WorldWideScience

Sample records for machinery selection model

  1. Optimization Model for Machinery Selection of Multi-Crop Farms in Elsuki Agricultural Scheme

    Directory of Open Access Journals (Sweden)

    Mysara Ahmed Mohamed

    2017-07-01

    Full Text Available The optimization machinery model was developed to aid decision-makers and farm machinery managers in determining the optimal number of tractors, scheduling the agricultural operation and minimizing machinery total costs. For purpose of model verification, validation and application input data was collected from primary & secondary sources from Elsuki agricultural scheme for two seasons namely 2011-2012 and 2013-2014. Model verification was made by comparing the numbers of tractors of Elsuki agricultural scheme for season 2011-2012 with those estimated by the model. The model succeeded in reducing the number of tractors and operation total cost by 23%. The effect of optimization model on elements of direct cost saving indicated that the highest cost saving is reached with depreciation, repair and maintenance (23% and the minimum cost saving is attained with fuel cost (22%. Sensitivity analysis in terms of change in model input for each of cultivated area and total costs of operations showing that: Increasing the operation total cost by 10% decreased the total number of tractors after optimization by 23% and total cost of operations was also decreased by 23%. Increasing the cultivated area by 10%, decreased the total number of tractors after optimization by(12% and total cost of operations was also decreased by 12% (16669206 SDG(1111280 $ to 14636376 SDG(975758 $. For the case of multiple input effect of the area and operation total cost resulted in decrease maximum number of tractors by 12%, and the total cost of operations also decreased by 12%. It is recommended to apply the optimization model as pre-requisite for improving machinery management during implementation of machinery scheduling.

  2. Availability analysis of selected mining machinery

    Directory of Open Access Journals (Sweden)

    Brodny Jarosław

    2017-06-01

    Full Text Available Underground extraction of coal is characterized by high variability of mining and geological conditions in which it is conducted. Despite ever more effective methods and tools, used to identify the factors influencing this process, mining machinery, used in mining underground, work in difficult and not always foreseeable conditions, which means that these machines should be very universal and reliable. Additionally, a big competition, occurring on the coal market, causes that it is necessary to take action in order to reduce the cost of its production, e.g. by increasing the efficiency of utilization machines. To meet this objective it should be pro-ceed with analysis presented in this paper. The analysis concerns to availability of utilization selected mining machinery, conducted using the model of OEE, which is a tool for quantitative estimate strategy TPM. In this article we considered the machines being part of the mechanized longwall complex and the basis of analysis was the data recording by the industrial automation system. Using this data set we evaluated the availability of studied machines and the structure of registered breaks in their work. The results should be an important source of information for maintenance staff and management of mining plants, needed to improve the economic efficiency of underground mining.

  3. Fluid machinery application, selection, and design

    CERN Document Server

    Wright, Terry

    2013-01-01

    Published nearly a decade ago, Fluid Machinery: Performance, Analysis, and Design quickly became popular with students, professors, and professionals because of its comprehensive and comprehensible introduction to the fluid mechanics of turbomachinery. Renamed to reflect its wider scope and reorganized content, this second edition provides a more logical flow of information that will enhance understanding. In particular, it presents a consistent notation within and across chapters, updating material when appropriate. Although the authors do account for the astounding growth in the field of com

  4. Implications of material selection on the design of packaging machinery.

    Science.gov (United States)

    Merritt, J P

    2009-01-01

    Material selection has significant implications on the design and cost of horizontal-form-fill-seal packaging machinery. To avoid excessive costs, machine redesigns and project delays, material selection must be reconciled early in the project and revisited throughout the construction of the machine.

  5. Glucose transport machinery reconstituted in cell models.

    Science.gov (United States)

    Hansen, Jesper S; Elbing, Karin; Thompson, James R; Malmstadt, Noah; Lindkvist-Petersson, Karin

    2015-02-11

    Here we demonstrate the production of a functioning cell model by formation of giant vesicles reconstituted with the GLUT1 glucose transporter and a glucose oxidase and hydrogen peroxidase linked fluorescent reporter internally. Hence, a simplified artificial cell is formed that is able to take up glucose and process it.

  6. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  7. Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling

    Directory of Open Access Journals (Sweden)

    Daniel Zurita-Millán

    2016-01-01

    Full Text Available Vibration monitoring plays a key role in the industrial machinery reliability since it allows enhancing the performance of the machinery under supervision through the detection of failure modes. Thus, vibration monitoring schemes that give information regarding future condition, that is, prognosis approaches, are of growing interest for the scientific and industrial communities. This work proposes a vibration signal prognosis methodology, applied to a rotating electromechanical system and its associated kinematic chain. The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios. The model tuning is performed by means of Genetic Algorithms along with a correlation based interval selection procedure. The performance and effectiveness of the proposed method are validated experimentally with an electromechanical test bench containing a kinematic chain. The results of the study indicate the suitability of the method for vibration forecasting in complex electromechanical systems and their associated kinematic chains.

  8. Updating the model TREMOD - Mobile Machinery (TREMOD-MM); Aktualisierung des Modells TREMOD - Mobile Machinery (TREMOD-MM)

    Energy Technology Data Exchange (ETDEWEB)

    Helms, Hinrich; Lambrecht, Udo; Knoerr, Wolfram [ifeu - Institut fuer Energie- und Umweltforschung Heidelberg gGmbH, Heidelberg (Germany)

    2010-05-15

    In the context of the project ''Development of a model for the computation of the air pollutant emissions and the fuel consumption of combustion engines in mobile devices and machines'', the Institute for Energy and Environmental Research GmbH (Heidelberg, Federal Republic of Germany) has created the model TREMOD-MM (TREMOD Mobile Machinery). Thus a detailed computation of the emissions from mobile devices and machines in the agriculture, construction industry, forestry and gardening as well as the sport shipping and passenger shipping can be accomplished. Strongly differentiated data are considered to the age structure, engine performance, use and emission behaviour. Thus it is possible to compute the emissions for different scenarios in high degree of detail.

  9. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  10. Genome-scale modeling of the protein secretory machinery in yeast

    DEFF Research Database (Denmark)

    Feizi, Amir; Österlund, Tobias; Petranovic, Dina

    2013-01-01

    The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking....... Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm...

  11. A Modelling Approach to Multibody Dynamics of Fluid Power Machinery with Hydrodynamic Lubrication

    DEFF Research Database (Denmark)

    Johansen, Per; Rømer, Daniel; Andersen, Torben Ole

    2013-01-01

    The efficiency potential of the digital displacement technology and the increasing interest in hydraulic transmissions in wind and wave energy applications has created an incentive for development of high efficiency fluid power machinery. Modelling and analysis of fluid power machinery loss...... mechanisms is necessary in order to accommodate this demand. At present fully coupled thermo-elastic models for various tribological interfaces has been presented. However, in order to analyse the interaction between tribological interfaces in fluid power pumps and motors, these interface models needs...

  12. A Fault Diagnosis Model Based on LCD-SVD-ANN-MIV and VPMCD for Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2016-01-01

    Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.

  13. Prediction model for sound transmission from machinery in buildings: feasible approaches and problems to be solved

    NARCIS (Netherlands)

    Gerretsen, E.

    2000-01-01

    Prediction models for the airborne and impact sound transmission in buildings have recently been established (EN 12354- 1&2:1999). However, these models do not cover technical installations and machinery as a source of sound in buildings. Yet these can cause unacceptable sound levels and it is

  14. Research on prediction of agricultural machinery total power based on grey model optimized by genetic algorithm

    Science.gov (United States)

    Xie, Yan; Li, Mu; Zhou, Jin; Zheng, Chang-zheng

    2009-07-01

    Agricultural machinery total power is an important index to reflex and evaluate the level of agricultural mechanization. It is the power source of agricultural production, and is the main factors to enhance the comprehensive agricultural production capacity expand production scale and increase the income of the farmers. Its demand is affected by natural, economic, technological and social and other "grey" factors. Therefore, grey system theory can be used to analyze the development of agricultural machinery total power. A method based on genetic algorithm optimizing grey modeling process is introduced in this paper. This method makes full use of the advantages of the grey prediction model and characteristics of genetic algorithm to find global optimization. So the prediction model is more accurate. According to data from a province, the GM (1, 1) model for predicting agricultural machinery total power was given based on the grey system theories and genetic algorithm. The result indicates that the model can be used as agricultural machinery total power an effective tool for prediction.

