phenotypes and predicted breeding values. When the training dataset included only data from the evaluated line, non-linear models yielded at best a similar accuracy as linear models. In some cases, when adding a distantly related line, the linear models showed a slight decrease in performance, while non-linear models generally showed no change in accuracy. When only information from a closely related line was used for training, linear models and non-linear radial basis function (RBF) kernel models performed similarly. The multi-trait GBLUP model took advantage of the estimated genetic correlations between the lines. Combining linear and non-linear models improved the accuracy of multi-line genomic prediction. Linear models and non-linear RBF models performed very similarly for genomic prediction, despite the expectation that non-linear models could deal better with the heterogeneous multi-population data. This heterogeneity of the data can be overcome by modelling trait by line combinations as separate but correlated traits, which avoids the occasional

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

Science.gov (United States)

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

General mirror pairs for gauged linear sigma models

Energy Technology Data Exchange (ETDEWEB)

Aspinwall, Paul S.; Plesser, M. Ronen [Departments of Mathematics and Physics, Duke University,Box 90320, Durham, NC 27708-0320 (United States)

2015-11-05

We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.

General mirror pairs for gauged linear sigma models

International Nuclear Information System (INIS)

Aspinwall, Paul S.; Plesser, M. Ronen

2015-01-01

We carefully analyze the conditions for an abelian gauged linear σ-model to exhibit nontrivial IR behavior described by a nonsingular superconformal field theory determining a superstring vacuum. This is done without reference to a geometric phase, by associating singular behavior to a noncompact space of (semi-)classical vacua. We find that models determined by reflexive combinatorial data are nonsingular for generic values of their parameters. This condition has the pleasant feature that the mirror of a nonsingular gauged linear σ-model is another such model, but it is clearly too strong and we provide an example of a non-reflexive mirror pair. We discuss a weaker condition inspired by considering extremal transitions, which is also mirror symmetric and which we conjecture to be sufficient. We apply these ideas to extremal transitions and to understanding the way in which both Berglund-Hübsch mirror symmetry and the Vafa-Witten mirror orbifold with discrete torsion can be seen as special cases of the general combinatorial duality of gauged linear σ-models. In the former case we encounter an example showing that our weaker condition is still not necessary.

Half-trek criterion for generic identifiability of linear structural equation models

NARCIS (Netherlands)

Foygel, R.; Draisma, J.; Drton, M.

2012-01-01

A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

Half-trek criterion for generic identifiability of linear structural equation models

NARCIS (Netherlands)

Foygel, R.; Draisma, J.; Drton, M.

2011-01-01

A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a mixed graph in which directed edges encode the linear equations, and bidirected edges indicate possible correlations

Aerodynamic Analysis and Three-Dimensional Redesign of a Multi-Stage Axial Flow Compressor

Directory of Open Access Journals (Sweden)

Tao Ning

2016-04-01

Full Text Available This paper describes the introduction of three-dimension (3-D blade designs into a 5-stage axial compressor with multi-stage computational fluid dynamic (CFD methods. Prior to a redesign, a validation study is conducted for the overall performance and flow details based on full-scale test data, proving that the multi-stage CFD applied is a relatively reliable tool for the analysis of the follow-up redesign. Furthermore, at the near stall point, the aerodynamic analysis demonstrates that significant separation exists in the last stator, leading to the aerodynamic redesign, which is the focus of the last stator. Multi-stage CFD methods are applied throughout the three-dimensional redesign process for the last stator to explore their aerodynamic improvement potential. An unconventional asymmetric bow configuration incorporated with leading edge re-camber and re-solidity is employed to reduce the high loss region dominated by the mainstream. The final redesigned version produces a 13% increase in the stall margin while maintaining the efficiency at the design point.

An integrated practical implementation of continuous aqueous two-phase systems for the recovery of human IgG: From the microdevice to a multistage bench-scale mixer-settler device.

Science.gov (United States)

Espitia-Saloma, Edith; Vâzquez-Villegas, Patricia; Rito-Palomares, Marco; Aguilar, Oscar

2016-05-01

Aqueous two-phase systems (ATPS) are a liquid-liquid extraction technology with clear process benefits; however, its lack of industrial embracement is still a challenge to overcome. Antibodies are a potential product to be recovered by ATPS in a commercial context. The objective of this work is to present a more integral approach of the different isolated strategies that have arisen in order to enable a practical, generic implementation of ATPS, using human immunoglobulin G (IgG) as experimental model. A microfluidic device is used for ATPS parameters preselection for product recovery. ATPS were continuously operated in a mixer-settler device in one stage, multistage and multistage with recirculation configuration. Single-stage pure IgG extraction with a polyethylene glycol (PEG) 3350-phophates ATPS within continuous operation allowed a 65% recovery. Further implementation of a multistage platform promoted a higher particle partitioning reaching a 90% recovery. The processing of IgG from a cell supernatant culture harvest in a multistage system with top phase recirculation resulted in 78% IgG recovery in bottom phase. This work conjugates three not widely spread methodologies for ATPS: microfluidics, continuous and multistage operation. Copyright © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Generalized Linear Models with Applications in Engineering and the Sciences

CERN Document Server

Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J

2012-01-01

Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma

Synthetic Multiple-Imputation Procedure for Multistage Complex Samples

Directory of Open Access Journals (Sweden)

Zhou Hanzhi

2016-03-01

Full Text Available Multiple imputation (MI is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models. For multistage stratified clustered designs, this requires dummy variables to represent strata as well as primary sampling units (PSUs nested within each stratum in the imputation model. Such a modeling strategy is not only operationally burdensome but also inferentially inefficient when there are many strata in the sample design. Complexity only increases when sampling weights need to be modeled. This article develops a generalpurpose analytic strategy for population inference from complex sample designs with item-level missingness. In a simulation study, the proposed procedures demonstrate efficient estimation and good coverage properties. We also consider an application to accommodate missing body mass index (BMI data in the analysis of BMI percentiles using National Health and Nutrition Examination Survey (NHANES III data. We argue that the proposed methods offer an easy-to-implement solution to problems that are not well-handled by current MI techniques. Note that, while the proposed method borrows from the MI framework to develop its inferential methods, it is not designed as an alternative strategy to release multiply imputed datasets for complex sample design data, but rather as an analytic strategy in and of itself.

Influence of dispatching rules on average production lead time for multi-stage production systems.

Science.gov (United States)

Hübl, Alexander; Jodlbauer, Herbert; Altendorfer, Klaus

2013-08-01

In this paper the influence of different dispatching rules on the average production lead time is investigated. Two theorems based on covariance between processing time and production lead time are formulated and proved theoretically. Theorem 1 links the average production lead time to the "processing time weighted production lead time" for the multi-stage production systems analytically. The influence of different dispatching rules on average lead time, which is well known from simulation and empirical studies, can be proved theoretically in Theorem 2 for a single stage production system. A simulation study is conducted to gain more insight into the influence of dispatching rules on average production lead time in a multi-stage production system. We find that the "processing time weighted average production lead time" for a multi-stage production system is not invariant of the applied dispatching rule and can be used as a dispatching rule independent indicator for single-stage production systems.

A penalized framework for distributed lag non-linear models.

Science.gov (United States)

Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G

2017-09-01

Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

Science.gov (United States)

Kawashima, Issaku; Kumano, Hiroaki

2017-01-01

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

Directory of Open Access Journals (Sweden)

Issaku Kawashima

2017-07-01

Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Game Theory and its Relationship with Linear Programming Models ...

African Journals Online (AJOL)

Game Theory and its Relationship with Linear Programming Models. ... This paper shows that game theory and linear programming problem are closely related subjects since any computing method devised for ... AJOL African Journals Online.

A theoretical analysis of price elasticity of energy demand in multi-stage energy conversion systems

International Nuclear Information System (INIS)

Lowe, Robert

2003-01-01

The objective of this paper is an analytical exploration of the problem of price elasticity of energy demand in multi-stage energy conversion systems. The paper describes in some detail an analytical model of energy demand in such systems. Under a clearly stated set of assumptions, the model makes it possible to explore both the impacts of the number of sub-systems, and of varying sub-system elasticities on overall system elasticity. The analysis suggests that overall price elasticity of energy demand for such systems will tend asymptotically to unity as the number of sub-systems increases

New improved counter - current multi-stage centrifugal extractor for solvent extraction process

International Nuclear Information System (INIS)

Gheorghe, Ionita; Mirica, Dumitru; Croitoru, Cornelia; Stefanescu, Ioan; Retegan, Teodora; Kitamoto, Asashi

2003-01-01

Total actinide recovery, lanthanide/actinide separation and selective partitioning of actinide from high level waste (HLW) are nowadays of a major interest. Actinide partitioning with a view to safe disposing of HLW or utilization in many other applications of recovered elements involves an extraction process usually by means of mixer-settler, pulse column or centrifugal contactor. The latter, presents some doubtless advantages and responds to the above mentioned goals. A new type of counter-current multistage centrifugal extractor has been designed and performed. A similar apparatus was not found from in other published papers as yet. The counter-current multi-stage centrifugal extractor was a cylinder made of stainless steel with an effective length of 346 mm, the effective diameter of 100 mm and a volume of 1.5 liters, working in a horizontal position. The new internal structure and geometry of the new advanced centrifugal extractor consisting of nine cells (units), five rotation units, two mixing units, two propelling units and two final plates, ensures the counter-current running of the two phases.The central shaft having the rotation cells fixed on it is coupled by an intermediary connection to a electric motor of high rotation speed. The conceptual layout of the advanced counter-current multi-stage centrifugal extractor is presented. The newly designed extractor has been tested at 500-2800 rot/min for a ratio of the aqueous/organic phase =1 to examine the mechanical behavior and the hydrodynamics of the two phases in countercurrent. The results showed that the performances have been generally good and the design requirements were fulfilled. The newly designed counter-current multistage centrifugal extractor appears to be a promising way to increase extraction rate of radionuclides and metals from liquid effluents. (authors)

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

Science.gov (United States)

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

Double generalized linear compound poisson models to insurance claims data

DEFF Research Database (Denmark)

Andersen, Daniel Arnfeldt; Bonat, Wagner Hugo

2017-01-01

This paper describes the specification, estimation and comparison of double generalized linear compound Poisson models based on the likelihood paradigm. The models are motivated by insurance applications, where the distribution of the response variable is composed by a degenerate distribution...... implementation and illustrate the application of double generalized linear compound Poisson models using a data set about car insurances....

Reliability modelling and simulation of switched linear system ...

African Journals Online (AJOL)

Reliability modelling and simulation of switched linear system control using temporal databases. ... design of fault-tolerant real-time switching systems control and modelling embedded micro-schedulers for complex systems maintenance.

Calculation of recovery plasticity in multistage hot forging under isothermal conditions.

Science.gov (United States)

Zhbankov, Iaroslav G; Perig, Alexander V; Aliieva, Leila I

2016-01-01

A widely used method for hot forming steels and alloys, especially heavy forging, is the process of multistage forging with pauses between stages. The well-known effect which accompanies multistage hot forging is metal plasticity recovery in comparison with monotonic deformation. A method which takes into consideration the recovery of plasticity in pauses between hot deformations of a billet under isothermal conditions is proposed. This method allows the prediction of billet forming limits as a function of deformation during the forging stage and the duration of the pause between the stages. This method takes into account the duration of pauses between deformations and the magnitude of subdivided deformations. A hot isothermal upsetting process with pauses was calculated by the proposed method. Results of the calculations have been confirmed with experimental data.

Mixed models, linear dependency, and identification in age-period-cohort models.

Science.gov (United States)

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

A fuzzy Bi-linear management model in reverse logistic chains

Directory of Open Access Journals (Sweden)

Tadić Danijela

2016-01-01

Full Text Available The management of the electrical and electronic waste (WEEE problem in the uncertain environment has a critical effect on the economy and environmental protection of each region. The considered problem can be stated as a fuzzy non-convex optimization problem with linear objective function and a set of linear and non-linear constraints. The original problem is reformulated by using linear relaxation into a fuzzy linear programming problem. The fuzzy rating of collecting point capacities and fix costs of recycling centers are modeled by triangular fuzzy numbers. The optimal solution of the reformulation model is found by using optimality concept. The proposed model is verified through an illustrative example with real-life data. The obtained results represent an input for future research which should include a good benchmark base for tested reverse logistic chains and their continuous improvement. [Projekat Ministarstva nauke Republike Srbije, br. 035033: Sustainable development technology and equipment for the recycling of motor vehicles

Optimizing Water Allocation under Uncertain System Conditions for Water and Agriculture Future Scenarios in Alfeios River Basin (Greece—Part B: Fuzzy-Boundary Intervals Combined with Multi-Stage Stochastic Programming Model

Directory of Open Access Journals (Sweden)

Eleni Bekri

2015-11-01

Full Text Available Optimal water allocation within a river basin still remains a great modeling challenge for engineers due to various hydrosystem complexities, parameter uncertainties and their interactions. Conventional deterministic optimization approaches have given their place to stochastic, fuzzy and interval-parameter programming approaches and their hybrid combinations for overcoming these difficulties. In many countries, including Mediterranean countries, water resources management is characterized by uncertain, imprecise and limited data because of the absence of permanent measuring systems, inefficient river monitoring and fragmentation of authority responsibilities. A fuzzy-boundary-interval linear programming methodology developed by Li et al. (2010 is selected and applied in the Alfeios river basin (Greece for optimal water allocation under uncertain system conditions. This methodology combines an ordinary multi-stage stochastic programming with uncertainties expressed as fuzzy-boundary intervals. Upper- and lower-bound solution intervals for optimized water allocation targets and probabilistic water allocations and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios for an optimistic and a pessimistic attitude of the decision makers. In this work, the uncertainty of the random water inflows is incorporated through the simultaneous generation of stochastic equal-probability hydrologic scenarios at various inflow positions instead of using a scenario-tree approach in the original methodology.

Identification of Influential Points in a Linear Regression Model

Directory of Open Access Journals (Sweden)

Jan Grosz

2011-03-01

Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

Linear induction accelerators

International Nuclear Information System (INIS)

Briggs, R.J.