  15. Design and modelling of innovative machinery systems for large ships

    DEFF Research Database (Denmark)

    Larsen, Ulrik

    Eighty percent of the growing global merchandise trade is transported by sea. The shipping industry is required to reduce the pollution and increase the energy efficiency of ships in the near future. There is a relatively large potential for approaching these requirements by implementing waste heat...... consisting of a two-zone combustion and NOx emission model, a double Wiebe heat release model, the Redlich-Kwong equation of state and the Woschni heat loss correlation. A novel methodology is presented and used to determine the optimum organic Rankine cycle process layout, working fluid and process......, are evaluated with regards to the fuel consumption and NOx emissions trade-off. The results of the calibration and validation of the engine model suggest that the main performance parameters can be predicted with adequate accuracies for the overall purpose. The results of the ORC and the Kalina cycle...

  16. OPTIMIZATION MODEL FOR VEHICLE ROUTING AND EQUIPMENT REPLACEMENT IN FARM MACHINERY

    OpenAIRE

    Grano, Carolina; Abensur, Eder

    2017-01-01

    ABSTRACT: An equipment replacement decision takes into account economic engineering models based on discounted cash flow (DCF) such as the Annual Equivalent Cost (AEC). Despite a large number of researches on industrial assets replacement, there is a lack of studies applied to farm goods. This study aimed at assessing an alternative model for economic decision analysis on farm machinery replacement, with no restrictions on the number of replacements and assessed goods during a defined timelin...

  17. Manufacturing of thin-walled parts for machinery by selective laser melting

    Directory of Open Access Journals (Sweden)

    Bobyr Vitaliy

    2017-01-01

    Full Text Available The paper describes the technology of selective laser melting, as well as its capabilities in the manufacture of thin-wall honeycomb energy absorber (HEA. The effect of the technological parameters of the building process on the HEA walls’ thickness is studied. Conformity analysis of the mass-dimensional characteristics of the finished composition with the predefined parameters of the 3D-CAD model is carried out. Dependencies of building parameterson the quality of the manufactured HEA are established, general recommendations for the practical use of technology in the creation of HEAare given.

  18. A comparison study of support vector machines and hidden Markov models in machinery condition monitoring

    International Nuclear Information System (INIS)

    Miao, Qiang; Huang, Hong Zhong; Fan, Xianfeng

    2007-01-01

    Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area

  19. Propulsive machinery selection for repowering of an old patrol craft - A case study

    Science.gov (United States)

    Rahman, M. Muzibur; Mridha, A. H. Yusuf; Ahsan, Kazi Sakib

    2017-12-01

    This paper presents a case study of repowering peculiarities in relation to an old vessel. The vessel selected for study was designed for cruising speed of 15 knots. Over the years of operation the vessel's cruising speed reduced to about 8 knots. So, the owner wanted to repower it to have a fresh tenure of life and the work was given to a shipyard. But after replacement of old two engines by new engines of same power with different model, the performance of the vessel was not satisfactory. In the present paper, the problem is studied with comprehensive calculations of hydrostatic particulars and resistance of the ship. The analysis is carried out in respect of engine specifications, gear ratios, propeller design etc. and found that the operating ranges of new engines are not at par with the old engines. The new engine does not also match with old propeller. At this situation, comparative studies have determined that among all possible solutions redesign of propeller is the most suitable one and cost effective.

  20. Part-time farmers and accidents with agricultural machinery: a moderated mediated model on the role played by frequency of use and unsafe beliefs.

    Science.gov (United States)

    Caffaro, Federica; Roccato, Michele; Micheletti Cremasco, Margherita; Cavallo, Eugenio

    2018-01-25

    We aimed at testing a model of the direct and indirect effects of being a part-time farmer on the probability of being involved in an agricultural machinery-related accident, considering the role played by unsafe beliefs and the frequency of use of machinery. Two-hundred and fifty-two Italian men, regular users of agricultural machinery (age: Mean = 45.1 years, standard Deviation = 17.5), were administered a paper-and-pencil questionnaire addressing their relation with work, unsafe beliefs, and previous experience of machinery-related accidents. Being a part-time farmer showed a positive association with unsafe beliefs only among occasional machinery users. Unsafe beliefs in turn showed a positive association with accidents. The study gave a novel contribution to the knowledge of the chain of events connecting part-time farmers with machinery-related accidents. Preventive training interventions targeting part-timer farmers using agricultural machinery just occasionally should be developed.

  1. The optimization model of the logging machinery usage in forestry practice

    Directory of Open Access Journals (Sweden)

    Jitka Janová

    2009-01-01

    Full Text Available The decision support systems commonly used in industry and economy managerial practice for optimizing the processes are based on algoritmization of the typical decision problems. In Czech forestry business, there is a lack of developed decision support systems, which could be easily used in daily practice. This stems from the fact, that the application of optimization methods is less successful in forestry decision making than in industry or economy due to inherent complexity of the forestry decision problems. There is worldwide ongoing research on optimization models applicable in forestry decision making, but the results are not globally applicable and moreover the cost of possibly arising software tools are indispensable. Especially small and medium forestry companies in Czech Republic can not afford such additional costs, although the results of optimization could positively in­fluen­ce not only the business itself but also the impact of forestry business on the environment. Hence there is a need for user friendly optimization models for forestry decision making in the area of Czech Republic, which could be easily solved in commonly available software, and whose results would be both, realistic and easily applicable in the daily decision making.The aim of this paper is to develop the optimization model for the machinery use planning in Czech logging firm in such a way, that the results can be obtained using MS EXCEL. The goal is to identify the integer number of particular machines which should be outsourced for the next period, when the total cost minimization is required. The linear programming model is designed covering the typical restrictions on available machinery and total volume of trees to be cut and transported. The model offers additional result in the form of optimal employment of particular machines. The solution procedure is described in detail and the results obtained are discussed with respect to its applicability in

  2. Wind power research at Oregon State University. [for selecting windpowered machinery sites

    Science.gov (United States)

    Hewson, E. W.

    1973-01-01

    There have been two primary thrusts of the research effort to date, along with several supplementary ones. One primary area has been an investigation of the wind fields along coastal areas of the Pacific Northwest, not only at the shoreline but also for a number of miles inland and offshore as well. Estimates have been made of the influence of the wind turbulence as measured at coastal sites in modifying the predicted dependence of power generated on the cube of the wind speed. Wind flow patterns in the Columbia River valley have also been studied. The second primary thrust has been to substantially modify and improve an existing wind tunnel to permit the build up of a boundary layer in which various model studies will be conducted. One of the secondary studies involved estimating the cost of building an aerogenerator.

  3. Design and modeling of an advanced marine machinery system including waste heat recovery and removal of sulphur oxides

    DEFF Research Database (Denmark)

    Frimann Nielsen, Rasmus; Haglind, Fredrik; Larsen, Ulrik

    2014-01-01

    the efficiency of machinery systems. The wet sulphuric acid process is an effective way of removing flue gas sulphur oxides from land-based coal-fired power plants. Moreover, organic Rankine cycles (ORC) are suitable for heat to power conversion for low temperature heat sources. This paper describes the design...... that an ORC placed after the conventional waste heat recovery system is able to extract the sulphuric acid from the exhaust gas, while at the same time increase the combined cycle thermal efficiency by 2.6%. The findings indicate that the technology has potential in marine applications regarding both energy...... and modeling of a highly efficient machinery system which includes the removal of exhaust gas sulphur oxides. The system consists of a two-stroke diesel engine, the wet sulphuric process for sulphur removal, a conventional steam Rankine cycle and an ORC. Results of numerical modeling efforts suggest...

  4. Compression-absorption (resorption) refrigerating machinery. Modeling of reactors; Machine frigorifique a compression-absorption (resorption). Modelisation des reacteurs

    Energy Technology Data Exchange (ETDEWEB)

    Lottin, O; Feidt, M; Benelmir, R [LEMTA-UHP Nancy-1, 54 - Vandoeuvre-les-Nancy (France)

    1998-12-31

    This paper is a series of transparencies presenting a comparative study of the thermal performances of different types of refrigerating machineries: di-thermal with vapor compression, tri-thermal with moto-compressor, with ejector, with free piston, adsorption-type, resorption-type, absorption-type, compression-absorption-type. A prototype of ammonia-water compression-absorption heat pump is presented and modeled. (J.S.)