1986-06-01

The development of linear induction accelerators has been motivated by applications requiring high-pulsed currents of charged particles at voltages exceeding the capability of single-stage, diode-type accelerators and at currents too high for rf accelerators. In principle, one can accelerate charged particles to arbitrarily high voltages using a multi-stage induction machine, but the 50-MeV, 10-kA Advanced Test Accelerator (ATA) at LLNL is the highest voltage machine in existence at this time. The advent of magnetic pulse power systems makes sustained operation at high-repetition rates practical, and this capability for high-average power is very likely to open up many new applications of induction machines in the future. This paper surveys the US induction linac technology with primary emphasis on electron machines. A simplified description of how induction machines couple energy to the electron beam is given, to illustrate many of the general issues that bound the design space of induction linacs

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Matrix model and time-like linear dila ton matter

International Nuclear Information System (INIS)

Takayanagi, Tadashi

2004-01-01

We consider a matrix model description of the 2d string theory whose matter part is given by a time-like linear dilaton CFT. This is equivalent to the c=1 matrix model with a deformed, but very simple Fermi surface. Indeed, after a Lorentz transformation, the corresponding 2d spacetime is a conventional linear dila ton background with a time-dependent tachyon field. We show that the tree level scattering amplitudes in the matrix model perfectly agree with those computed in the world-sheet theory. The classical trajectories of fermions correspond to the decaying D-boranes in the time-like linear dilaton CFT. We also discuss the ground ring structure. Furthermore, we study the properties of the time-like Liouville theory by applying this matrix model description. We find that its ground ring structure is very similar to that of the minimal string. (author)

Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

Energy Technology Data Exchange (ETDEWEB)

Bernstein, Andrey; Dall' Anese, Emiliano

2017-05-26

This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.

Contact force and mechanical loss of multistage cable under tension and bending

Science.gov (United States)

Ru, Yanyun; Yong, Huadong; Zhou, Youhe

2016-10-01

A theoretical model for calculating the stress and strain states of cabling structures with different loadings has been developed in this paper. We solve the problem for the first- and second-stage cable with tensile or bending strain. The contact and friction forces between the strands are presented by two-dimensional contact model. Several theoretical models have been proposed to verify the results when the triplet subjected to the tensile strain, including contact force, contact stresses, and mechanical loss. It is found that loadings will affect the friction force and the mechanical loss of the triplet. The results show that the contact force and mechanical loss are dependent on the twist pitch. A shorter twist pitch can lead to higher contact force, while the trend of mechanical loss with twist pitch is complicated. The mechanical loss may be reduced by adjusting the twist pitch reasonably. The present model provides a simple analysis method to investigate the mechanical behaviors in multistage-structures under different loads.

Linear mixed models a practical guide using statistical software

CERN Document Server

West, Brady T; Galecki, Andrzej T

2006-01-01

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo

The Expected Loss in the Discretization of Multistage Stochastic Programming Problems - Estimation and Convergence Rate

Czech Academy of Sciences Publication Activity Database

Šmíd, Martin

2009-01-01

Roč. 165, č. 1 (2009), s. 29-45 ISSN 0254-5330 R&D Projects: GA ČR GA402/04/1294 Institutional research plan: CEZ:AV0Z10750506 Keywords : multistage stochastic programming problems * approximation * discretization * Monte Carlo Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.961, year: 2009 http://library.utia.cas.cz/separaty/2008/E/smid-the expected loss in the discretization of multistage stochastic programming problems - estimation and convergence rate.pdf

Optimal designs for linear mixture models

NARCIS (Netherlands)

Mendieta, E.J.; Linssen, H.N.; Doornbos, R.

1975-01-01

In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of

Functional linear models for association analysis of quantitative traits.

Science.gov (United States)

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Science.gov (United States)

Champion, Billy Ray

projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of "of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency's traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

Renormalization a la BRS of the non-linear σ-model

International Nuclear Information System (INIS)

Blasi, A.; Collina, R.

1987-01-01

We characterize the non-linear O(N+1) σ-model in an arbitrary parametrization with a nihilpotent BRS operator obtained from the symmetry transformation by the use of anticommuting parameters. The identity can be made compatible with the presence of a mass term in the model, so we can analyze its stability and prove that the model is anomaly free. This procedure avoids many problems encountered in the conventional analysis; in particular the introduction of an infinite number of sources coupled to the successive variations of the field is not necessary and the linear O(N) symmetry is respected as a consequence of the identity. The approach may provide useful in discussing the renormalizability of a wider class of models with non-linear symmetries. (orig.)

Robust Comparison of the Linear Model Structures in Self-tuning Adaptive Control

DEFF Research Database (Denmark)

Zhou, Jianjun; Conrad, Finn

1989-01-01

The Generalized Predictive Controller (GPC) is extended to the systems with a generalized linear model structure which contains a number of choices of linear model structures. The Recursive Prediction Error Method (RPEM) is used to estimate the unknown parameters of the linear model structures...... to constitute a GPC self-tuner. Different linear model structures commonly used are compared and evaluated by applying them to the extended GPC self-tuner as well as to the special cases of the GPC, the GMV and MV self-tuners. The simulation results show how the choice of model structure affects the input......-output behaviour of self-tuning controllers....

Multicollinearity in hierarchical linear models.

Science.gov (United States)

Yu, Han; Jiang, Shanhe; Land, Kenneth C

2015-09-01

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

Optimal designs for linear mixture models

NARCIS (Netherlands)

Mendieta, E.J.; Linssen, H.N.; Doornbos, R.

1975-01-01

In a recent paper Snee and Marquardt [8] considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of this

Low-energy limit of the extended Linear Sigma Model

Energy Technology Data Exchange (ETDEWEB)

Divotgey, Florian [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); Kovacs, Peter [Wigner Research Center for Physics, Hungarian Academy of Sciences, Institute for Particle and Nuclear Physics, Budapest (Hungary); GSI Helmholtzzentrum fuer Schwerionenforschung, ExtreMe Matter Institute, Darmstadt (Germany); Giacosa, Francesco [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); Jan-Kochanowski University, Institute of Physics, Kielce (Poland); Rischke, Dirk H. [Johann Wolfgang Goethe-Universitaet, Institut fuer Theoretische Physik, Frankfurt am Main (Germany); University of Science and Technology of China, Interdisciplinary Center for Theoretical Study and Department of Modern Physics, Hefei, Anhui (China)

2018-01-15

The extended Linear Sigma Model is an effective hadronic model based on the linear realization of chiral symmetry SU(N{sub f}){sub L} x SU(N{sub f}){sub R}, with (pseudo)scalar and (axial-)vector mesons as degrees of freedom. In this paper, we study the low-energy limit of the extended Linear Sigma Model (eLSM) for N{sub f} = flavors by integrating out all fields except for the pions, the (pseudo-)Nambu-Goldstone bosons of chiral symmetry breaking. The resulting low-energy effective action is identical to Chiral Perturbation Theory (ChPT) after choosing a representative for the coset space generated by chiral symmetry breaking and expanding it in powers of (derivatives of) the pion fields. The tree-level values of the coupling constants of the effective low-energy action agree remarkably well with those of ChPT. (orig.)

A national-scale model of linear features improves predictions of farmland biodiversity.

Science.gov (United States)

Sullivan, Martin J P; Pearce-Higgins, James W; Newson, Stuart E; Scholefield, Paul; Brereton, Tom; Oliver, Tom H

2017-12-01

Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications . This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

The task of validation of gas-dynamic characteristics of a multistage centrifugal compressor for a natural gas booster compressor station

Science.gov (United States)

Danilishin, A. M.; Kozhukhov, Y. V.; Neverov, V. V.; Malev, K. G.; Mironov, Y. R.

2017-08-01

The aim of this work is the validation study for the numerical modeling of characteristics of a multistage centrifugal compressor for natural gas. In the research process was the analysis used grid interfaces and software systems. The result revealed discrepancies between the simulated and experimental characteristics and outlined the future work plan.

Modeling containment of large wildfires using generalized linear mixed-model analysis

Science.gov (United States)

Mark Finney; Isaac C. Grenfell; Charles W. McHugh

2009-01-01

Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and...

A test for the parameters of multiple linear regression models ...

African Journals Online (AJOL)

A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

Development of JSTAMP-Works/NV and HYSTAMP for Multipurpose Multistage Sheet Metal Forming Simulation

Science.gov (United States)

Umezu, Yasuyoshi; Watanabe, Yuko; Ma, Ninshu

2005-08-01

Since 1996, Japan Research Institute Limited (JRI) has been providing a sheet metal forming simulation system called JSTAMP-Works packaged the FEM solvers of LS-DYNA and JOH/NIKE, which might be the first multistage system at that time and has been enjoying good reputation among users in Japan. To match the recent needs, "faster, more accurate and easier", of process designers and CAE engineers, a new metal forming simulation system JSTAMP-Works/NV is developed. The JSTAMP-Works/NV packaged the automatic healing function of CAD and had much more new capabilities such as prediction of 3D trimming lines for flanging or hemming, remote control of solver execution for multi-stage forming processes and shape evaluation between FEM and CAD. On the other way, a multi-stage multi-purpose inverse FEM solver HYSTAMP is developed and will be soon put into market, which is approved to be very fast, quite accurate and robust. Lastly, authors will give some application examples of user defined ductile damage subroutine in LS-DYNA for the estimation of material failure and springback in metal forming simulation.

Development of JSTAMP-Works/NV and HYSTAMP for Multipurpose Multistage Sheet Metal Forming Simulation

International Nuclear Information System (INIS)

Umezu, Yasuyoshi; Watanabe, Yuko; Ma, Ninshu

2005-01-01

Since 1996, Japan Research Institute Limited (JRI) has been providing a sheet metal forming simulation system called JSTAMP-Works packaged the FEM solvers of LS-DYNA and JOH/NIKE, which might be the first multistage system at that time and has been enjoying good reputation among users in Japan. To match the recent needs, 'faster, more accurate and easier', of process designers and CAE engineers, a new metal forming simulation system JSTAMP-Works/NV is developed. The JSTAMP-Works/NV packaged the automatic healing function of CAD and had much more new capabilities such as prediction of 3D trimming lines for flanging or hemming, remote control of solver execution for multi-stage forming processes and shape evaluation between FEM and CAD.On the other way, a multi-stage multi-purpose inverse FEM solver HYSTAMP is developed and will be soon put into market, which is approved to be very fast, quite accurate and robust.Lastly, authors will give some application examples of user defined ductile damage subroutine in LS-DYNA for the estimation of material failure and springback in metal forming simulation

Time stamp technique using a nuclear emulsion multi-stage shifter for gamma-ray telescope

International Nuclear Information System (INIS)

Takahashi, Satoru; Aoki, Shigeki; Rokujo, Hiroki; Hamada, Kaname; Komatsu, Masahiro; Morishima, Kunihiro; Nakamura, Mitsuhiro; Nakano, Toshiyuki; Niwa, Kimio; Sato, Osamu; Yoshioka, Teppei; Kodama, Koichi

2010-01-01

Nuclear emulsion has a potential use as a gamma-ray telescope with high angular resolution. For this application it is necessary to know the time when each track was recorded in the emulsion. In previous experiments using nuclear emulsion, various efforts were used to associate time to nuclear emulsion tracks and to improve the time resolution. Using a high speed readout system for nuclear emulsion together with a clock-based multi-stage emulsion shifter, we invented a technique to give a time-stamp to emulsion tracks and greatly improve the time resolution. A test experiment with a 2-stage shifter was used to demonstrate the principle of multi-stage shifting, and we achieved a time resolution 1.5 s for 12.1 h (about 1 part in 29 000) with the time stamp reliability 97% and the time stamp efficiency 98%. This multi-stage shifter can achieve the time resolution required for a gamma-ray telescope and can also be applied to another cosmic ray observations and accelerator experiments using nuclear emulsion.

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

Science.gov (United States)

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

2015-08-01

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

Nonlinear Modeling by Assembling Piecewise Linear Models

Science.gov (United States)

Yao, Weigang; Liou, Meng-Sing

2013-01-01

To preserve nonlinearity of a full order system over a parameters range of interest, we propose a simple modeling approach by assembling a set of piecewise local solutions, including the first-order Taylor series terms expanded about some sampling states. The work by Rewienski and White inspired our use of piecewise linear local solutions. The assembly of these local approximations is accomplished by assigning nonlinear weights, through radial basis functions in this study. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving at different Mach numbers and pitching motions, under which the flow exhibits prominent nonlinear behaviors. All results confirm that our nonlinear model is accurate and stable for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robustness-accurate for inputs considerably different from the base trajectory in form and magnitude. This modeling preserves nonlinearity of the problems considered in a rather simple and accurate manner.

A remark on empirical estimates in multistage stochastic programming

Czech Academy of Sciences Publication Activity Database

Kaňková, Vlasta

2002-01-01

Roč. 9, č. 17 (2002), s. 31-50 ISSN 1212-074X R&D Projects: GA ČR GA402/01/0539; GA ČR GA402/02/1015; GA ČR GA402/01/0034 Institutional research plan: CEZ:AV0Z1075907 Keywords : multistage stochastic programming * empirical estimates * Markov dependence Subject RIV: BB - Applied Statistics, Operational Research

Simulated dynamic response of a multi-stage compressor with variable molecular weight flow medium

Science.gov (United States)

Babcock, Dale A.

1995-01-01

A mathematical model of a multi-stage compressor with variable molecular weight flow medium is derived. The modeled system consists of a five stage, six cylinder, double acting, piston type compressor. Each stage is followed by a water cooled heat exchanger which serves to transfer the heat of compression from the gas. A high molecular weight gas (CFC-12) mixed with air in varying proportions is introduced to the suction of the compressor. Condensation of the heavy gas may occur in the upper stage heat exchangers. The state equations for the system are integrated using the Advanced Continuous Simulation Language (ACSL) for determining the system's dynamic and steady state characteristics under varying operating conditions.

Hydrogen iodide processing section in a thermochemical water-splitting iodine-sulfur process using a multistage hydrogen iodide decomposer

International Nuclear Information System (INIS)

Ohashi, Hirofumi; Sakaba, Nariaki; Imai, Yoshiyuki; Kubo, Shinji; Sato, Hiroyuki; Tachibana, Yukio; Kunitomi, Kazuhiko; Kato, Ryoma

2009-01-01

A multistage hydrogen iodide (HI) decomposer (repetition of HI decomposition reaction and removal of product iodine by a HIx solution) in a thermochemical water-splitting iodine-sulfur process for hydrogen production using high-temperature heat from the high-temperature gas-cooled reactor was numerically evaluated, especially in terms of the flow rate of undecomposed HI and product iodine at the outlet of the decomposer, in order to reduce the total heat transfer area of heat exchangers for the recycle of undecomposed HI and to eliminate components for the separation. A suitable configuration of the multistage HI decomposer was countercurrent rather than concurrent, and the HIx solution from an electro-electro dialysis at a low temperature was a favorable feed condition for the multistage HI decomposer. The flow rate of undecomposed HI and product iodine at the outlet of the multistage HI decomposer was significantly lower than that of the conventional HI decomposer, because the conversion was increased, and HI and iodine were removed by the HIx solution. Based on this result, an alternative HI processing section using the multistage HI decomposer and eliminating some recuperators, coolers, and components for the separation was proposed and evaluated. The total heat transfer area of heat exchangers in the proposed HI processing section could be reduced to less than about 1/2 that in the conventional HI processing section. (author)

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

Comparing a single-stage geocoding method to a multi-stage geocoding method: how much and where do they disagree?

Directory of Open Access Journals (Sweden)

Rice Kenneth

2007-03-01

Full Text Available Abstract Background Geocoding methods vary among spatial epidemiology studies. Errors in the geocoding process and differential match rates may reduce study validity. We compared two geocoding methods using 8,157 Washington State addresses. The multi-stage geocoding method implemented by the state health department used a sequence of local and national reference files. The single-stage method used a single national reference file. For each address geocoded by both methods, we measured the distance between the locations assigned by each method. Area-level characteristics were collected from census data, and modeled as predictors of the discordance between geocoded address coordinates. Results The multi-stage method had a higher match rate than the single-stage method: 99% versus 95%. Of 7,686 addresses were geocoded by both methods, 96% were geocoded to the same census tract by both methods and 98% were geocoded to locations within 1 km of each other by the two methods. The distance between geocoded coordinates for the same address was higher in sparsely populated and low poverty areas, and counties with local reference files. Conclusion The multi-stage geocoding method had a higher match rate than the single-stage method. An examination of differences in the location assigned to the same address suggested that study results may be most sensitive to the choice of geocoding method in sparsely populated or low-poverty areas.

Plane answers to complex questions the theory of linear models

CERN Document Server

Christensen, Ronald

1987-01-01

This book was written to rigorously illustrate the practical application of the projective approach to linear models. To some, this may seem contradictory. I contend that it is possible to be both rigorous and illustrative and that it is possible to use the projective approach in practical applications. Therefore, unlike many other books on linear models, the use of projections and sub­ spaces does not stop after the general theory. They are used wherever I could figure out how to do it. Solving normal equations and using calculus (outside of maximum likelihood theory) are anathema to me. This is because I do not believe that they contribute to the understanding of linear models. I have similar feelings about the use of side conditions. Such topics are mentioned when appropriate and thenceforward avoided like the plague. On the other side of the coin, I just as strenuously reject teaching linear models with a coordinate free approach. Although Joe Eaton assures me that the issues in complicated problems freq...

A novel multi-stage direct contact membrane distillation module: Design, experimental and theoretical approaches

KAUST Repository

Lee, Jung Gil

2016-10-24

An economic desalination system with a small scale and footprint for remote areas, which have a limited and inadequate water supply, insufficient water treatment and low infrastructure, is strongly demanded in the desalination markets. Here, a direct contact membrane distillation (DCMD) process has the simplest configuration and potentially the highest permeate flux among all of the possible MD processes. This process can also be easily instituted in a multi-stage manner for enhanced compactness, productivity, versatility and cost-effectiveness. In this study, an innovative, multi-stage, DCMD module under countercurrent-flow configuration is first designed and then investigate both theoretically and experimentally to identify its feasibility and operability for desalination application. Model predictions and measured data for mean permeate flux are compared and shown to be in good agreement. The effect of the number of module stages on the mean permeate flux, performance ratio and daily water production of the MDCMD system has been theoretically identified at inlet feed and permeate flow rates of 1.5 l/min and inlet feed and permeate temperatures of 70 °C and 25 °C, respectively. The daily water production of a three-stage DCMD module with a membrane area of 0.01 m2 at each stage is found to be 21.5 kg.

Approximating chiral quark models with linear σ-models

International Nuclear Information System (INIS)

Broniowski, Wojciech; Golli, Bojan

2003-01-01

We study the approximation of chiral quark models with simpler models, obtained via gradient expansion. The resulting Lagrangian of the type of the linear σ-model contains, at the lowest level of the gradient-expanded meson action, an additional term of the form ((1)/(2))A(σ∂ μ σ+π∂ μ π) 2 . We investigate the dynamical consequences of this term and its relevance to the phenomenology of the soliton models of the nucleon. It is found that the inclusion of the new term allows for a more efficient approximation of the underlying quark theory, especially in those cases where dynamics allows for a large deviation of the chiral fields from the chiral circle, such as in quark models with non-local regulators. This is of practical importance, since the σ-models with valence quarks only are technically much easier to treat and simpler to solve than the quark models with the full-fledged Dirac sea

Multivariate statistical modelling based on generalized linear models

CERN Document Server

Fahrmeir, Ludwig

1994-01-01

This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...

Finiteness of Ricci flat supersymmetric non-linear sigma-models

International Nuclear Information System (INIS)

Alvarez-Gaume, L.; Ginsparg, P.

1985-01-01

Combining the constraints of Kaehler differential geometry with the universality of the normal coordinate expansion in the background field method, we study the ultraviolet behavior of 2-dimensional supersymmetric non-linear sigma-models with target space an arbitrary riemannian manifold M. We show that the constraint of N=2 supersymmetry requires that all counterterms to the metric beyond one-loop order are cohomologically trivial. It follows that such supersymmetric non-linear sigma-models defined on locally symmetric spaces are super-renormalizable and that N=4 models are on-shell ultraviolet finite to all orders of perturbation theory. (orig.)

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Robust Linear Models for Cis-eQTL Analysis.

Science.gov (United States)

Rantalainen, Mattias; Lindgren, Cecilia M; Holmes, Christopher C

2015-01-01

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

A model for optimization of process integration investments under uncertainty

International Nuclear Information System (INIS)

Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

2011-01-01

The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

Practical likelihood analysis for spatial generalized linear mixed models

DEFF Research Database (Denmark)

Bonat, W. H.; Ribeiro, Paulo Justiniano

2016-01-01

We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

European dominance in multistage ultramarathons: an analysis of finisher rate and performance trends from 1992 to 2010

Directory of Open Access Journals (Sweden)

Abou Shoak M

2013-01-01

Full Text Available Mohannad Abou Shoak,1 Beat Knechtle,1,2 Christoph Alexander Rüst,1 Romuald Lepers,3 Thomas Rosemann11Institute of General Practice and for Health Services Research, University of Zurich, Zurich, Switzerland; 2Gesundheitszentrum St Gallen, St Gallen, Switzerland; 3INSERM U1093, Faculty of Sport Sciences, University of Burgundy, Dijon, FranceBackground: Participation and performance trends regarding the nationality of ultraendurance athletes have been investigated in the triathlon, but not in running. The present study aimed to identify the countries in which multistage ultramarathons were held around the world and the nationalities of successful finishers.Methods: Finisher rates and performance trends of finishers in multistage ultramarathons held worldwide between 1992 and 2010 were investigated.Results: Between 1992 and 2010, the bulk of multistage ultramarathons were held in Germany and France, with more than 30 races organized in each country. Completion rates for men and women increased exponentially, with women representing on average 16.4% of the total field. Since 1992, 6480 athletes have competed in Morocco, 2538 in Germany, and 1842 in France. A total of 81.9% of athletes originated from Europe, and more specifically from France (22.9%, Great Britain (18.0%, and Germany (13.4%.Conclusion: European ultramarathoners dominated the athletes who completed multistage ultramarathons worldwide, with specific dominance of French, British, and German athletes. Future studies should investigate social aspects, such as sport tourism, among European athletes to understand why European athletes are so interested in participating in multistage ultramarathons.Keywords: ultraendurance, run, nationality, distance, stage

A linear model of ductile plastic damage

International Nuclear Information System (INIS)

Lemaitre, J.

1983-01-01

A three-dimensional model of isotropic ductile plastic damage based on a continuum damage variable on the effective stress concept and on thermodynamics is derived. As shown by experiments on several metals and alloys, the model, integrated in the case of proportional loading, is linear with respect to the accumulated plastic strain and shows a large influence of stress triaxiality [fr

Sphaleron in a non-linear sigma model

International Nuclear Information System (INIS)

Sogo, Kiyoshi; Fujimoto, Yasushi.

1989-08-01

We present an exact classical saddle point solution in a non-linear sigma model. It has a topological charge 1/2 and mediates the vacuum transition. The quantum fluctuations and the transition rate are also examined. (author)

Fast and local non-linear evolution of steep wave-groups on deep water: A comparison of approximate models to fully non-linear simulations

International Nuclear Information System (INIS)

Adcock, T. A. A.; Taylor, P. H.

2016-01-01

The non-linear Schrödinger equation and its higher order extensions are routinely used for analysis of extreme ocean waves. This paper compares the evolution of individual wave-packets modelled using non-linear Schrödinger type equations with packets modelled using fully non-linear potential flow models. The modified non-linear Schrödinger Equation accurately models the relatively large scale non-linear changes to the shape of wave-groups, with a dramatic contraction of the group along the mean propagation direction and a corresponding extension of the width of the wave-crests. In addition, as extreme wave form, there is a local non-linear contraction of the wave-group around the crest which leads to a localised broadening of the wave spectrum which the bandwidth limited non-linear Schrödinger Equations struggle to capture. This limitation occurs for waves of moderate steepness and a narrow underlying spectrum

Ground Motion Models for Future Linear Colliders

International Nuclear Information System (INIS)

Seryi, Andrei

2000-01-01

Optimization of the parameters of a future linear collider requires comprehensive models of ground motion. Both general models of ground motion and specific models of the particular site and local conditions are essential. Existing models are not completely adequate, either because they are too general, or because they omit important peculiarities of ground motion. The model considered in this paper is based on recent ground motion measurements performed at SLAC and at other accelerator laboratories, as well as on historical data. The issues to be studied for the models to become more predictive are also discussed

Predicting musically induced emotions from physiological inputs: linear and neural network models.

Science.gov (United States)

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Multi-Stage Admission Control for Load Balancing in Next Generation Systems

DEFF Research Database (Denmark)

Mihovska, Albena D.; Anggorojati, Bayu; Luo, Jijun

2008-01-01

This paper describes a load-dependent multi-stage admission control suitable for next generation systems. The concept uses decision polling in entities located at different levels of the architecture hierarchy and based on the load to activate a sequence of actions related to the admission...

Multi-stage fuzzy load frequency control using PSO

International Nuclear Information System (INIS)

Shayeghi, H.; Jalili, A.; Shayanfar, H.A.

2008-01-01

In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes

Multi-stage fuzzy load frequency control using PSO

Energy Technology Data Exchange (ETDEWEB)

Shayeghi, H. [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran); Jalili, A. [Islamic Azad University, Ardabil Branch, Ardabil (Iran); Shayanfar, H.A. [Center of Excellence for Power Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran)

2008-10-15

In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes. (author)

A study of routing algorithms for SCI-Based multistage networks

International Nuclear Information System (INIS)

Wu Bin; Kristiansen, E.; Skaali, B.; Bogaerts, A.; )

1994-03-01

The report deals with a particular class of multistage Scalable Coherent Interface (SCI) network systems and two important routing algorithms, namely self-routing and table-look up routing. The effect of routing delay on system performance is investigated by simulations. Adaptive routing and deadlock-free routing are studied. 8 refs., 11 figs., 1 tab

Modelling point patterns with linear structures

DEFF Research Database (Denmark)

Møller, Jesper; Rasmussen, Jakob Gulddahl

2009-01-01

processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

Modelling point patterns with linear structures

DEFF Research Database (Denmark)

Møller, Jesper; Rasmussen, Jakob Gulddahl

processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...... consider simulations of this model and compare with real data....

Modeling exposure–lag–response associations with distributed lag non-linear models

Science.gov (United States)

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

Biochemical methane potential prediction of plant biomasses: Comparing chemical composition versus near infrared methods and linear versus non-linear models.

Science.gov (United States)

Godin, Bruno; Mayer, Frédéric; Agneessens, Richard; Gerin, Patrick; Dardenne, Pierre; Delfosse, Philippe; Delcarte, Jérôme

2015-01-01

The reliability of different models to predict the biochemical methane potential (BMP) of various plant biomasses using a multispecies dataset was compared. The most reliable prediction models of the BMP were those based on the near infrared (NIR) spectrum compared to those based on the chemical composition. The NIR predictions of local (specific regression and non-linear) models were able to estimate quantitatively, rapidly, cheaply and easily the BMP. Such a model could be further used for biomethanation plant management and optimization. The predictions of non-linear models were more reliable compared to those of linear models. The presentation form (green-dried, silage-dried and silage-wet form) of biomasses to the NIR spectrometer did not influence the performances of the NIR prediction models. The accuracy of the BMP method should be improved to enhance further the BMP prediction models. Copyright © 2014 Elsevier Ltd. All rights reserved.

An R2 statistic for fixed effects in the linear mixed model.