  5. Compression-absorption (resorption) refrigerating machinery. Modeling of reactors; Machine frigorifique a compression-absorption (resorption). Modelisation des reacteurs

    Energy Technology Data Exchange (ETDEWEB)

    Lottin, O.; Feidt, M.; Benelmir, R. [LEMTA-UHP Nancy-1, 54 - Vandoeuvre-les-Nancy (France)

    1997-12-31

    This paper is a series of transparencies presenting a comparative study of the thermal performances of different types of refrigerating machineries: di-thermal with vapor compression, tri-thermal with moto-compressor, with ejector, with free piston, adsorption-type, resorption-type, absorption-type, compression-absorption-type. A prototype of ammonia-water compression-absorption heat pump is presented and modeled. (J.S.)

  6. MODELING ENVIRONMENTAL IMPACT OF MACHINERY SECTORS TO PROMOTE SUSTAINABLE DEVELOPMENT OF THAILAND

    Directory of Open Access Journals (Sweden)

    Pruethsan Sutthichaimethee

    2016-01-01

    Full Text Available The objective of this research is to propose an indicator to evaluate environmental impacts from the machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the forward linkage, backward linkage and real benefit included the total environmental costs. The highest total environmental cost was railway equipment need to be resolved immediately because it uses natural resources in carrying capacity, higher than standard environmental cost, and contribute to low real benefit. Electric accumulator & battery, secondary special industrial machinery, motorcycle, bicycle & other carriages, and engines and turbines need to monitor closely because they are able to link to other production sectors more than other production sector do and they have high environmental cost. In order to decide the sustainable development strategy of the country, there is a need to use this research to support decision-making.

  7. MODELING ENVIRONMENTAL IMPACT OF MACHINERY SECTORS TO PROMOTE SUSTAINABLE DEVELOPMENT OF THAILAND

    OpenAIRE

    Pruethsan Sutthichaimethee

    2016-01-01

    The objective of this research is to propose an indicator to evaluate environmental impacts from the machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the forward linkage, backward linkage and real benefit included the total environmental costs. The highest total environmental cost was railway equipment need to be resolved immediately because it uses natural resources in carrying capacity, higher ...

  8. Vibration of hydraulic machinery

    CERN Document Server

    Wu, Yulin; Liu, Shuhong; Dou, Hua-Shu; Qian, Zhongdong

    2013-01-01

    Vibration of Hydraulic Machinery deals with the vibration problem which has significant influence on the safety and reliable operation of hydraulic machinery. It provides new achievements and the latest developments in these areas, even in the basic areas of this subject. The present book covers the fundamentals of mechanical vibration and rotordynamics as well as their main numerical models and analysis methods for the vibration prediction. The mechanical and hydraulic excitations to the vibration are analyzed, and the pressure fluctuations induced by the unsteady turbulent flow is predicted in order to obtain the unsteady loads. This book also discusses the loads, constraint conditions and the elastic and damping characters of the mechanical system, the structure dynamic analysis, the rotor dynamic analysis and the system instability of hydraulic machines, including the illustration of monitoring system for the instability and the vibration in hydraulic units. All the problems are necessary for vibration pr...

  9. Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Xiaojie Guo

    2016-12-01

    Full Text Available Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN and deep convolution neural network (DCNN, have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

  10. Dengue Virus Selectively Annexes Endoplasmic Reticulum-Associated Translation Machinery as a Strategy for Co-opting Host Cell Protein Synthesis.

    Science.gov (United States)

    Reid, David W; Campos, Rafael K; Child, Jessica R; Zheng, Tianli; Chan, Kitti Wing Ki; Bradrick, Shelton S; Vasudevan, Subhash G; Garcia-Blanco, Mariano A; Nicchitta, Christopher V

    2018-04-01

    A primary question in dengue virus (DENV) biology is the molecular strategy for recruitment of host cell protein synthesis machinery. Here, we combined cell fractionation, ribosome profiling, and transcriptome sequencing (RNA-seq) to investigate the subcellular organization of viral genome translation and replication as well as host cell translation and its response to DENV infection. We report that throughout the viral life cycle, DENV plus- and minus-strand RNAs were highly partitioned to the endoplasmic reticulum (ER), identifying the ER as the primary site of DENV translation. DENV infection was accompanied by an ER compartment-specific remodeling of translation, where ER translation capacity was subverted from host transcripts to DENV plus-strand RNA, particularly at late stages of infection. Remarkably, translation levels and patterns in the cytosol compartment were only modestly affected throughout the experimental time course of infection. Comparisons of ribosome footprinting densities of the DENV plus-strand RNA and host mRNAs indicated that DENV plus-strand RNA was only sparsely loaded with ribosomes. Combined, these observations suggest a mechanism where ER-localized translation and translational control mechanisms, likely cis encoded, are used to repurpose the ER for DENV virion production. Consistent with this view, we found ER-linked cellular stress response pathways commonly associated with viral infection, namely, the interferon response and unfolded protein response, to be only modestly activated during DENV infection. These data support a model where DENV reprograms the ER protein synthesis and processing environment to promote viral survival and replication while minimizing the activation of antiviral and proteostatic stress response pathways. IMPORTANCE DENV, a prominent human health threat with no broadly effective or specific treatment, depends on host cell translation machinery for viral replication, immune evasion, and virion biogenesis. The

  11. A fuel consumption model for off-road use of mobile machinery in agriculture

    International Nuclear Information System (INIS)

    Van linden, Veerle; Herman, Lieve

    2014-01-01

    Until 2009, the annual reporting of emissions by off-road transport in agriculture in Belgium was based on a 1994 calculation model that needed to be updated. An energy consumption model was established for plant production in Belgium as a backbone for a new emission model. The model starts from agricultural activities involving off-road fuel consumption. Effects of soil type, tractor size, field size and machine load are modelled. Twenty-seven FCIs (fuel consumption indicators) were computed for plant production. FCIs are expressed per year and are used for emission estimates on a regional level. FCIs ranged from 37 to 311 L/ha. Sensitivity analysis showed the highest impact of tractor size with a surplus fuel consumption between 10 and 41% depending on the crop type. Fuel consumption (L) can be further processed into greenhouse gas emissions. FCIs can be adopted in LCA (life cycle assessment) studies. With ∼310 L/ha, orchards are most fuel intensive, followed by field vegetables and sugar beets (∼150 L/ha). The total off-road energy consumption of field vegetables is high because second cropping is a common practice. - Highlights: • An energy consumption model is proposed that is based on farming activities. • No statistical data on fuel consumption are required for the calculation. • The published FCIs can be adopted in LCA studies directly or as an allocation key

  12. The Regulatory Machinery of Neurodegeneration in In Vitro Models of Amyotrophic Lateral Sclerosis

    Directory of Open Access Journals (Sweden)

    Burcin Ikiz

    2015-07-01

    Full Text Available Neurodegenerative phenotypes reflect complex, time-dependent molecular processes whose elucidation may reveal neuronal class-specific therapeutic targets. The current focus in neurodegeneration has been on individual genes and pathways. In contrast, we assembled a genome-wide regulatory model (henceforth, “interactome”, whose unbiased interrogation revealed 23 candidate causal master regulators of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis (ALS, characterized by a loss of spinal motor neurons (MNs. Of these, eight were confirmed as specific MN death drivers in our model of familial ALS, including NF-κB, which has long been considered a pro-survival factor. Through an extensive array of molecular, pharmacological, and biochemical approaches, we have confirmed that neuronal NF-κB drives the degeneration of MNs in both familial and sporadic models of ALS, thus providing proof of principle that regulatory network analysis is a valuable tool for studying cell-specific mechanisms of neurodegeneration.