Science.gov (United States)

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

Comparison of linear, skewed-linear, and proportional hazard models for the analysis of lambing interval in Ripollesa ewes.

Science.gov (United States)

Casellas, J; Bach, R

2012-06-01

Lambing interval is a relevant reproductive indicator for sheep populations under continuous mating systems, although there is a shortage of selection programs accounting for this trait in the sheep industry. Both the historical assumption of small genetic background and its unorthodox distribution pattern have limited its implementation as a breeding objective. In this manuscript, statistical performances of 3 alternative parametrizations [i.e., symmetric Gaussian mixed linear (GML) model, skew-Gaussian mixed linear (SGML) model, and piecewise Weibull proportional hazard (PWPH) model] have been compared to elucidate the preferred methodology to handle lambing interval data. More specifically, flock-by-flock analyses were performed on 31,986 lambing interval records (257.3 ± 0.2 d) from 6 purebred Ripollesa flocks. Model performances were compared in terms of deviance information criterion (DIC) and Bayes factor (BF). For all flocks, PWPH models were clearly preferred; they generated a reduction of 1,900 or more DIC units and provided BF estimates larger than 100 (i.e., PWPH models against linear models). These differences were reduced when comparing PWPH models with different number of change points for the baseline hazard function. In 4 flocks, only 2 change points were required to minimize the DIC, whereas 4 and 6 change points were needed for the 2 remaining flocks. These differences demonstrated a remarkable degree of heterogeneity across sheep flocks that must be properly accounted for in genetic evaluation models to avoid statistical biases and suboptimal genetic trends. Within this context, all 6 Ripollesa flocks revealed substantial genetic background for lambing interval with heritabilities ranging between 0.13 and 0.19. This study provides the first evidence of the suitability of PWPH models for lambing interval analysis, clearly discarding previous parametrizations focused on mixed linear models.

Estimation of group means when adjusting for covariates in generalized linear models.

Science.gov (United States)

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Identifiability Results for Several Classes of Linear Compartment Models.

Science.gov (United States)

Meshkat, Nicolette; Sullivant, Seth; Eisenberg, Marisa

2015-08-01

Identifiability concerns finding which unknown parameters of a model can be estimated, uniquely or otherwise, from given input-output data. If some subset of the parameters of a model cannot be determined given input-output data, then we say the model is unidentifiable. In this work, we study linear compartment models, which are a class of biological models commonly used in pharmacokinetics, physiology, and ecology. In past work, we used commutative algebra and graph theory to identify a class of linear compartment models that we call identifiable cycle models, which are unidentifiable but have the simplest possible identifiable functions (so-called monomial cycles). Here we show how to modify identifiable cycle models by adding inputs, adding outputs, or removing leaks, in such a way that we obtain an identifiable model. We also prove a constructive result on how to combine identifiable models, each corresponding to strongly connected graphs, into a larger identifiable model. We apply these theoretical results to several real-world biological models from physiology, cell biology, and ecology.

A non-linear model of economic production processes

Science.gov (United States)

Ponzi, A.; Yasutomi, A.; Kaneko, K.

2003-06-01

We present a new two phase model of economic production processes which is a non-linear dynamical version of von Neumann's neoclassical model of production, including a market price-setting phase as well as a production phase. The rate of an economic production process is observed, for the first time, to depend on the minimum of its input supplies. This creates highly non-linear supply and demand dynamics. By numerical simulation, production networks are shown to become unstable when the ratio of different products to total processes increases. This provides some insight into observed stability of competitive capitalist economies in comparison to monopolistic economies. Capitalist economies are also shown to have low unemployment.

Effect Displays in R for Generalised Linear Models

Directory of Open Access Journals (Sweden)

John Fox

2003-07-01

Full Text Available This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by marginality or hierarchy, such as polynomial terms, or main effects and interactions. I call these tables or graphs effect displays. Effect displays are constructed by identifying high-order terms in a generalised linear model. Fitted values under the model are computed for each such term. The lower-order "relatives" of a high-order term (e.g., main effects marginal to an interaction are absorbed into the term, allowing the predictors appearing in the high-order term to range over their values. The values of other predictors are fixed at typical values: for example, a covariate could be fixed at its mean or median, a factor at its proportional distribution in the data, or to equal proportions in its several levels. Variations of effect displays are also described, including representation of terms higher-order to any appearing in the model.

H∞ /H2 model reduction through dilated linear matrix inequalities

DEFF Research Database (Denmark)

Adegas, Fabiano Daher; Stoustrup, Jakob

2012-01-01

This paper presents sufficient dilated linear matrix inequalities (LMI) conditions to the $H_{infty}$ and $H_{2}$ model reduction problem. A special structure of the auxiliary (slack) variables allows the original model of order $n$ to be reduced to an order $r=n/s$ where $n,r,s in field{N}$. Arb......This paper presents sufficient dilated linear matrix inequalities (LMI) conditions to the $H_{infty}$ and $H_{2}$ model reduction problem. A special structure of the auxiliary (slack) variables allows the original model of order $n$ to be reduced to an order $r=n/s$ where $n,r,s in field...

Optimization Research of Generation Investment Based on Linear Programming Model

Science.gov (United States)

Wu, Juan; Ge, Xueqian

Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

Modeling of non-linear CHP efficiency curves in distributed energy systems

DEFF Research Database (Denmark)

Milan, Christian; Stadler, Michael; Cardoso, Gonçalo

2015-01-01

Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation...... for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation......, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two...

Non-linear characterisation of the physical model of an ancient masonry bridge

International Nuclear Information System (INIS)

Fragonara, L Zanotti; Ceravolo, R; Matta, E; Quattrone, A; De Stefano, A; Pecorelli, M

2012-01-01

This paper presents the non-linear investigations carried out on a scaled model of a two-span masonry arch bridge. The model has been built in order to study the effect of the central pile settlement due to riverbank erosion. Progressive damage was induced in several steps by applying increasing settlements at the central pier. For each settlement step, harmonic shaker tests were conducted under different excitation levels, this allowing for the non-linear identification of the progressively damaged system. The shaker tests have been performed at resonance with the modal frequency of the structure, which were determined from a previous linear identification. Estimated non-linearity parameters, which result from the systematic application of restoring force based identification algorithms, can corroborate models to be used in the reassessment of existing structures. The method used for non-linear identification allows monitoring the evolution of non-linear parameters or indicators which can be used in damage and safety assessment.

Study of the critical behavior of the O(N) linear and nonlinear sigma models

International Nuclear Information System (INIS)

Graziani, F.R.

1983-01-01

A study of the large N behavior of both the O(N) linear and nonlinear sigma models is presented. The purpose is to investigate the relationship between the disordered (ordered) phase of the linear and nonlinear sigma models. Utilizing operator product expansions and stability analyses, it is shown that for 2 - (lambda/sub R/(M) is the dimensionless renormalized quartic coupling and lambda* is the IR fixed point) limit of the linear sigma model which yields the nonlinear sigma model. It is also shown that stable large N linear sigma models with lambda 0) and nonlinear models are trivial. This result (i.e., triviality) is well known but only for one and two component models. Interestingly enough, the lambda< d = 4 linear sigma model remains nontrivial and tachyonic free

Linear versus non-linear supersymmetry, in general

Energy Technology Data Exchange (ETDEWEB)

Ferrara, Sergio [Theoretical Physics Department, CERN,CH-1211 Geneva 23 (Switzerland); INFN - Laboratori Nazionali di Frascati,Via Enrico Fermi 40, I-00044 Frascati (Italy); Department of Physics and Astronomy, UniversityC.L.A.,Los Angeles, CA 90095-1547 (United States); Kallosh, Renata [SITP and Department of Physics, Stanford University,Stanford, California 94305 (United States); Proeyen, Antoine Van [Institute for Theoretical Physics, Katholieke Universiteit Leuven,Celestijnenlaan 200D, B-3001 Leuven (Belgium); Wrase, Timm [Institute for Theoretical Physics, Technische Universität Wien,Wiedner Hauptstr. 8-10, A-1040 Vienna (Austria)

2016-04-12

We study superconformal and supergravity models with constrained superfields. The underlying version of such models with all unconstrained superfields and linearly realized supersymmetry is presented here, in addition to the physical multiplets there are Lagrange multiplier (LM) superfields. Once the equations of motion for the LM superfields are solved, some of the physical superfields become constrained. The linear supersymmetry of the original models becomes non-linearly realized, its exact form can be deduced from the original linear supersymmetry. Known examples of constrained superfields are shown to require the following LM’s: chiral superfields, linear superfields, general complex superfields, some of them are multiplets with a spin.

Linear versus non-linear supersymmetry, in general

International Nuclear Information System (INIS)

Ferrara, Sergio; Kallosh, Renata; Proeyen, Antoine Van; Wrase, Timm

2016-01-01

We study superconformal and supergravity models with constrained superfields. The underlying version of such models with all unconstrained superfields and linearly realized supersymmetry is presented here, in addition to the physical multiplets there are Lagrange multiplier (LM) superfields. Once the equations of motion for the LM superfields are solved, some of the physical superfields become constrained. The linear supersymmetry of the original models becomes non-linearly realized, its exact form can be deduced from the original linear supersymmetry. Known examples of constrained superfields are shown to require the following LM’s: chiral superfields, linear superfields, general complex superfields, some of them are multiplets with a spin.

Inverse Modelling Problems in Linear Algebra Undergraduate Courses

Science.gov (United States)

Martinez-Luaces, Victor E.

2013-01-01

This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

Science.gov (United States)

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were

Available pressure amplitude of linear compressor based on phasor triangle model

Science.gov (United States)

Duan, C. X.; Jiang, X.; Zhi, X. Q.; You, X. K.; Qiu, L. M.

2017-12-01

The linear compressor for cryocoolers possess the advantages of long-life operation, high efficiency, low vibration and compact structure. It is significant to study the match mechanisms between the compressor and the cold finger, which determines the working efficiency of the cryocooler. However, the output characteristics of linear compressor are complicated since it is affected by many interacting parameters. The existing matching methods are simplified and mainly focus on the compressor efficiency and output acoustic power, while neglecting the important output parameter of pressure amplitude. In this study, a phasor triangle model basing on analyzing the forces of the piston is proposed. It can be used to predict not only the output acoustic power, the efficiency, but also the pressure amplitude of the linear compressor. Calculated results agree well with the measurement results of the experiment. By this phasor triangle model, the theoretical maximum output pressure amplitude of the linear compressor can be calculated simply based on a known charging pressure and operating frequency. Compared with the mechanical and electrical model of the linear compressor, the new model can provide an intuitionistic understanding on the match mechanism with faster computational process. The model can also explain the experimental phenomenon of the proportional relationship between the output pressure amplitude and the piston displacement in experiments. By further model analysis, such phenomenon is confirmed as an expression of the unmatched design of the compressor. The phasor triangle model may provide an alternative method for the compressor design and matching with the cold finger.

The Overgeneralization of Linear Models among University Students' Mathematical Productions: A Long-Term Study

Science.gov (United States)

Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.

2010-01-01

Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…

Inspection logistics planning for multi-stage production systems with applications to semiconductor fabrication lines

Science.gov (United States)

Chen, Kyle Dakai

Since the market for semiconductor products has become more lucrative and competitive, research into improving yields for semiconductor fabrication lines has lately received a tremendous amount of attention. One of the most critical tasks in achieving such yield improvements is to plan the in-line inspection sampling efficiently so that any potential yield problems can be detected early and eliminated quickly. We formulate a multi-stage inspection planning model based on configurations in actual semiconductor fabrication lines, specifically taking into account both the capacity constraint and the congestion effects at the inspection station. We propose a new mixed First-Come-First-Serve (FCFS) and Last-Come-First-Serve (LCFS) discipline for serving the inspection samples to expedite the detection of potential yield problems. Employing this mixed FCFS and LCFS discipline, we derive approximate expressions for the queueing delays in yield problem detection time and develop near-optimal algorithms to obtain the inspection logistics planning policies. We also investigate the queueing performance with this mixed type of service discipline under different assumptions and configurations. In addition, we conduct numerical tests and generate managerial insights based on input data from actual semiconductor fabrication lines. To the best of our knowledge, this research is novel in developing, for the first time in the literature, near-optimal results for inspection logistics planning in multi-stage production systems with congestion effects explicitly considered.

A novel multi-stage direct contact membrane distillation module: Design, experimental and theoretical approaches.

Science.gov (United States)

Lee, Jung-Gil; Kim, Woo-Seung; Choi, June-Seok; Ghaffour, Noreddine; Kim, Young-Deuk

2016-12-15

An economic desalination system with a small scale and footprint for remote areas, which have a limited and inadequate water supply, insufficient water treatment and low infrastructure, is strongly demanded in the desalination markets. Here, a direct contact membrane distillation (DCMD) process has the simplest configuration and potentially the highest permeate flux among all of the possible MD processes. This process can also be easily instituted in a multi-stage manner for enhanced compactness, productivity, versatility and cost-effectiveness. In this study, an innovative, multi-stage, DCMD module under countercurrent-flow configuration is first designed and then investigate both theoretically and experimentally to identify its feasibility and operability for desalination application. Model predictions and measured data for mean permeate flux are compared and shown to be in good agreement. The effect of the number of module stages on the mean permeate flux, performance ratio and daily water production of the MDCMD system has been theoretically identified at inlet feed and permeate flow rates of 1.5 l/min and inlet feed and permeate temperatures of 70 °C and 25 °C, respectively. The daily water production of a three-stage DCMD module with a membrane area of 0.01 m 2  at each stage is found to be 21.5 kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Free-piston engine linear generator for hybrid vehicles modeling study

Science.gov (United States)

Callahan, T. J.; Ingram, S. K.

1995-05-01

Development of a free piston engine linear generator was investigated for use as an auxiliary power unit for a hybrid electric vehicle. The main focus of the program was to develop an efficient linear generator concept to convert the piston motion directly into electrical power. Computer modeling techniques were used to evaluate five different designs for linear generators. These designs included permanent magnet generators, reluctance generators, linear DC generators, and two and three-coil induction generators. The efficiency of the linear generator was highly dependent on the design concept. The two-coil induction generator was determined to be the best design, with an efficiency of approximately 90 percent.

Neutron stars in non-linear coupling models

International Nuclear Information System (INIS)

Taurines, Andre R.; Vasconcellos, Cesar A.Z.; Malheiro, Manuel; Chiapparini, Marcelo

2001-01-01

We present a class of relativistic models for nuclear matter and neutron stars which exhibits a parameterization, through mathematical constants, of the non-linear meson-baryon couplings. For appropriate choices of the parameters, it recovers current QHD models found in the literature: Walecka, ZM and ZM3 models. We have found that the ZM3 model predicts a very small maximum neutron star mass, ∼ 0.72M s un. A strong similarity between the results of ZM-like models and those with exponential couplings is noted. Finally, we discuss the very intense scalar condensates found in the interior of neutron stars which may lead to negative effective masses. (author)

Neutron stars in non-linear coupling models

Energy Technology Data Exchange (ETDEWEB)

Taurines, Andre R.; Vasconcellos, Cesar A.Z. [Rio Grande do Sul Univ., Porto Alegre, RS (Brazil); Malheiro, Manuel [Universidade Federal Fluminense, Niteroi, RJ (Brazil); Chiapparini, Marcelo [Universidade do Estado, Rio de Janeiro, RJ (Brazil)

2001-07-01

We present a class of relativistic models for nuclear matter and neutron stars which exhibits a parameterization, through mathematical constants, of the non-linear meson-baryon couplings. For appropriate choices of the parameters, it recovers current QHD models found in the literature: Walecka, ZM and ZM3 models. We have found that the ZM3 model predicts a very small maximum neutron star mass, {approx} 0.72M{sub s}un. A strong similarity between the results of ZM-like models and those with exponential couplings is noted. Finally, we discuss the very intense scalar condensates found in the interior of neutron stars which may lead to negative effective masses. (author)

Hydrodynamic and mechanical tests of a newly improved counter-current multi-stage centrifugal extractor

International Nuclear Information System (INIS)

Ionita, Gheorghe; Mirica, Dumitru; Croitoru, Cornelia; Stefanescu, Ioan; Retegan, Teodora

2003-01-01

Total actinide recovery, lanthanide/actinide separation and the selective partitioning of actinide from high level waste (HLW) are nowadays of major interest. Actinide partitioning with a view to safe disposing of HLW or utilization in many other applications of recovered elements involves an extraction process usually carried out by means of a mixer-settler, pulse column or centrifugal contactor. This last, presents some doubtless advantages and responds to the above mentioned goals. A new type of counter-current multistage centrifugal extractor has been designed and built. Similar apparatus was not found in the literature published to-date. The counter-current multi-stage centrifugal extractor is a stainless steel cylinder with an effective length of 346 mm, the effective diameter of 100 mm and a volume of 1.5 liters, working in horizontal position. The new internal structure and geometry of the new advanced centrifugal extractor consisting of nine cells (units), five rotation units, two mixing units, two propelling units and two final plates, ensures the counter-current running of the two phases.The central shaft having the rotation cells fixed on it is coupled by an intermediary connection to a electric motor of high rotation speed. Conceptual layout of the advanced counter-current multi-stage centrifugal extractor is presented. The newly designed extractor has been tested at 1000-3000 rot/min for a ratio of the aqueous/organic phase =1 to examine the mechanical behavior and the hydrodynamics of the two phases in countercurrent. The results showed that the performances have been generally good and the design requirements were fulfilled. The newly designed counter-current multistage centrifugal extractor appears to be a promising way to increase extraction rate of radionuclides and metals from liquid effluents. (authors)

A comparison of linear tyre models for analysing shimmy

NARCIS (Netherlands)

Besselink, I.J.M.; Maas, J.W.L.H.; Nijmeijer, H.

2011-01-01

A comparison is made between three linear, dynamic tyre models using low speed step responses and yaw oscillation tests. The match with the measurements improves with increasing complexity of the tyre model. Application of the different tyre models to a two degree of freedom trailing arm suspension

S-AMP for non-linear observation models

DEFF Research Database (Denmark)

Cakmak, Burak; Winther, Ole; Fleury, Bernard H.

2015-01-01

Recently we presented the S-AMP approach, an extension of approximate message passing (AMP), to be able to handle general invariant matrix ensembles. In this contribution we extend S-AMP to non-linear observation models. We obtain generalized AMP (GAMP) as the special case when the measurement...

Diagnostics for Linear Models With Functional Responses

OpenAIRE

Xu, Hongquan; Shen, Qing

2005-01-01

Linear models where the response is a function and the predictors are vectors are useful in analyzing data from designed experiments and other situations with functional observations. Residual analysis and diagnostics are considered for such models. Studentized residuals are defined and their properties are studied. Chi-square quantile-quantile plots are proposed to check the assumption of Gaussian error process and outliers. Jackknife residuals and an associated test are proposed to det...

New classical r-matrices from integrable non-linear sigma-models

International Nuclear Information System (INIS)

Laartz, J.; Bordemann, M.; Forger, M.; Schaper, U.

1993-01-01

Non-linear sigma models on Riemannian symmetric spaces constitute the most general class of classical non-linear sigma models which are known to be integrable. Using the current algebra structure of these models their canonical structure is analyzed and it is shown that their non-ultralocal fundamental Poisson bracket relation is governed by a field dependent non antisymmetric r-matrix obeying a dynamical Yang Baxter equation. The fundamental Poisson bracket relations and the r-matrix are derived explicitly and a new kind of algebra is found that is supposed to replace the classical Yang Baxter algebra governing the canonical structure of ultralocal models. (Author) 9 refs

Non-linear mixed-effects pharmacokinetic/pharmacodynamic modelling in NLME using differential equations

DEFF Research Database (Denmark)

Tornøe, Christoffer Wenzel; Agersø, Henrik; Madsen, Henrik

2004-01-01

The standard software for non-linear mixed-effect analysis of pharmacokinetic/phar-macodynamic (PK/PD) data is NONMEM while the non-linear mixed-effects package NLME is an alternative as tong as the models are fairly simple. We present the nlmeODE package which combines the ordinary differential...... equation (ODE) solver package odesolve and the non-Linear mixed effects package NLME thereby enabling the analysis of complicated systems of ODEs by non-linear mixed-effects modelling. The pharmacokinetics of the anti-asthmatic drug theophylline is used to illustrate the applicability of the nlme...

Matrix algebra for linear models

CERN Document Server

Gruber, Marvin H J

2013-01-01

Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. Matrix Algebra for Linear Models offers readers a unique, unified view of matrix analysis theory (where and when necessary), methods, and their applications. Written f

A gauge model describing N relativistic particles bound by linear forces

International Nuclear Information System (INIS)

Filippov, A.T.

1988-01-01

A relativistic model of N particles bound by linear forces is obtained by applying the gauging procedure to the linear canonical symmteries of a simple (rudimentary) nonrelativistic N-particle Lagrangian extended to relativistic phase space. The new (gauged) Lagrangian is formally Poincare invariant, the Hamiltonian is a linear combination of first-class constraints which are closed with respect to Pisson brackets and generate the localized canonical symmteries. The gauge potentials appear as the Lagrange multipliers of the constraints. Gauge fixing and quantization of the model are also briefly discussed. 11 refs

Mathematical modelling in engineering: A proposal to introduce linear algebra concepts

Directory of Open Access Journals (Sweden)

Andrea Dorila Cárcamo

2016-03-01

Full Text Available The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasize the development of mathematical abilities primarily associated with modelling and interpreting, which aren´t limited only to calculus abilities. Considering this, an instructional design was elaborated based on mathematic modelling and emerging heuristic models for the construction of specific linear algebra concepts:  span and spanning set. This was applied to first year engineering students. Results suggest that this type of instructional design contributes to the construction of these mathematical concepts and can also favour first year engineering students understanding of key linear algebra concepts and potentiate the development of higher order skills.

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

Science.gov (United States)

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Automated simultaneous assembly of multi-stage testing for the uniform CPA examination

NARCIS (Netherlands)

Breithaupt, Krista; Ariel, A.; Veldkamp, Bernard P.

2004-01-01

Some solutions used in the assembly of the computerized Uniform Certified Public Accountancy (CPA) licensing examination are offered as practical alternatives for operational programs producing large numbers of forms. The Uniform CPA examination will be offered as an adaptive multi-stage test (MST)

Research on EMI Reduction of Multi-stage Interleaved Bridgeless Power Factor Corrector

DEFF Research Database (Denmark)

Li, Qingnan; Thomsen, Ole Cornelius; Andersen, Michael A. E.

2012-01-01

Working as an electronic pollution eliminator, the Power Factor Corrector's (PFC) own Electromagnetic Interference (EMI) problems have been blocking its performance improvement for long. In this paper, a systematic research on EMI generation of a multi-stage Two-Boost-Circuit Interleaved Bridgeless...

Multi-stage Optimization of Matchings in Trees with Application to Kidney Exchange

KAUST Repository

Mankowski, Michal; Moshkov, Mikhail

2017-01-01

In this paper, we propose a method for multi-stage optimization of matchings in trees relative to different weight functions that assign positive weights to the edges of the trees. This method can be useful in transplantology where nodes of the tree

Analysis of baseline, average, and longitudinally measured blood pressure data using linear mixed models.

Science.gov (United States)

Hossain, Ahmed; Beyene, Joseph

2014-01-01

This article compares baseline, average, and longitudinal data analysis methods for identifying genetic variants in genome-wide association study using the Genetic Analysis Workshop 18 data. We apply methods that include (a) linear mixed models with baseline measures, (b) random intercept linear mixed models with mean measures outcome, and (c) random intercept linear mixed models with longitudinal measurements. In the linear mixed models, covariates are included as fixed effects, whereas relatedness among individuals is incorporated as the variance-covariance structure of the random effect for the individuals. The overall strategy of applying linear mixed models decorrelate the data is based on Aulchenko et al.'s GRAMMAR. By analyzing systolic and diastolic blood pressure, which are used separately as outcomes, we compare the 3 methods in identifying a known genetic variant that is associated with blood pressure from chromosome 3 and simulated phenotype data. We also analyze the real phenotype data to illustrate the methods. We conclude that the linear mixed model with longitudinal measurements of diastolic blood pressure is the most accurate at identifying the known single-nucleotide polymorphism among the methods, but linear mixed models with baseline measures perform best with systolic blood pressure as the outcome.

Simulations of multistage intense ion beam acceleration

International Nuclear Information System (INIS)

Slutz, S.A.; Poukey, J.W.

1992-01-01

An analytic theory for magnetically insulated, multistage acceleration of high intensity ion beams, where the diamagnetic effect due to electron flow is important, has been presented by Slutz and Desjarlais. The theory predicts the existence of two limiting voltages called V 1 (W) and V 2 (W), which are both functions of the injection energy qW of ions entering the accelerating gap. As the voltage approaches V 1 (W), unlimited beam-current density can penetrate the gap without the formation of a virtual anode because the dynamic gap goes to zero. Unlimited beam current density can penetrate an accelerating gap above V 2 (W), although a virtual anode is formed. It was found that the behavior of these limiting voltages is strongly dependent on the electron density profile. The authors have investigated the behavior of these limiting voltages numerically using the 2-D particle-in-cell (PIC) code MAGIC. Results of these simulations are consistent with the superinsulated analytic results. This is not surprising, since the ignored coordinate eliminates instabilities known to be important from studies of single stage magnetically insulated ion diodes. To investigate the effect of these instabilities the authors have simulated the problem with the 3-D PIC code QUICKSILVER, which indicates behavior that is consistent with the saturated model

Linear models for joint association and linkage QTL mapping

Directory of Open Access Journals (Sweden)

Fernando Rohan L

2009-09-01

Full Text Available Abstract Background Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.

Multistage charged particle accelerator, with high-vacuum insulation

International Nuclear Information System (INIS)

Holl, P.

1976-01-01

A multistage charged-particle accelerator for operating with accelerating voltages higher than 150 kV is described. The device consists essentially of a high-voltage insulator, a source for producing charged particles, a Wehnelt cylinder, an anode, and a post-accelerating tube containing stack-wise positioned post-accelerating electrodes. A high vacuum is used for insulating the parts carrying the high voltages, and at least one cylindrical screen surrounding these parts is interposed between them and the vacuum vessel, which can itself also function as a cylindrical screen

A non-linear dissipative model of magnetism

Czech Academy of Sciences Publication Activity Database

Durand, P.; Paidarová, Ivana

2010-01-01

Roč. 89, č. 6 (2010), s. 67004 ISSN 1286-4854 R&D Projects: GA AV ČR IAA100400501 Institutional research plan: CEZ:AV0Z40400503 Keywords : non-linear dissipative model of magnetism * thermodynamics * physical chemistry Subject RIV: CF - Physical ; Theoretical Chemistry http://epljournal.edpsciences.org/

Modelling and measurement of a moving magnet linear compressor performance

International Nuclear Information System (INIS)

Liang, Kun; Stone, Richard; Davies, Gareth; Dadd, Mike; Bailey, Paul

2014-01-01

A novel moving magnet linear compressor with clearance seals and flexure bearings has been designed and constructed. It is suitable for a refrigeration system with a compact heat exchanger, such as would be needed for CPU cooling. The performance of the compressor has been experimentally evaluated with nitrogen and a mathematical model has been developed to evaluate the performance of the linear compressor. The results from the compressor model and the measurements have been compared in terms of cylinder pressure, the ‘P–V’ loop, stroke, mass flow rate and shaft power. The cylinder pressure was not measured directly but was derived from the compressor dynamics and the motor magnetic force characteristics. The comparisons indicate that the compressor model is well validated and can be used to study the performance of this type of compressor, to help with design optimization and the identification of key parameters affecting the system transients. The electrical and thermodynamic losses were also investigated, particularly for the design point (stroke of 13 mm and pressure ratio of 3.0), since a full understanding of these can lead to an increase in compressor efficiency. - Highlights: • Model predictions of the performance of a novel moving magnet linear compressor. • Prototype linear compressor performance measurements using nitrogen. • Reconstruction of P–V loops using a model of the dynamics and electromagnetics. • Close agreement between the model and measurements for the P–V loops. • The design point motor efficiency was 74%, with potential improvements identified

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

Directory of Open Access Journals (Sweden)

R. Barbiero

2007-05-01

Full Text Available Model Output Statistics (MOS refers to a method of post-processing the direct outputs of numerical weather prediction (NWP models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511–L60 and LAMI-3. Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself.