  13. An application to model traffic intensity of agricultural machinery at field scale

    Science.gov (United States)

    Augustin, Katja; Kuhwald, Michael; Duttmann, Rainer

    2017-04-01

    Several soil-pressure-models deal with the impact of agricultural machines on soils. In many cases, these models were used for single spots and consider a static machine configuration. Therefore, a statement about the spatial distribution of soil compaction risk for entire working processes is limited. The aim of the study is the development of an application for the spatial modelling of traffic lanes from agricultural vehicles including wheel load, ground pressure and wheel passages at the field scale. The application is based on Open Source software, application and data formats, using python programming language. Minimum input parameters are GPS-positions, vehicles and tires (producer and model) and the tire inflation pressure. Five working processes were distinguished: soil tillage, manuring, plant protection, sowing and harvest. Currently, two different models (Diserens 2009, Rücknagel et al. 2015) were implemented to calculate the soil pressure. The application was tested at a study site in Lower Saxony, Germany. Since 2015, field traffic were recorded by RTK-GPS and used machine set ups were noted. Using these input information the traffic lanes, wheel load and soil pressure were calculated for all working processes. For instance, the maize harvest in 2016 with a crop chopper and one transport vehicle crossed about 55 % of the total field area. At some places the machines rolled over up to 46 times. Approximately 35 % of the total area was affected by wheel loads over 7 tons and soil pressures between 163 and 193 kPa. With the information about the spatial distribution of wheel passages, wheel load and soil pressure it is possible to identify hot spots of intensive field traffic. Additionally, the use of the application enables the analysis of soil compaction risk induced by agricultural machines for long- and short-term periods.

  14. Improving machinery reliability

    CERN Document Server

    Bloch, Heinz P

    1998-01-01

    This totally revised, updated and expanded edition provides proven techniques and procedures that extend machinery life, reduce maintenance costs, and achieve optimum machinery reliability. This essential text clearly describes the reliability improvement and failure avoidance steps practiced by best-of-class process plants in the U.S. and Europe.

  15. Recruiter Selection Model

    National Research Council Canada - National Science Library

    Halstead, John B

    2006-01-01

    .... The research uses a combination of statistical learning, feature selection methods, and multivariate statistics to determine the better prediction function approximation with features obtained...

  16. Model of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand

    Directory of Open Access Journals (Sweden)

    Sutthichaimethee Pruethsan

    2016-05-01

    Full Text Available The objective of this research is to propose an indicator to evaluate environmental impacts from the Machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the Forward Linkage, Backward Linkage and Real Benefit were the Total Environmental Costs. The highest total environmental cost was Railway Equipment which needs to be resolved immediately because it uses natural resources more than its carrying capacity, higher environmental cost than standard, and contributes low real benefit. Electric Accumulator & Battery, Secondary Special Industrial Machinery, Motorcycle, Bicycle & Other Carriages, and Engines and Turbines need to be monitored closely because they are able to link to other production sectors more than any other production sectors do, and they have high environmental cost. To decide a sustainable development strategy of the country, therefore, results of this research must be used to support decision-making.

  17. Model of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand

    Science.gov (United States)

    Sutthichaimethee, Pruethsan; Sawangdee, Yothin

    2016-05-01

    The objective of this research is to propose an indicator to evaluate environmental impacts from the Machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the Forward Linkage, Backward Linkage and Real Benefit were the Total Environmental Costs. The highest total environmental cost was Railway Equipment which needs to be resolved immediately because it uses natural resources more than its carrying capacity, higher environmental cost than standard, and contributes low real benefit. Electric Accumulator & Battery, Secondary Special Industrial Machinery, Motorcycle, Bicycle & Other Carriages, and Engines and Turbines need to be monitored closely because they are able to link to other production sectors more than any other production sectors do, and they have high environmental cost. To decide a sustainable development strategy of the country, therefore, results of this research must be used to support decision-making.

  18. Model of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand

    OpenAIRE

    Sutthichaimethee Pruethsan; Sawangdee Yothin

    2016-01-01

    The objective of this research is to propose an indicator to evaluate environmental impacts from the Machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the Forward Linkage, Backward Linkage and Real Benefit were the Total Environmental Costs. The highest total environmental cost was Railway Equipment which needs to be resolved immediately because it uses natural resources more than its carrying cap...

  19. Model selection in periodic autoregressions

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1994-01-01

    textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selection in practice. One aspect of model selection is the choice for the appropriate PAR order. This can be of interest for the valuation of economic models. Further, the appropriate PAR order is important

  20. Modeling Natural Selection

    Science.gov (United States)

    Bogiages, Christopher A.; Lotter, Christine

    2011-01-01

    In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…

  1. Models selection and fitting

    International Nuclear Information System (INIS)

    Martin Llorente, F.

    1990-01-01

    The models of atmospheric pollutants dispersion are based in mathematic algorithms that describe the transport, diffusion, elimination and chemical reactions of atmospheric contaminants. These models operate with data of contaminants emission and make an estimation of quality air in the area. This model can be applied to several aspects of atmospheric contamination

  2. Modeling of turbulent flows in cooling channels of turbo-machineries; Modelisation des ecoulements turbulents dans des canaux de refroidissement de turbomachines

    Energy Technology Data Exchange (ETDEWEB)

    Bidart, A.; Caltagirone, J.P.; Parneix, S. [Laboratoire MASTER-ENSCPB, 33 - Talence (France)

    1997-12-31

    The MASTER laboratory has been involved since several years in the creation and utilization of modeling tools for the prediction of 3-D turbulent flows and heat transfers in turbine blades in order to optimize the cooling systems of turbo-machineries. This paper describes one of the test-cases that has been used for the validation of the `Aquilon` calculation code developed in this aim. Then, the modeling performed with the `Fluent` industrial code in order to evaluate the possible improvements of the Aquilon code, is presented. (J.S.) 5 refs.

  3. Selected System Models

    Science.gov (United States)

    Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.

    Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.

  4. Design and modeling of an advanced marine machinery system including waste heat recovery and removal of sulphur oxides

    DEFF Research Database (Denmark)

    Frimann Nielsen, Rasmus; Haglind, Fredrik; Larsen, Ulrik

    2013-01-01

    -stroke diesel engine and a conventional waste heat recovery system. The results suggest that an organic Rankine cycle placed after the conventional waste heat recovery system is able to extract the sulphuric acid from the exhaust gas, while at the same time increase power generation from waste heat by 32...... consists of a two-stroke diesel engine, the wet sulphuric process for sulphur removal and an advanced waste heat recovery system including a conventional steam Rankine cycle and an organic Rankine cycle. The results are compared with those of a state-of-the-art machinery system featuring a two...

  5. Cavitation in Hydraulic Machinery

    Energy Technology Data Exchange (ETDEWEB)

    Kjeldsen, M.

    1996-11-01

    The main purpose of this doctoral thesis on cavitation in hydraulic machinery is to change focus towards the coupling of non-stationary flow phenomena and cavitation. It is argued that, in addition to turbulence, superimposed sound pressure fluctuations can have a major impact on cavitation and lead to particularly severe erosion. For the design of hydraulic devices this finding may indicate how to further limit the cavitation problems. Chapter 1 reviews cavitation in general in the context of hydraulic machinery, emphasizing the initial cavitation event and the role of the water quality. Chapter 2 discusses the existence of pressure fluctuations for situations common in such machinery. Chapter 3 on cavitation dynamics presents an algorithm for calculating the nucleation of a cavity cluster. Chapter 4 describes the equipment used in this work. 53 refs., 55 figs.,10 tabs.

  6. Launch vehicle selection model

    Science.gov (United States)

    Montoya, Alex J.

    1990-01-01

    Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction

  7. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.

    2012-03-01

    Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.

  8. Bacterial mitotic machineries

    DEFF Research Database (Denmark)

    Gerdes, Kenn; Møller-Jensen, Jakob; Ebersbach, Gitte

    2004-01-01

    Here, we review recent progress that yields fundamental new insight into the molecular mechanisms behind plasmid and chromosome segregation in prokaryotic cells. In particular, we describe how prokaryotic actin homologs form mitotic machineries that segregate DNA before cell division. Thus, the P......M protein of plasmid R1 forms F actin-like filaments that separate and move plasmid DNA from mid-cell to the cell poles. Evidence from three different laboratories indicate that the morphogenetic MreB protein may be involved in segregation of the bacterial chromosome.......Here, we review recent progress that yields fundamental new insight into the molecular mechanisms behind plasmid and chromosome segregation in prokaryotic cells. In particular, we describe how prokaryotic actin homologs form mitotic machineries that segregate DNA before cell division. Thus, the Par...