A Linear Viscoelastic Model Calibration of Sylgard 184.

Energy Technology Data Exchange (ETDEWEB)

Long, Kevin Nicholas; Brown, Judith Alice

2017-04-01

We calibrate a linear thermoviscoelastic model for solid Sylgard 184 (90-10 formulation), a lightly cross-linked, highly flexible isotropic elastomer for use both in Sierra / Solid Mechanics via the Universal Polymer Model as well as in Sierra / Structural Dynamics (Salinas) for use as an isotropic viscoelastic material. Material inputs for the calibration in both codes are provided. The frequency domain master curve of oscillatory shear was obtained from a report from Los Alamos National Laboratory (LANL). However, because the form of that data is different from the constitutive models in Sierra, we also present the mapping of the LANL data onto Sandia’s constitutive models. Finally, blind predictions of cyclic tension and compression out to moderate strains of 40 and 20% respectively are compared with Sandia’s legacy cure schedule material. Although the strain rate of the data is unknown, the linear thermoviscoelastic model accurately predicts the experiments out to moderate strains for the slower strain rates, which is consistent with the expectation that quasistatic test procedures were likely followed. This good agreement comes despite the different cure schedules between the Sandia and LANL data.

A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands

DEFF Research Database (Denmark)

Fattahi, Mohammad; Govindan, Kannan; Keyvanshokooh, Esmaeil

2018-01-01

In this paper, we address a multi-period supply chain network redesign problem in which customer zones have price-dependent stochastic demand for multiple products. A novel multi-stage stochastic program is proposed to simultaneously make tactical decisions including products' prices and strategic...... redesign decisions. Existing uncertainty in potential demands of customer zones is modeled through a finite set of scenarios, described in the form of a scenario tree. The scenarios are generated using a Latin Hypercube Sampling method and then a forward scenario construction technique is employed...

Linearization of the interaction principle: Analytic Jacobians in the 'Radiant' model

International Nuclear Information System (INIS)

Spurr, R.J.D.; Christi, M.J.

2007-01-01

In this paper we present a new linearization of the Radiant radiative transfer model. Radiant uses discrete ordinates for solving the radiative transfer equation in a multiply-scattering anisotropic medium with solar and thermal sources, but employs the adding method (interaction principle) for the stacking of reflection and transmission matrices in a multilayer atmosphere. For the linearization, we show that the entire radiation field is analytically differentiable with respect to any surface or atmospheric parameter for which we require Jacobians (derivatives of the radiance field). Derivatives of the discrete ordinate solutions are based on existing methods developed for the LIDORT radiative transfer models. Linearization of the interaction principle is completely new and constitutes the major theme of the paper. We discuss the application of the Radiant model and its linearization in the Level 2 algorithm for the retrieval of columns of carbon dioxide as the main target of the Orbiting Carbon Observatory (OCO) mission

Study of gas production from shale reservoirs with multi-stage hydraulic fracturing horizontal well considering multiple transport mechanisms

Science.gov (United States)

Wei, Mingzhen; Liu, Hong

2018-01-01

Development of unconventional shale gas reservoirs (SGRs) has been boosted by the advancements in two key technologies: horizontal drilling and multi-stage hydraulic fracturing. A large number of multi-stage fractured horizontal wells (MsFHW) have been drilled to enhance reservoir production performance. Gas flow in SGRs is a multi-mechanism process, including: desorption, diffusion, and non-Darcy flow. The productivity of the SGRs with MsFHW is influenced by both reservoir conditions and hydraulic fracture properties. However, rare simulation work has been conducted for multi-stage hydraulic fractured SGRs. Most of them use well testing methods, which have too many unrealistic simplifications and assumptions. Also, no systematical work has been conducted considering all reasonable transport mechanisms. And there are very few works on sensitivity studies of uncertain parameters using real parameter ranges. Hence, a detailed and systematic study of reservoir simulation with MsFHW is still necessary. In this paper, a dual porosity model was constructed to estimate the effect of parameters on shale gas production with MsFHW. The simulation model was verified with the available field data from the Barnett Shale. The following mechanisms have been considered in this model: viscous flow, slip flow, Knudsen diffusion, and gas desorption. Langmuir isotherm was used to simulate the gas desorption process. Sensitivity analysis on SGRs’ production performance with MsFHW has been conducted. Parameters influencing shale gas production were classified into two categories: reservoir parameters including matrix permeability, matrix porosity; and hydraulic fracture parameters including hydraulic fracture spacing, and fracture half-length. Typical ranges of matrix parameters have been reviewed. Sensitivity analysis have been conducted to analyze the effect of the above factors on the production performance of SGRs. Through comparison, it can be found that hydraulic fracture

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Linear accelerator modeling: development and application

International Nuclear Information System (INIS)

Jameson, R.A.; Jule, W.D.

1977-01-01

Most of the parameters of a modern linear accelerator can be selected by simulating the desired machine characteristics in a computer code and observing how the parameters affect the beam dynamics. The code PARMILA is used at LAMPF for the low-energy portion of linacs. Collections of particles can be traced with a free choice of input distributions in six-dimensional phase space. Random errors are often included in order to study the tolerances which should be imposed during manufacture or in operation. An outline is given of the modifications made to the model, the results of experiments which indicate the validity of the model, and the use of the model to optimize the longitudinal tuning of the Alvarez linac

Design considerations for a pressure-driven multi-stage rocket

Science.gov (United States)

Sauerwein, Steven Craig

2002-01-01

The purpose of this study was to examine the feasibility of using propellant tank pressurization to eliminate the use of high-pressure turbopumps in multi-stage liquid-fueled satellite launchers. Several new technologies were examined to reduce the mass of such a rocket. Composite materials have a greater strength-to-weight ratio than metals and can be used to reduce the weight of rocket propellant tanks and structure. Catalytically combined hydrogen and oxygen can be used to heat pressurization gas, greatly reducing the amount of gas required. Ablatively cooled rocket engines can reduce the complexity and cost of the rocket. Methods were derived to estimate the mass of the various rocket components. These included a method to calculate the amount of gas needed to pressurize a propellant tank by modeling the behavior of the pressurization gas as the liquid propellant flows out of the tank. A way to estimate the mass and size of a ablatively cooled composite cased rocket engine. And a method to model the flight of such a rocket through the atmosphere in conjunction with optimization of the rockets trajectory. The results show that while a liquid propellant rocket using tank pressurization are larger than solid propellant rockets and turbopump driven liquid propellant rockets, they are not impractically large.

The effect of tooling deformation on process control in multistage metal forming

NARCIS (Netherlands)

Havinga, Gosse Tjipke; van den Boogaard, Antonius H.; Chinesta, F; Cueto, E; Abisset-Chavanne, E.

2016-01-01

Forming of high-strength steels leads to high loads within the production process. In multistage metal forming, the loads in different process stages are transferred to the other stages through elastic deformation of the stamping press. This leads to interactions between process steps, affecting the

Metabolite Identification Using Automated Comparison of High-Resolution Multistage Mass Spectral Trees

NARCIS (Netherlands)

Rojas-Cherto, M.; Peironcely, J.E.; Kasper, P.T.; Hooft, van der J.J.J.; Vos, de R.C.H.; Vreeken, R.; Hankemeier, T.; Reijmers, T.

2012-01-01

Multistage mass spectrometry (MSn) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a

Multi-Stage Selective Catalytic Reduction of NOx in Lean-Burn Engine Exhaust

National Research Council Canada - National Science Library

Penetrante, B

1997-01-01

.... A plasma can also be used to oxidize NO to NO2. This paper compares the multi-stage catalytic scheme with the plasma-assisted catalytic scheme for reduction of NOx in lean-burn engine exhausts. The advantages of plasma oxidation over catalytic oxidation are presented.

Fuel system for diesel engine with multi-stage heated

Science.gov (United States)

Ryzhov, Yu N.; Kuznetsov, Yu A.; Kolomeichenko, A. V.; Kuznetsov, I. S.; Solovyev, R. Yu; Sharifullin, S. N.

2017-09-01

The article describes a fuel system of a diesel engine with a construction tractor multistage heating, allowing the use of pure rapeseed oil as a diesel engine fuel. The paper identified the kinematic viscosity depending on the temperature and composition of the mixed fuel, supplemented by the existing recommendations on the use of mixed fuels based on vegetable oils and developed the device allowing use as fuel for diesel engines of biofuels based on vegetable oils.

A multi-stage heuristic algorithm for matching problem in the modified miniload automated storage and retrieval system of e-commerce

Science.gov (United States)

Wang, Wenrui; Wu, Yaohua; Wu, Yingying

2016-05-01

E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.

Benchmarking and performance enhancement framework for multi-staging object-oriented languages

Directory of Open Access Journals (Sweden)

Ahmed H. Yousef

2013-06-01

Full Text Available This paper focuses on verifying the readiness, feasibility, generality and usefulness of multi-staging programming in software applications. We present a benchmark designed to evaluate the performance gain of different multi-staging programming (MSP languages implementations of object oriented languages. The benchmarks in this suite cover different tests that range from classic simple examples (like matrix algebra to advanced examples (like encryption and image processing. The benchmark is applied to compare the performance gain of two different MSP implementations (Mint and Metaphor that are built on object oriented languages (Java and C# respectively. The results concerning the application of this benchmark on these languages are presented and analysed. The measurement technique used in benchmarking leads to the development of a language independent performance enhancement framework that allows the programmer to select which code segments need staging. The framework also enables the programmer to verify the effectiveness of staging on the application performance. The framework is applied to a real case study. The case study results showed the effectiveness of the framework to achieve significant performance enhancement.

Optimal difference-based estimation for partially linear models

KAUST Repository

Zhou, Yuejin; Cheng, Yebin; Dai, Wenlin; Tong, Tiejun

2017-01-01

Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

Optimal difference-based estimation for partially linear models

KAUST Repository

Zhou, Yuejin

2017-12-16

Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.

The minimal linear σ model for the Goldstone Higgs

International Nuclear Information System (INIS)

Feruglio, F.; Gavela, M.B.; Kanshin, K.; Machado, P.A.N.; Rigolin, S.; Saa, S.

2016-01-01

In the context of the minimal SO(5) linear σ-model, a complete renormalizable Lagrangian -including gauge bosons and fermions- is considered, with the symmetry softly broken to SO(4). The scalar sector describes both the electroweak Higgs doublet and the singlet σ. Varying the σ mass would allow to sweep from the regime of perturbative ultraviolet completion to the non-linear one assumed in models in which the Higgs particle is a low-energy remnant of some strong dynamics. We analyze the phenomenological implications and constraints from precision observables and LHC data. Furthermore, we derive the d≤6 effective Lagrangian in the limit of heavy exotic fermions.

Quality prediction modeling for multistage manufacturing based on classification and association rule mining

Directory of Open Access Journals (Sweden)

Kao Hung-An

2017-01-01

Full Text Available For manufacturing enterprises, product quality is a key factor to assess production capability and increase their core competence. To reduce external failure cost, many research and methodology have been introduced in order to improve process yield rate, such as TQC/TQM, Shewhart CycleDeming's 14 Points, etc. Nowadays, impressive progress has been made in process monitoring and industrial data analysis because of the Industry 4.0 trend. Industries start to utilize quality control (QC methodology to lower inspection overhead and internal failure cost. Currently, the focus of QC is mostly in the inspection of single workstation and final product, however, for multistage manufacturing, many factors (like equipment, operators, parameters, etc. can have cumulative and interactive effects to the final quality. When failure occurs, it is difficult to resume the original settings for cause analysis. To address these problems, this research proposes a combination of principal components analysis (PCA with classification and association rule mining algorithms to extract features representing relationship of multiple workstations, predict final product quality, and analyze the root-cause of product defect. The method is demonstrated on a semiconductor data set.

Multistage switching hardware and software implementations for student experiment purpose

Science.gov (United States)

Sani, A.; Suherman

2018-02-01

Current communication and internet networks are underpinned by the switching technologies that interconnect one network to the others. Students’ understanding on networks rely on how they conver the theories. However, understanding theories without touching the reality may exert spots in the overall knowledge. This paper reports the progress of the multistage switching design and implementation for student laboratory activities. The hardware and software designs are based on three stages clos switching architecture with modular 2x2 switches, controlled by an arduino microcontroller. The designed modules can also be extended for batcher and bayan switch, and working on circuit and packet switching systems. The circuit analysis and simulation show that the blocking probability for each switch combinations can be obtained by generating random or patterned traffics. The mathematic model and simulation analysis shows 16.4% blocking probability differences as the traffic generation is uniform. The circuits design components and interfacing solution have been identified to allow next step implementation.

Rapid profiling of polymeric phenolic acids in Salvia miltiorrhiza by hybrid data-dependent/targeted multistage mass spectrometry acquisition based on expected compounds prediction and fragment ion searching.