  9. The Resource Benefits Evaluation Model on Remanufacturing Processes of End-of-Life Construction Machinery under the Uncertainty in Recycling Price

    Directory of Open Access Journals (Sweden)

    Qian-wang Deng

    2017-02-01

    Full Text Available In the process of end-of-life construction machinery remanufacturing, the existence of uncertainties in all aspects of the remanufacturing process increase the difficulty and complexity of resource benefits evaluation for them. To quantify the effects of those uncertainty factors, this paper makes a mathematical analysis of the recycling and remanufacturing processes, building a resource benefits evaluation model for the end-of-life construction machinery. The recycling price and the profits of remanufacturers can thereby be obtained with a maximum remanufacturing resource benefit. The study investigates the change regularity of the resource benefits, recycling price, and profits of remanufacturers when the recycling price, quality fluctuation coefficient, demand coefficient, and the reusing ratio of products or parts are varying. In the numerical experiment, we explore the effects of uncertainties on the remanufacturing decisions and the total expected costs. The simulated analysis shows when the quality fluctuation coefficient is approaching to 1, the values of the profits of remanufacturer, the maximal resource benefits and recycling price grade into constants.

  10. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.; Genton, Marc G.

    2012-01-01

    for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical

  11. A Selective Assay to Detect Chitin and Biologically Active Nano-Machineries for Chitin-Biosynthesis with Their Intrinsic Chitin-Synthase Molecules

    Directory of Open Access Journals (Sweden)

    Hildgund Schrempf

    2010-09-01

    Full Text Available A new assay system for chitin has been developed. It comprises the chitin-binding protein ChbB in fusion with a His-tag as well as with a Strep-tag, the latter of which was chemically coupled to horseradish peroxidase. With the resulting complex, minimal quantities of chitin are photometrically detectable. In addition, the assay allows rapid scoring of the activity of chitin-synthases. As a result, a refined procedure for the rapid purification of yeast chitosomes (nano-machineries for chitin biosynthesis has been established. Immuno-electronmicroscopical studies of purified chitosomes, gained from a yeast strain carrying a chitin-synthase gene fused to that for GFP (green-fluorescence protein, has led to the in situ localization of chitin-synthase-GFP molecules within chitosomes.

  12. Model for Determining the Consumption of Machinery, Tableware, Fuel, Oils and Lubricants with the Participation of Units from the Bulgarian Army in Humanitarian Operations

    Directory of Open Access Journals (Sweden)

    Nichev Nikolay

    2017-06-01

    Full Text Available Regulating documents of the Bulgarian Army considered norms for support classic combat operations and in fact Bulgarian Army has no planning methods to be used for planning of humanitarian operations. Solving this problem can be achieved through the use of planning factors. Planning factors are generally based on experience or data on the use of resources from previous operations. The participation of units of the Bulgarian Army in humanitarian operations have failed to bring to the accumulation of data needed to create a suitable planning factors. The purpose of the study is to develop a model by which to bring out planning factors in determining the the consumption of machinery, tableware, fuel, oils and lubricants with the participation of units from the Bulgarian Army in humanitarian operations.

  13. Integration of fuzzy theory into Kano model for classification of service quality elements: A case study in a machinery industry of China

    Directory of Open Access Journals (Sweden)

    Qingliang Meng

    2015-11-01

    Full Text Available Purpose: The purpose of study is to meet customer requirements and improve customer satisfaction that aims to classify customer requirements more effectively. And the classification is focused on the customer psychology. Design/methodology/approach: In this study, considering the advantages of Kano model in taking into account both customer’s consuming psychology and motivation, and combining with fuzzy theory which is effective to cope with uncertainty and ambiguity, a Kano model based on fuzzy theory is proposed. In view of the strong subjectivity of traditional Kano questionnaires, a fuzzy Kano questionnaire to classify the service quality elements more objectively is proposed. Furthermore, this study will also develop a mathematical calculation performance according to the quality classification of fuzzy Kano model. It’s more objective than traditional Kano model to realize the service quality elements classification. With this method, the accurate mentality can be fully reasonable reflected in some unknown circumstances. Finally, an empirical study in Xuzhou Construction Machinery Group Co., Ltd, the largest manufacturing industry in China, is showed to testify its feasibility and validity. Findings: The calculation results indicate that the proposed model has good performance in classifying customer requirements. With this method, the accurate mentality can be fully reasonable reflected in unknown circumstances and it is more objective than traditional Kano model to classify the service quality elements. Originality/value: This study provides a method to integrate fuzzy theory and Kano model, and develops a mathematical calculation performance according to the quality classification of fuzzy Kano model.

  14. Matrix analysis of electrical machinery

    CERN Document Server

    Hancock, N N

    2013-01-01

    Matrix Analysis of Electrical Machinery, Second Edition is a 14-chapter edition that covers the systematic analysis of electrical machinery performance. This edition discusses the principles of various mathematical operations and their application to electrical machinery performance calculations. The introductory chapters deal with the matrix representation of algebraic equations and their application to static electrical networks. The following chapters describe the fundamentals of different transformers and rotating machines and present torque analysis in terms of the currents based on the p

  15. Pumping machinery theory and practice

    CERN Document Server

    Badr, Hassan M

    2014-01-01

    Pumping Machinery Theory and Practice comprehensively covers the theoretical foundation and applications of pumping machinery. Key features: Covers characteristics of centrifugal pumps, axial flow pumps and displacement pumpsConsiders pumping machinery performance and operational-type problemsCovers advanced topics in pumping machinery including multiphase flow principles, and two and three-phase flow pumping systemsCovers different methods of flow rate control and relevance to machine efficiency and energy consumptionCovers different methods of flow rate control and relevance to machine effi

  16. Selected Tether Applications Cost Model

    Science.gov (United States)

    Keeley, Michael G.

    1988-01-01

    Diverse cost-estimating techniques and data combined into single program. Selected Tether Applications Cost Model (STACOM 1.0) is interactive accounting software tool providing means for combining several independent cost-estimating programs into fully-integrated mathematical model capable of assessing costs, analyzing benefits, providing file-handling utilities, and putting out information in text and graphical forms to screen, printer, or plotter. Program based on Lotus 1-2-3, version 2.0. Developed to provide clear, concise traceability and visibility into methodology and rationale for estimating costs and benefits of operations of Space Station tether deployer system.

  17. Safeness of radiological machinery

    International Nuclear Information System (INIS)

    Yokoyama, Shun

    1979-01-01

    The human factors affecting the safeness of radiological machinery, which are often very big and complicated machines, are described from the stand point of handling. 20 to 50% of the troubles on equipments seem to be caused by men. This percentage will become even higher in highly developed equipments. Human factors have a great influence on the safeness of radiological equipments. As the human factors, there are sensory factors and knowledge factors as well as psychological factors, and the combination of these factors causes mishandling and danger. Medical services at present are divided in various areas, and consist of the teamwork of the people in various professions. Good human relationship, education and control are highly required to secure the safeness. (Kobatake, H.)

  18. Nonlinear Dynamic Modeling of a Supersonic Commercial Transport Turbo-Machinery Propulsion System for Aero-Propulso-Servo-Elasticity Research

    Science.gov (United States)

    Connolly, Joe; Carlson, Jan-Renee; Kopasakis, George; Woolwine, Kyle

    2015-01-01

    This paper covers the development of an integrated nonlinear dynamic model for a variable cycle turbofan engine, supersonic inlet, and convergent-divergent nozzle that can be integrated with an aeroelastic vehicle model to create an overall Aero-Propulso-Servo-Elastic (APSE) modeling tool. The primary focus of this study is to provide a means to capture relevant thrust dynamics of a full supersonic propulsion system by using relatively simple quasi-one dimensional computational fluid dynamics (CFD) methods that will allow for accurate control algorithm development and capture the key aspects of the thrust to feed into an APSE model. Previously, propulsion system component models have been developed and are used for this study of the fully integrated propulsion system. An overview of the methodology is presented for the modeling of each propulsion component, with a focus on its associated coupling for the overall model. To conduct APSE studies the described dynamic propulsion system model is integrated into a high fidelity CFD model of the full vehicle capable of conducting aero-elastic studies. Dynamic thrust analysis for the quasi-one dimensional dynamic propulsion system model is presented along with an initial three dimensional flow field model of the engine integrated into a supersonic commercial transport.

  19. Necrosome core machinery: MLKL.

    Science.gov (United States)

    Zhang, Jing; Yang, Yu; He, Wenyan; Sun, Liming

    2016-06-01

    In the study of regulated cell death, the rapidly expanding field of regulated necrosis, in particular necroptosis, has been drawing much attention. The signaling of necroptosis represents a sophisticated form of a death pathway. Anti-caspase mechanisms (e.g., using inhibitors of caspases, or genetic ablation of caspase-8) switch cell fate from apoptosis to necroptosis. The initial extracellular death signals regulate RIP1 and RIP3 kinase activation. The RIP3-associated death complex assembly is necessary and sufficient to initiate necroptosis. MLKL was initially identified as an essential mediator of RIP1/RIP3 kinase-initiated necroptosis. Recent studies on the signal transduction using chemical tools and biomarkers support the idea that MLKL is able to make more functional sense for the core machinery of the necroptosis death complex, called the necrosome, to connect to the necroptosis execution. The experimental data available now have pointed that the activated MLKL forms membrane-disrupting pores causing membrane leakage, which extends the prototypical concept of morphological and biochemical events following necroptosis happening in vivo. The key role of MLKL in necroptosis signaling thus sheds light on the logic underlying this unique "membrane-explosive" cell death pathway. In this review, we provide the general concepts and strategies that underlie signal transduction of this form of cell death, and then focus specifically on the role of MLKL in necroptosis.

  20. Basal transcription machinery

    Indian Academy of Sciences (India)

    2007-03-29

    Mar 29, 2007 ... The holoenzyme of prokaryotic RNA polymerase consists of the core enzyme, made of two , , ' and subunits, which lacks promoter selectivity and a sigma () subunit which enables the core enzyme to initiate transcription in a promoter dependent fashion. A stress sigma factor s, in prokaryotes ...

  1. Selected sports talent development models

    Directory of Open Access Journals (Sweden)

    Michal Vičar

    2017-06-01

    Full Text Available Background: Sports talent in the Czech Republic is generally viewed as a static, stable phenomena. It stands in contrast with widespread praxis carried out in Anglo-Saxon countries that emphasise its fluctuant nature. This is reflected in the current models describing its development. Objectives: The aim is to introduce current models of talent development in sport. Methods: Comparison and analysing of the following models: Balyi - Long term athlete development model, Côté - Developmental model of sport participation, Csikszentmihalyi - The flow model of optimal expertise, Bailey and Morley - Model of talent development. Conclusion: Current models of sport talent development approach talent as dynamic phenomenon, varying in time. They are based in particular on the work of Simonton and his Emergenic and epigenic model and of Gagné and his Differentiated model of giftedness and talent. Balyi's model is characterised by its applicability and impications for practice. Côté's model highlights the role of family and deliberate play. Both models describe periodization of talent development. Csikszentmihalyi's flow model explains how the athlete acquires experience and develops during puberty based on the structure of attention and flow experience. Bailey and Morley's model accents the situational approach to talent and development of skills facilitating its growth.

  2. Selected sports talent development models

    OpenAIRE

    Michal Vičar

    2017-01-01

    Background: Sports talent in the Czech Republic is generally viewed as a static, stable phenomena. It stands in contrast with widespread praxis carried out in Anglo-Saxon countries that emphasise its fluctuant nature. This is reflected in the current models describing its development. Objectives: The aim is to introduce current models of talent development in sport. Methods: Comparison and analysing of the following models: Balyi - Long term athlete development model, Côté - Developmen...

  3. MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS

    International Nuclear Information System (INIS)

    Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.

    2012-01-01

    Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.

  4. A Computational Model of Selection by Consequences

    Science.gov (United States)

    McDowell, J. J.

    2004-01-01

    Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of…

  5. Vibrations of rotating machinery

    CERN Document Server

    Matsushita, Osami; Kanki, Hiroshi; Kobayashi, Masao; Keogh, Patrick

    2017-01-01

    This book opens with an explanation of the vibrations of a single degree-of-freedom (dof) system for all beginners. Subsequently, vibration analysis of multi-dof systems is explained by modal analysis. Mode synthesis modeling is then introduced for system reduction, which aids understanding in a simplified manner of how complicated rotors behave. Rotor balancing techniques are offered for rigid and flexible rotors through several examples. Consideration of gyroscopic influences on the rotordynamics is then provided and vibration evaluation of a rotor-bearing system is emphasized in terms of forward and backward whirl rotor motions through eigenvalue (natural frequency and damping ratio) analysis. In addition to these rotordynamics concerning rotating shaft vibration measured in a stationary reference frame, blade vibrations are analyzed with Coriolis forces expressed in a rotating reference frame. Other phenomena that may be assessed in stationary and rotating reference frames include stability characteristic...

  6. A computational model of selection by consequences.

    OpenAIRE

    McDowell, J J

    2004-01-01

    Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied o...

  7. Bayesian Model Selection under Time Constraints

    Science.gov (United States)

    Hoege, M.; Nowak, W.; Illman, W. A.

    2017-12-01

    Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

  8. A Dynamic Model for Limb Selection

    NARCIS (Netherlands)

    Cox, R.F.A; Smitsman, A.W.

    2008-01-01

    Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In

  9. A Gambler's Model of Natural Selection.

    Science.gov (United States)

    Nolan, Michael J.; Ostrovsky, David S.

    1996-01-01

    Presents an activity that highlights the mechanism and power of natural selection. Allows students to think in terms of modeling a biological process and instills an appreciation for a mathematical approach to biological problems. (JRH)

  10. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)

    1994-04-04

    Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.

  11. Review and selection of unsaturated flow models

    International Nuclear Information System (INIS)

    Reeves, M.; Baker, N.A.; Duguid, J.O.

    1994-01-01

    Since the 1960's, ground-water flow models have been used for analysis of water resources problems. In the 1970's, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970's and well into the 1980's focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M ampersand O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M ampersand O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing

  12. Conceptual design and analysis of roads and road construction machinery for initial lunar base operations

    Science.gov (United States)

    Sines, Jeffrey L.; Banks, Joel; Efatpenah, Keyanoush

    1990-01-01

    Recent developments have made it possible for scientists and engineers to consider returning to the Moon to build a manned lunar base. The base can be used to conduct scientific research, develop new space technology, and utilize the natural resources of the Moon. Areas of the base will be separated, connected by a system of roads that reduce the power requirements of vehicles traveling on them. Feasible road types for the lunar surface were analyzed and a road construction system was designed for initial lunar base operations. A model was also constructed to show the system configuration and key operating features. The alternate designs for the lunar road construction system were developed in four stages: analyze and select a road type; determine operations and machinery needed to produce the road; develop machinery configurations; and develop alternates for several machine components. A compacted lunar soil road was selected for initial lunar base operations. The only machinery required to produce this road were a grader and a compactor. The road construction system consists of a main drive unit which is used for propulsion, a detachable grader assembly, and a towed compactor.

  13. 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…

  14. An evolutionary algorithm for model selection

    Energy Technology Data Exchange (ETDEWEB)

    Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)

    2013-07-01

    When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.

  15. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  16. Melody Track Selection Using Discriminative Language Model

    Science.gov (United States)

    Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong

    In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

  17. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  18. Strategies for Improving Enterprise Standardization Management of Tropical Crop Machinery

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    @@ There are two categories of tropical crop machinery. One comprises operation machinery that is used for planting, managing and harvesting tropical crops, while the other comprises process machinery for processing tropical crops. Tropical crop machinery is distinguished from other agricultural machinery by the special crops that such machinery cultivates and processes.