Science.gov (United States)

Shen, Yao; Feng, Zijin; Yang, Min; Zhou, Zhe; Han, Sumei; Hou, Jinjun; Li, Zhenwei; Wu, Wanying; Guo, De-An

2018-04-01

Phenolic acids are the major water-soluble components in Salvia miltiorrhiza (>5%). According to previous studies, many of them contribute to the cardiovascular effects and antioxidant effects of S. miltiorrhiza. Polymeric phenolic acids can be considered as the tanshinol derived metabolites, e.g., dimmers, trimers, and tetramers. A strategy combined with tanshinol-based expected compounds prediction, total ion chromatogram filtering, fragment ion searching, and parent list-based multistage mass spectrometry acquisition by linear trap quadropole-orbitrap Velos mass spectrometry was proposed to rapid profile polymeric phenolic acids in S. miltiorrhiza. More than 480 potential polymeric phenolic acids could be screened out by this strategy. Based on the fragment information obtained by parent list-activated data dependent multistage mass spectrometry acquisition, 190 polymeric phenolic acids were characterized by comparing their mass information with literature data, and 18 of them were firstly detected from S. miltiorrhiza. Seven potential compounds were tentatively characterized as new polymeric phenolic acids from S. miltiorrhiza. This strategy facilitates identification of polymeric phenolic acids in complex matrix with both selectivity and sensitivity, which could be expanded for rapid discovery and identification of compounds from complex matrix. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

Science.gov (United States)

Assaad, Bassel; Eltabach, Mario; Antoni, Jérôme

2014-01-01

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.

Robust estimation for partially linear models with large-dimensional covariates.

Science.gov (United States)

Zhu, LiPing; Li, RunZe; Cui, HengJian

2013-10-01

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.

Permian-Triassic maturation and multistage migration of hydrocarbons in the Assistência Formation (Irati Subgroup, Paraná Basin, Brazil: implications for the exploration model

Directory of Open Access Journals (Sweden)

António Mateus

Full Text Available New lines of geological evidence strongly suggest that the main period of hydrocarbon maturation within Assistência Formation should be Permian-Triassic, stimulated by a high geothermal gradient that also sustained various manifestations of hydrothermal activity. Three main stages of fluid/hydrocarbon migration can also be inferred on the basis of multiscale observations: confined flow in late Permian to Triassic times, depending on the local build-up of fluid pressures; heterogeneous flow in Lower Cretaceous, triggered by a rejuvenated temperature gradient assisted by the early developed permeability conditions; and a late flow possibly driven by local pressure gradients, after complete cooling of dolerite dykes/sills. The early maturation and multistage migration of hydrocarbons have significant consequences in the design of exploration models to be applied in Paraná Basin.

Optimization for decision making linear and quadratic models

CERN Document Server

Murty, Katta G

2010-01-01

While maintaining the rigorous linear programming instruction required, Murty's new book is unique in its focus on developing modeling skills to support valid decision-making for complex real world problems, and includes solutions to brand new algorithms.

A note on probabilistic models over strings: the linear algebra approach.

Science.gov (United States)

Bouchard-Côté, Alexandre

2013-12-01

Probabilistic models over strings have played a key role in developing methods that take into consideration indels as phylogenetically informative events. There is an extensive literature on using automata and transducers on phylogenies to do inference on these probabilistic models, in which an important theoretical question is the complexity of computing the normalization of a class of string-valued graphical models. This question has been investigated using tools from combinatorics, dynamic programming, and graph theory, and has practical applications in Bayesian phylogenetics. In this work, we revisit this theoretical question from a different point of view, based on linear algebra. The main contribution is a set of results based on this linear algebra view that facilitate the analysis and design of inference algorithms on string-valued graphical models. As an illustration, we use this method to give a new elementary proof of a known result on the complexity of inference on the "TKF91" model, a well-known probabilistic model over strings. Compared to previous work, our proving method is easier to extend to other models, since it relies on a novel weak condition, triangular transducers, which is easy to establish in practice. The linear algebra view provides a concise way of describing transducer algorithms and their compositions, opens the possibility of transferring fast linear algebra libraries (for example, based on GPUs), as well as low rank matrix approximation methods, to string-valued inference problems.

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

Science.gov (United States)

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

2016-03-01

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

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models.

Science.gov (United States)

Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert

2011-08-25

Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

Directory of Open Access Journals (Sweden)

Sorribas Albert

2011-08-01

Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

Science.gov (United States)

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

The general theory of multistage geminate reactions of isolated pairs of reactants. III. Two-stage reversible dissociation in geminate reaction A + A↔C↔B + B

Energy Technology Data Exchange (ETDEWEB)

Kipriyanov, Alexey A.; Kipriyanov, Alexander A.; Doktorov, Alexander B. [Voevodsky Institute of Chemical Kinetics and Combustion, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia and Novosibirsk State University, Novosibirsk 630090 (Russian Federation)

2016-04-14

Specific two-stage reversible reaction A + A↔C↔B + B of the decay of species C reactants by two independent transition channels is considered on the basis of the general theory of multistage reactions of isolated pairs of reactants. It is assumed that at the initial instant of time, the reacting system contains only reactants C. The employed general approach has made it possible to consider, in the general case, the inhomogeneous initial distribution of reactants, and avoid application of model concepts of a reaction system structure (i.e., of the structure of reactants and their molecular mobility). Slowing of multistage reaction kinetics as compared to the kinetics of elementary stages is established and physically interpreted. To test approximations (point approximation) used to develop a universal kinetic law, a widely employed specific model of spherical particles with isotropic reactivity diffusing in solution is applied. With this particular model as an example, ultimate kinetics of chemical conversion of reactants is investigated. The question concerning the depths of chemical transformation at which long-term asymptotes are reached is studied.

Non-linear sigma model on the fuzzy supersphere

International Nuclear Information System (INIS)

Kurkcuoglu, Seckin

2004-01-01

In this note we develop fuzzy versions of the supersymmetric non-linear sigma model on the supersphere S (2,2) . In hep-th/0212133 Bott projectors have been used to obtain the fuzzy C P 1 model. Our approach utilizes the use of supersymmetric extensions of these projectors. Here we obtain these (super)-projectors and quantize them in a fashion similar to the one given in hep-th/0212133. We discuss the interpretation of the resulting model as a finite dimensional matrix model. (author)

Linear and Nonlinear Career Models: Metaphors, Paradigms, and Ideologies.

Science.gov (United States)

Buzzanell, Patrice M.; Goldzwig, Steven R.

1991-01-01

Examines the linear or bureaucratic career models (dominant in career research, metaphors, paradigms, and ideologies) which maintain career myths of flexibility and individualized routes to success in organizations incapable of offering such versatility. Describes nonlinear career models which offer suggestive metaphors for re-visioning careers…

MAGDM linear-programming models with distinct uncertain preference structures.

Science.gov (United States)

Xu, Zeshui S; Chen, Jian

2008-10-01

Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

niques, an alternative `linear approximation model' (LAM) network approach is .... network is LPV, existing LTI theory is difficult to apply (Kailath 1980). ..... Beck J V, Arnold K J 1977 Parameter estimation in engineering and science (New York: ...

Nonlinearity measure and internal model control based linearization in anti-windup design

Energy Technology Data Exchange (ETDEWEB)

Perev, Kamen [Systems and Control Department, Technical University of Sofia, 8 Cl. Ohridski Blvd., 1756 Sofia (Bulgaria)

2013-12-18

This paper considers the problem of internal model control based linearization in anti-windup design. The nonlinearity measure concept is used for quantifying the control system degree of nonlinearity. The linearizing effect of a modified internal model control structure is presented by comparing the nonlinearity measures of the open-loop and closed-loop systems. It is shown that the linearization properties are improved by increasing the control system local feedback gain. However, it is emphasized that at the same time the stability of the system deteriorates. The conflicting goals of stability and linearization are resolved by solving the design problem in different frequency ranges.

Um modelo baseado em programação linear e programação de metas para análise de um sistema de produção e distribuição de suco concentrado congelado de laranja A model based on linear programming and goal programming to analyze a frozen concentrated orange juice production and distribution system

Directory of Open Access Journals (Sweden)

José Renato Munhoz

2001-08-01

Full Text Available Neste trabalho apresenta-se um modelo baseado em programação linear e programação de metas para apoiar decisões no processo de mistura e na distribuição de suco concentrado congelado de laranja. Explora-se a importância das decisões do processo de mistura para a análise da logística de distribuição do suco de laranja, além das decisões de transporte e armazenagem. O modelo utiliza conceitos conhecidos da literatura de problemas de mistura e planejamento da produção com múltiplos produtos, estágios e períodos, e foi resolvido por meio da linguagem de modelagem GAMS (General Algebraic Modeling System. Um estudo de caso foi realizado numa empresa de suco de laranja localizada no interior do estado de São Paulo, e os resultados preliminares obtidos são promissores.This work proposes a model based on linear programming and goal programming to support decisions in the blending process and distribution of frozen concentrated orange juice. This study explores the importance of blending decisions for the logistic analysis of the orange juice distribution, besides transportation and storage decisions. The model utilizes well-known concepts from the literature of blending problems and multistage, multiproduct and multiperiod production planning problems, and it was solved using the GAMS (General Algebraic Modeling System programming language. A case study was developed in an orange juice industry located in São Paulo State, and the preliminary results are promising.

Linear Model for Optimal Distributed Generation Size Predication

Directory of Open Access Journals (Sweden)

Ahmed Al Ameri

2017-01-01

Full Text Available This article presents a linear model predicting optimal size of Distributed Generation (DG that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.

Dynamic generalized linear models for monitoring endemic diseases

DEFF Research Database (Denmark)

Lopes Antunes, Ana Carolina; Jensen, Dan; Hisham Beshara Halasa, Tariq

2016-01-01

The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...... and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends...... in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance...

Ajuste de modelos estocásticos lineares e não-lineares para a descrição do perfil longitudinal de árvores Fitting linear and nonlinear stochastic models to describe longitudinal tree profile

Directory of Open Access Journals (Sweden)

Leonardo Machado Pires

2007-10-01

Full Text Available Os modelos polinomiais são mais difundidos no meio florestal brasileiro na descrição do perfil de árvores devido à sua facilidade de ajuste e precisão. O mesmo não ocorre com os modelos não-lineares, os quais possuem maior dificuldade de ajuste. Dentre os modelos não-lineares clássicos, na descrição do perfil, podem-se citar o de Gompertz, o Logístico e o de Weibull. Portanto, este estudo visou comparar os modelos lineares e não lineares para a descrição do perfil de árvores. As medidas de comparação foram o coeficiente de determinação (R², o erro-padrão residual (s yx, o coeficiente de determinação corrigido (R²ajustado, o gráfico dos resíduos e a facilidade de ajuste. Os resultados ressaltaram que, dentre os modelos não-lineares, o que obteve melhor desempenho, de forma geral, foi o modelo Logístico, apesar de o modelo de Gompertz ser melhor em termos de erro-padrão residual. Nos modelos lineares, o polinômio proposto por Pires & Calegario foi superior aos demais. Ao comparar os modelos não-lineares com os lineares, o modelo Logístico foi melhor em razão, principalmente, do fato de o comportamento dos dados ser não-linear, à baixa correlação entre os parâmetros e à fácil interpretação deles, facilitando a convergência e o ajuste.Polynomial models are most commonly used in Brazilian forestry for taper modeling due to its straightforwardly fitting and precision. The use of nonlinear regression classic models, like Gompertz, Logistic and Weibull, is not very common in Brazil. Therefore, this study aimed to verify the best nonlinear and linear models, and among these the best model to describe the longitudinal tree profile. The comparison measures were: R², syx, R²adjusted, residual graphics and fitting convergence. The results pointed out that among the non-linear models the best behavior, in general, was given by the Logistic model, although the Gompertz model was superior compared with the Weibull

A non-linear model of information seeking behaviour

Directory of Open Access Journals (Sweden)

Allen E. Foster

2005-01-01

Full Text Available The results of a qualitative, naturalistic, study of information seeking behaviour are reported in this paper. The study applied the methods recommended by Lincoln and Guba for maximising credibility, transferability, dependability, and confirmability in data collection and analysis. Sampling combined purposive and snowball methods, and led to a final sample of 45 inter-disciplinary researchers from the University of Sheffield. In-depth semi-structured interviews were used to elicit detailed examples of information seeking. Coding of interview transcripts took place in multiple iterations over time and used Atlas-ti software to support the process. The results of the study are represented in a non-linear Model of Information Seeking Behaviour. The model describes three core processes (Opening, Orientation, and Consolidation and three levels of contextual interaction (Internal Context, External Context, and Cognitive Approach, each composed of several individual activities and attributes. The interactivity and shifts described by the model show information seeking to be non-linear, dynamic, holistic, and flowing. The paper concludes by describing the whole model of behaviours as analogous to an artist's palette, in which activities remain available throughout information seeking. A summary of key implications of the model and directions for further research are included.

A quantitative analysis of instabilities in the linear chiral sigma model

International Nuclear Information System (INIS)

Nemes, M.C.; Nielsen, M.; Oliveira, M.M. de; Providencia, J. da

1990-08-01

We present a method to construct a complete set of stationary states corresponding to small amplitude motion which naturally includes the continuum solution. The energy wheighted sum rule (EWSR) is shown to provide for a quantitative criterium on the importance of instabilities which is known to occur in nonasymptotically free theories. Out results for the linear σ model showed be valid for a large class of models. A unified description of baryon and meson properties in terms of the linear σ model is also given. (author)

Valuing a gas-fired power plant: A comparison of ordinary linear models, regime-switching approaches, and models with stochastic volatility

International Nuclear Information System (INIS)

Heydari, Somayeh; Siddiqui, Afzal

2010-01-01

Energy prices are often highly volatile with unexpected spikes. Capturing these sudden spikes may lead to more informed decision-making in energy investments, such as valuing gas-fired power plants, than ignoring them. In this paper, non-linear regime-switching models and models with mean-reverting stochastic volatility are compared with ordinary linear models. The study is performed using UK electricity and natural gas daily spot prices and suggests that with the aim of valuing a gas-fired power plant with and without operational flexibility, non-linear models with stochastic volatility, specifically for logarithms of electricity prices, provide better out-of-sample forecasts than both linear models and regime-switching models.

Linear mixed models a practical guide using statistical software

CERN Document Server

West, Brady T; Galecki, Andrzej T

2014-01-01

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggest...

Multiscale Concrete Modeling of Aging Degradation

Energy Technology Data Exchange (ETDEWEB)

Hammi, Yousseff [Mississippi State Univ., Mississippi State, MS (United States); Gullett, Philipp [Mississippi State Univ., Mississippi State, MS (United States); Horstemeyer, Mark F. [Mississippi State Univ., Mississippi State, MS (United States)

2015-07-31

In this work a numerical finite element framework is implemented to enable the integration of coupled multiscale and multiphysics transport processes. A User Element subroutine (UEL) in Abaqus is used to simultaneously solve stress equilibrium, heat conduction, and multiple diffusion equations for 2D and 3D linear and quadratic elements. Transport processes in concrete structures and their degradation mechanisms are presented along with the discretization of the governing equations. The multiphysics modeling framework is theoretically extended to the linear elastic fracture mechanics (LEFM) by introducing the eXtended Finite Element Method (XFEM) and based on the XFEM user element implementation of Giner et al. [2009]. A damage model that takes into account the damage contribution from the different degradation mechanisms is theoretically developed. The total contribution of damage is forwarded to a Multi-Stage Fatigue (MSF) model to enable the assessment of the fatigue life and the deterioration of reinforced concrete structures in a nuclear power plant. Finally, two examples are presented to illustrate the developed multiphysics user element implementation and the XFEM implementation of Giner et al. [2009].

Application of the simplex method of linear programming model to ...

African Journals Online (AJOL)

This work discussed how the simplex method of linear programming could be used to maximize the profit of any business firm using Saclux Paint Company as a case study. It equally elucidated the effect variation in the optimal result obtained from linear programming model, will have on any given firm. It was demonstrated ...

Electron Model of Linear-Field FFAG

CERN Document Server

Koscielniak, Shane R

2005-01-01

A fixed-field alternating-gradient accelerator (FFAG) that employs only linear-field elements ushers in a new regime in accelerator design and dynamics. The linear-field machine has the ability to compact an unprecedented range in momenta within a small component aperture. With a tune variation which results from the natural chromaticity, the beam crosses many strong, uncorrec-table, betatron resonances during acceleration. Further, relativistic particles in this machine exhibit a quasi-parabolic time-of-flight that cannot be addressed with a fixed-frequency rf system. This leads to a new concept of bucketless acceleration within a rotation manifold. With a large energy jump per cell, there is possibly strong synchro-betatron coupling. A few-MeV electron model has been proposed to demonstrate the feasibility of these untested acceleration features and to investigate them at length under a wide range of operating conditions. This paper presents a lattice optimized for a 1.3 GHz rf, initial technology choices f...

Predicting musically induced emotions from physiological inputs: Linear and neural network models

Directory of Open Access Journals (Sweden)

Frank A. Russo

2013-08-01

Full Text Available Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of 'felt' emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants – heart rate, respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a nonlinear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The nonlinear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the nonlinear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Effect of linear and non-linear blade modelling techniques on simulated fatigue and extreme loads using Bladed

Science.gov (United States)

Beardsell, Alec; Collier, William; Han, Tao

2016-09-01

There is a trend in the wind industry towards ever larger and more flexible turbine blades. Blade tip deflections in modern blades now commonly exceed 10% of blade length. Historically, the dynamic response of wind turbine blades has been analysed using linear models of blade deflection which include the assumption of small deflections. For modern flexible blades, this assumption is becoming less valid. In order to continue to simulate dynamic turbine performance accurately, routine use of non-linear models of blade deflection may be required. This can be achieved by representing the blade as a connected series of individual flexible linear bodies - referred to in this paper as the multi-part approach. In this paper, Bladed is used to compare load predictions using single-part and multi-part blade models for several turbines. The study examines the impact on fatigue and extreme loads and blade deflection through reduced sets of load calculations based on IEC 61400-1 ed. 3. Damage equivalent load changes of up to 16% and extreme load changes of up to 29% are observed at some turbine load locations. It is found that there is no general pattern in the loading differences observed between single-part and multi-part blade models. Rather, changes in fatigue and extreme loads with a multi-part blade model depend on the characteristics of the individual turbine and blade. Key underlying causes of damage equivalent load change are identified as differences in edgewise- torsional coupling between the multi-part and single-part models, and increased edgewise rotor mode damping in the multi-part model. Similarly, a causal link is identified between torsional blade dynamics and changes in ultimate load results.

The low-energy constants of the extended linear sigma model

Energy Technology Data Exchange (ETDEWEB)

Divotgey, Florian; Giacosa, Francesco; Kovacs, Peter; Rischke, Dirk H. [Institut fuer Theoretische Physik, Goethe-Universitaet Frankfurt am Main (Germany)

2016-07-01

The low-energy dynamics of Quantum Chromodynamics (QCD) is fully determined by the interactions of the (pseudo-) Nambu-Goldstone bosons of spontaneous chiral symmetry breaking, i.e., for two quark flavors, the pions. Pion dynamics is described by the low-energy effective theory of QCD, chiral perturbation theory (ChPT), which is based on the nonlinear realization of chiral symmetry. An alternative description is provided by the Linear Sigma Model, where chiral symmetry is linearly realized. An extended version of this model, the so-called extended Linear Sigma Model (eLSM) was recently developed which incorporates all J{sup P}=0{sup ±}, 1{sup ±} anti qq mesons up to 2 GeV in mass. A fit of the coupling constants of this model to experimentally measured masses and decay widths has a surprisingly good quality. In this talk, it is demonstrated that the low-energy limit of the eLSM, obtained by integrating out all fields which are heavier than the pions, assumes the same form as ChPT. Moreover, the low-energy constants (LECs) of the eLSM agree with those of ChPT.

Non Linear Modelling and Control of Hydraulic Actuators

Directory of Open Access Journals (Sweden)

B. Šulc

2002-01-01

Full Text Available This paper deals with non-linear modelling and control of a differential hydraulic actuator. The nonlinear state space equations are derived from basic physical laws. They are more powerful than the transfer function in the case of linear models, and they allow the application of an object oriented approach in simulation programs. The effects of all friction forces (static, Coulomb and viscous have been modelled, and many phenomena that are usually neglected are taken into account, e.g., the static term of friction, the leakage between the two chambers and external space. Proportional Differential (PD and Fuzzy Logic Controllers (FLC have been applied in order to make a comparison by means of simulation. Simulation is performed using Matlab/Simulink, and some of the results are compared graphically. FLC is tuned in a such way that it produces a constant control signal close to its maximum (or minimum, where possible. In the case of PD control the occurrence of peaks cannot be avoided. These peaks produce a very high velocity that oversteps the allowed values.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

Science.gov (United States)

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.

Unification of three linear models for the transient visual system

NARCIS (Netherlands)

Brinker, den A.C.

1989-01-01

Three different linear filters are considered as a model describing the experimentally determined triphasic impulse responses of discs. These impulse responses arc associated with the transient visual system. Each model reveals a different feature of the system. Unification of the models is

An improved robust model predictive control for linear parameter-varying input-output models

NARCIS (Netherlands)

Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.

2018-01-01

This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal

A Detailed Analytical Study of Non-Linear Semiconductor Device Modelling

Directory of Open Access Journals (Sweden)

Umesh Kumar

1995-01-01

junction diode have been developed. The results of computer simulated examples have been presented in each case. The non-linear lumped model for Gunn is a unified model as it describes the diffusion effects as the-domain traves from cathode to anode. An additional feature of this model is that it describes the domain extinction and nucleation phenomena in Gunn dioder with the help of a simple timing circuit. The non-linear lumped model for SCR is general and is valid under any mode of operation in any circuit environment. The memristive circuit model for p-n junction diodes is capable of simulating realistically the diode’s dynamic behavior under reverse, forward and sinusiodal operating modes. The model uses memristor, the charge-controlled resistor to mimic various second-order effects due to conductivity modulation. It is found that both storage time and fall time of the diode can be accurately predicted.

Separation-induced boundary layer transition: Modeling with a non-linear eddy-viscosity model coupled with the laminar kinetic energy equation

International Nuclear Information System (INIS)

Vlahostergios, Z.; Yakinthos, K.; Goulas, A.

2009-01-01

We present an effort to model the separation-induced transition on a flat plate with a semi-circular leading edge, using a cubic non-linear eddy-viscosity model combined with the laminar kinetic energy. A non-linear model, compared to a linear one, has the advantage to resolve the anisotropic behavior of the Reynolds-stresses in the near-wall region and it provides a more accurate expression for the generation of turbulence in the transport equation of the turbulence kinetic energy. Although in its original formulation the model is not able to accurately predict the separation-induced transition, the inclusion of the laminar kinetic energy increases its accuracy. The adoption of the laminar kinetic energy by the non-linear model is presented in detail, together with some additional modifications required for the adaption of the laminar kinetic energy into the basic concepts of the non-linear eddy-viscosity model. The computational results using the proposed combined model are shown together with the ones obtained using an isotropic linear eddy-viscosity model, which adopts also the laminar kinetic energy concept and in comparison with the existing experimental data.

Modelling non-linear effects of dark energy

Science.gov (United States)

Bose, Benjamin; Baldi, Marco; Pourtsidou, Alkistis

2018-04-01

We investigate the capabilities of perturbation theory in capturing non-linear effects of dark energy. We test constant and evolving w models, as well as models involving momentum exchange between dark energy and dark matter. Specifically, we compare perturbative predictions at 1-loop level against N-body results for four non-standard equations of state as well as varying degrees of momentum exchange between dark energy and dark matter. The interaction is modelled phenomenologically using a time dependent drag term in the Euler equation. We make comparisons at the level of the matter power spectrum and the redshift space monopole and quadrupole. The multipoles are modelled using the Taruya, Nishimichi and Saito (TNS) redshift space spectrum. We find perturbation theory does very well in capturing non-linear effects coming from dark sector interaction. We isolate and quantify the 1-loop contribution coming from the interaction and from the non-standard equation of state. We find the interaction parameter ξ amplifies scale dependent signatures in the range of scales considered. Non-standard equations of state also give scale dependent signatures within this same regime. In redshift space the match with N-body is improved at smaller scales by the addition of the TNS free parameter σv. To quantify the importance of modelling the interaction, we create mock data sets for varying values of ξ using perturbation theory. This data is given errors typical of Stage IV surveys. We then perform a likelihood analysis using the first two multipoles on these sets and a ξ=0 modelling, ignoring the interaction. We find the fiducial growth parameter f is generally recovered even for very large values of ξ both at z=0.5 and z=1. The ξ=0 modelling is most biased in its estimation of f for the phantom w=‑1.1 case.

Generalized linear mixed models modern concepts, methods and applications

CERN Document Server

Stroup, Walter W

2012-01-01

PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data

Wavefront Sensing for WFIRST with a Linear Optical Model

Science.gov (United States)

Jurling, Alden S.; Content, David A.

2012-01-01

In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.

Multistage electrodeposition of supported platinum-based nanostructured systems for electrocatalytic applications

CSIR Research Space (South Africa)

Mkwizu, TS

2011-05-01

Full Text Available .R. Modibedi and Mkhulu K. Mathe* *kmathe@csir.co.za 219th ECS Meeting, 1 ? 6 May, 2011, Montreal, Canada Multistage Electrodeposition of Supported Platinum-based Nanostructured Systems for Electrocatalytic Applications Overview ? Acknowledgements... of constituent elements of the given electrode surface. ? Applications areas: Fuel cells, electrochemical sensors, electrolyzers Introduction e- A B 5 Introduction Atomic-level processes during electrocatalysis www...

Heterotic non-linear sigma models with anti-de Sitter target spaces

International Nuclear Information System (INIS)

Michalogiorgakis, Georgios; Gubser, Steven S.

2006-01-01

We calculate the beta function of non-linear sigma models with S D+1 and AdS D+1 target spaces in a 1/D expansion up to order 1/D 2 and to all orders in α ' . This beta function encodes partial information about the spacetime effective action for the heterotic string to all orders in α ' . We argue that a zero of the beta function, corresponding to a worldsheet CFT with AdS D+1 target space, arises from competition between the one-loop and higher-loop terms, similarly to the bosonic and supersymmetric cases studied previously in [J.J. Friess, S.S. Gubser, Non-linear sigma models with anti-de Sitter target spaces, Nucl. Phys. B 750 (2006) 111-141]. Various critical exponents of the non-linear sigma model are calculated, and checks of the calculation are presented

Improvement of a multi-stage model for the modeling of a functionalized nursing bed as support for the sensor-assisted function-alization of furniture in the hospital and care sector

Directory of Open Access Journals (Sweden)

Kitzig Andreas

2017-09-01

Full Text Available Development of preparation-free functionalized furniture based patient monitoring systems for use in the area of home- or stationary- care is often empirically driven. In particular, functionalization of furniture by means of different sensors is strongly affected by this development methodology. As a result, the systems are often not extensive-ly extendable or cannot be optimized because basic mechanisms are not comprehensible. In order to support development or optimization, a modelling approach is often useful. Thus, using a more comprehensive approach the required sensitivity of the sensors as well as their position in the system can be derived from a simulation model. In order to solve this problem, a multi-stage model was introduced at the BMT conference in 2014 by the authors, which allows the designer to model the entire system. The model has been extended and improved in the meantime and the achieved progress is presented in this work. The presented modelling approach can be divided into three main components. These are the person under supervision, the furniture (in our case a nursing bed and the sensors (force measuring cells which are modelled separately. In this work the main focus will be on improving the modelling of the human movement process and its implementation. Furthermore, the modelling of the sensor behavior in the nursing bed is described in detail with regard to their oscillation behavior and the influence on the model.

Non Linear signa models probing the string structure

International Nuclear Information System (INIS)

Abdalla, E.

1987-01-01

The introduction of a term depending on the extrinsic curvature to the string action, and related non linear sigma models defined on a symmetric space SO(D)/SO(2) x SO(d-2) is descussed . Coupling to fermions are also treated. (author) [pt

Weighted functional linear regression models for gene-based association analysis.

Science.gov (United States)

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

Optical linear algebra processors - Noise and error-source modeling

Science.gov (United States)

Casasent, D.; Ghosh, A.

1985-01-01

The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.