  19. Technical development of fluid machinery area

    International Nuclear Information System (INIS)

    Chung, Kyung Nam; Kim, Jin Young; Kim, Yang Ik

    2008-01-01

    In this paper, recent research activity of Hyundai Heavy Industries in the fluid machinery area is introduced. Technical development has been carried out in pumps, turbines, construction equipment, side thrusters, engine lubrication flow, etc. Here the technology of pumps and cooling of construction equipment will be dealt with. We have actively used computational fluid dynamics in the performance analysis of pump models and the design of new models of various industrial pumps or marine pumps. And a cooling analysis system composed of 3D flow analysis and 1D cooling analysis has been established, and applied to the design of cooling systems of new models of excavators and wheel loaders. The above mentioned technology is presented in details, and some future works are mentioned

  20. Expert System Model for Educational Personnel Selection

    Directory of Open Access Journals (Sweden)

    Héctor A. Tabares-Ospina

    2013-06-01

    Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.

  1. Utility machinery vibration monitoring guide: Final report

    International Nuclear Information System (INIS)

    Moore, T.T.; Thomas, C.C.

    1987-08-01

    Section I of this guide presents a methodology for developing machinery vibration monitoring programs specifically designed for application within the utility industry. The methodology is designed to enhance a monitoring program and can be used at the outset of program development or as a reference after programs have been started. Section I evaluates all aspects of the monitoring program, including Objectives and Goals, Information Type, Timing and Format, Data Analysis, Data Acquisition, Measurement and Transducer Selection, Personnel and Organization, Program Instrumentation, Program Costs, Program Justification, and Implementation of a Monitoring Program. The methodology is then applied to two host utility plants in Section II, which contains the monitoring programs developed by Gulf States Utilities and Philadelphia Electric Company using this guide. Section III contains the histories of several different types of existing utility monitoring programs. Some of the lessons learned, including the recommendations of these ''mature'' programs for persons starting new programs, are included

  2. Automated sample plan selection for OPC modeling

    Science.gov (United States)

    Casati, Nathalie; Gabrani, Maria; Viswanathan, Ramya; Bayraktar, Zikri; Jaiswal, Om; DeMaris, David; Abdo, Amr Y.; Oberschmidt, James; Krause, Andreas

    2014-03-01

    It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers' insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.

  3. Variable selection and model choice in geoadditive regression models.

    Science.gov (United States)

    Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

    2009-06-01

    Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

  4. Fault size classification of rotating machinery using support vector machine

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Y. S.; Lee, D. H.; Park, S. K. [Korea Hydro and Nuclear Power Co. Ltd., Daejeon (Korea, Republic of)

    2012-03-15

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults.

  5. Fault size classification of rotating machinery using support vector machine

    International Nuclear Information System (INIS)

    Kim, Y. S.; Lee, D. H.; Park, S. K.

    2012-01-01

    Studies on fault diagnosis of rotating machinery have been carried out to obtain a machinery condition in two ways. First is a classical approach based on signal processing and analysis using vibration and acoustic signals. Second is to use artificial intelligence techniques to classify machinery conditions into normal or one of the pre-determined fault conditions. Support Vector Machine (SVM) is well known as intelligent classifier with robust generalization ability. In this study, a two-step approach is proposed to predict fault types and fault sizes of rotating machinery in nuclear power plants using multi-class SVM technique. The model firstly classifies normal and 12 fault types and then identifies their sizes in case of predicting any faults. The time and frequency domain features are extracted from the measured vibration signals and used as input to SVM. A test rig is used to simulate normal and the well-know 12 artificial fault conditions with three to six fault sizes of rotating machinery. The application results to the test data show that the present method can estimate fault types as well as fault sizes with high accuracy for bearing an shaft-related faults and misalignment. Further research, however, is required to identify fault size in case of unbalance, rubbing, looseness, and coupling-related faults

  6. STUDY OF THE PARAMETERS OF EFFICIENCY IN CENTRES FOR REPAIR OF AGRICULTURAL MACHINERY

    Directory of Open Access Journals (Sweden)

    Natalia Stoyanova

    2015-06-01

    Full Text Available The paper makes a thorough study of the parameters of efficiency in the centers for the repair of agricultural machinery, considering production and technological structure, the basic principles for design of the process of service, the quantitative indicators for servicing. It presents a theoretical model for the management of services in the service business, taking into account the basic system requirements for maintenance of agricultural machinery, the main elements of the standards of customer service, choice of forms for maintenance of agricultural machinery. Opportunities are proposed for the optimization of repair activities in the centers for repair of agricultural machinery.

  7. Comparison of Management-Operational Efficiency of Agricultural Machinery Operating Systems (Case Study Alborz Province

    Directory of Open Access Journals (Sweden)

    A Omidi

    2017-10-01

    Full Text Available Introduction Measuring the efficiency of operating systems in comparison with the methods of comparing the performance of systems explains the various dimensions of issues such as, the lack of full use of agricultural machinery capacity, improper selection of machine, incorrect use of machinery, ownership, etc.. Any improvement in operating system conditions reduces costs,, consumption of inputs, increases the efficiency of production factors and consequently reduces the price and increases agricultural profitability. The main objective of this research is to compare the operational-management efficiency of operating systems in Alborz province and comparison of managerial and operational efficiency of agricultural machinery farming systems by calculating the efficiency of its major components in agricultural machinery farming systems including efficiency, social, economic, technical-operational and managerial and ranking them in order to understand the optimal model of agricultural machinery systems. Materials and Methods This research is a survey study.The study population was beneficiaries of agricultural machinery in the Alborz province which in the multi-stage random sample was determined. Alborz province has 31,438 agricultural operations, of which 543 are exploited agricultural machinery. Cochran formula was used to determine sample size. Since, Cronbach's alpha coefficient greater than 0.7 was obtained by questionnaire, the reliability of the questionnaires was assessed as desirable. To calculate the efficiency the component data were extracted from 4 specialized questionnaires after the initial examination and encoding, then they were analyzed using the software SPSS, MCDM Engine. TOPSIS techniques were used for ranking managerial performance operating system for operating agricultural machinery Alborz province. Results and Discussion The results showed that social efficiency of dedicated-professional operation with an average of 6.6 had

  8. Model Selection in Data Analysis Competitions

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Winther, Ole

    2014-01-01

    The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platform...... performers from Kaggle and use previous personal experiences from competing in Kaggle competitions. The stated hypotheses about feature engineering, ensembling, overfitting, model complexity and evaluation metrics give indications and guidelines on how to select a proper model for performing well...... Kaggle. In this paper, we will state and try to verify a set of qualitative hypotheses about predictive modelling, both in general and in the scope of data analysis competitions. To verify our hypotheses we will look at previous competitions and their outcomes, use qualitative interviews with top...

  9. Adverse selection model regarding tobacco consumption

    Directory of Open Access Journals (Sweden)

    Dumitru MARIN

    2006-01-01

    Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.

  10. Biosynthetic machinery of ionophore polyether lasalocid: enzymatic construction of polyether skeleton.

    Science.gov (United States)

    Minami, Atsushi; Oguri, Hiroki; Watanabe, Kenji; Oikawa, Hideaki

    2013-08-01

    Diversity of natural polycyclic polyethers originated from very simple yet versatile strategy consisting of epoxidation of linear polyene followed by epoxide opening cascade. To understand two-step enzymatic transformations at molecular basis, a flavin containing monooxygenase (EPX) Lsd18 and an epoxide hydrolase (EH) Lsd19 were selected as model enzymes for extensive investigation on substrate specificity, catalytic mechanism, cofactor requirement and crystal structure. This pioneering study on prototypical lasalocid EPX and EH provides insight into detailed mechanism of ionophore polyether assembly machinery and clarified remaining issues for polyether biosynthesis. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. FOREWORD: 26th IAHR Symposium on Hydraulic Machinery and Systems

    Science.gov (United States)

    Wu, Yulin; Wang, Zhengwei; Liu, Shuhong; Yuan, Shouqi; Luo, Xingqi; Wang, Fujun

    2012-11-01

    the value of hydraulic machinery to the end user, to the societies, and to improve societies understanding and appreciation of that value. The series of IAHR Symposia on Hydraulic Machinery and Cavitation started with the 1st edition in Nice, France, 1960. For the past decade, all the symposia have focused on an extended portfolio of topics under the name of 'Hydraulic Machinery and Systems', such as the 20th edition in Charlotte, USA, 2000, the 21st in Lausanne, Switzerland, 2002, the 22nd in Stockholm, Sweden, 2004, the 23rd in Yokohama, Japan, 2006, the 24th in Foz do Iguassu, Brasil, 2008, and the 25th in Timisoara, Romania, 2010. The 26th IAHR Symposium on Hydraulic Machinery and Systems brings together more than 250 scientists and researchers from 25 countries, affiliated with universities, technology centers and industrial firms to debate topics related to advanced technologies for hydraulic machinery and systems, which will enhance the sustainable development of water resources and hydropower production. The Scientific Committee has selected 268 papers, out of 430 abstracts submitted, on the following topics: (i) Hydraulic Turbines and Pumps, (ii) Sustainable Hydropower, (iii) Hydraulic Systems, (iv) Advances in Computational and Experimental Techniques, (v) Application in Industries and in Special Conditions, to be presented at the symposium and to be included in the proceedings. All the papers, published in this Volume 15 of IOP Conference Series: Earth and Environmental Science, have been peer reviewed through processes administered by the editors of the 26th IAHR Symposium on Hydraulic Machinery and Systems proceedings, those are Yulin Wu, Zhengwei Wang, Shuhong Liu, Shouqi Yuan, Xingqi Luo and Fujun Wang. We sincerely hope that this edition of the symposium will be a significant step forward in the worldwide efforts to address the present challenges facing the modern Hydraulic Machinery and Systems. Professor Yulin Wu Chairman of the Organizing Committee

  12. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1993-09-10

    Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.

  13. Expatriates Selection: An Essay of Model Analysis

    Directory of Open Access Journals (Sweden)

    Rui Bártolo-Ribeiro

    2015-03-01

    Full Text Available The business expansion to other geographical areas with different cultures from which organizations were created and developed leads to the expatriation of employees to these destinations. Recruitment and selection procedures of expatriates do not always have the intended success leading to an early return of these professionals with the consequent organizational disorders. In this study, several articles published in the last five years were analyzed in order to identify the most frequently mentioned dimensions in the selection of expatriates in terms of success and failure. The characteristics in the selection process that may increase prediction of adaptation of expatriates to new cultural contexts of the some organization were studied according to the KSAOs model. Few references were found concerning Knowledge, Skills and Abilities dimensions in the analyzed papers. There was a strong predominance on the evaluation of Other Characteristics, and was given more importance to dispositional factors than situational factors for promoting the integration of the expatriates.

  14. Post-model selection inference and model averaging

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2011-07-01

    Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.

  15. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon

    2015-12-21

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  16. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon; Maadooliat, Mehdi; Arellano-Valle, Reinaldo B.; Genton, Marc G.

    2015-01-01

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  17. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  18. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  19. Robust inference in sample selection models

    KAUST Repository

    Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

    2015-01-01

    The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman's two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

  20. Robust inference in sample selection models

    KAUST Repository

    Zhelonkin, Mikhail

    2015-11-20

    The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

  1. Efficiently adapting graphical models for selectivity estimation

    DEFF Research Database (Denmark)

    Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.

    2013-01-01

    cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss...... in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...

  2. Modelling the factors influencing the selection of the construction equipment for Indian construction organizations

    Directory of Open Access Journals (Sweden)

    S.V.S. Raja Prasad

    2016-09-01

    Full Text Available The contribution of Indian construction sector to the GDP is approximately 10%. Under new government policy, it is anticipated that $1000 Billion share for exclusively infrastructure segment would be completed within the next few years. Construction sector in developing country like India still depends on labor and the practice of mechanization, adopting to use of versatile construction equipment is not in force. The need for implementing new technologies and automation is essential to improve the quality, safety and efficiency. To meet the challenges ahead the construction, organizations should focus on utilization of machinery/equipment to achieve desirable results. Modern construction is characterized by the increase in utilization of equipment to accomplish numerous construction activities. The selection of construction equipment often affects the required amount of time and effort. It is therefore important for managements of construction organizations and planners to be familiar with the features of various types of equipment commonly used in construction activities. The selection of appropriate equipment is a crucial decision making process as it involves huge capital investment. The purpose of the present study is to develop a model pertaining to the factors influencing the selection of construction equipment by using interpretive structural modelling and the results indicate that productivity and safety are the important factors in selection of equipment in Indian construction organizations.

  3. Item selection via Bayesian IRT models.

    Science.gov (United States)

    Arima, Serena

    2015-02-10

    With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled as a function of individual latent traits and as a function of item characteristics, such as their difficulty and their discrimination power. We jointly model the discrimination and the difficulty parameters by using a k-component mixture of normal distributions. Mixture components correspond to disjoint groups of items. Items that belong to the same groups can be considered equivalent in terms of both difficulty and discrimination power. According to decision criteria, we select a subset of items such that the reduced questionnaire is able to provide the same information that the complete questionnaire provides. The model is estimated by using a Bayesian approach, and the choice of the number of mixture components is justified according to information criteria. We illustrate the proposed approach on the basis of data that are collected for 104 dysarthric patients by local health authorities in Lecce and in Milan. Copyright © 2014 John Wiley & Sons, Ltd.

  4. A Method of Rotating Machinery Fault Diagnosis Based on the Close Degree of Information Entropy

    Institute of Scientific and Technical Information of China (English)

    GENG Jun-bao; HUANG Shu-hong; JIN Jia-shan; CHEN Fei; LIU Wei

    2006-01-01

    This paper presents a method of rotating machinery fault diagnosis based on the close degree of information entropy. In the view of the information entropy, we introduce four information entropy features of the rotating machinery, which describe the vibration condition of the machinery. The four features are, respectively, denominated as singular spectrum entropy, power spectrum entropy, wavelet space state feature entropy and wavelet power spectrum entropy. The value scopes of the four information entropy features of the rotating machinery in some typical fault conditions are gained by experiments, which can be acted as the standard features of fault diagnosis. According to the principle of the shorter distance between the more similar models, the decision-making method based on the close degree of information entropy is put forward to deal with the recognition of fault patterns. We demonstrate the effectiveness of this approach in an instance involving the fault pattern recognition of some rotating machinery.

  5. Factors influencing creep model equation selection

    International Nuclear Information System (INIS)

    Holdsworth, S.R.; Askins, M.; Baker, A.; Gariboldi, E.; Holmstroem, S.; Klenk, A.; Ringel, M.; Merckling, G.; Sandstrom, R.; Schwienheer, M.; Spigarelli, S.

    2008-01-01

    During the course of the EU-funded Advanced-Creep Thematic Network, ECCC-WG1 reviewed the applicability and effectiveness of a range of model equations to represent the accumulation of creep strain in various engineering alloys. In addition to considering the experience of network members, the ability of several models to describe the deformation characteristics of large single and multi-cast collations of ε(t,T,σ) creep curves have been evaluated in an intensive assessment inter-comparison activity involving three steels, 21/4 CrMo (P22), 9CrMoVNb (Steel-91) and 18Cr13NiMo (Type-316). The choice of the most appropriate creep model equation for a given application depends not only on the high-temperature deformation characteristics of the material under consideration, but also on the characteristics of the dataset, the number of casts for which creep curves are available and on the strain regime for which an analytical representation is required. The paper focuses on the factors which can influence creep model selection and model-fitting approach for multi-source, multi-cast datasets

  6. Radiation technologies in metallurgy and machinery

    International Nuclear Information System (INIS)

    Meshkov, I.N.

    1990-01-01

    Applications of electron beam accelerators for technologies in metallurgy and machinery are discussed. Processes described are provided with special industrial accelerators, developed in the Institute of Nuclear Physics, Novosibirsk. (author)

  7. High-dimensional model estimation and model selection

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

  8. Halo models of HI selected galaxies

    Science.gov (United States)

    Paul, Niladri; Choudhury, Tirthankar Roy; Paranjape, Aseem

    2018-06-01

    Modelling the distribution of neutral hydrogen (HI) in dark matter halos is important for studying galaxy evolution in the cosmological context. We use a novel approach to infer the HI-dark matter connection at the massive end (m_H{I} > 10^{9.8} M_{⊙}) from radio HI emission surveys, using optical properties of low-redshift galaxies as an intermediary. In particular, we use a previously calibrated optical HOD describing the luminosity- and colour-dependent clustering of SDSS galaxies and describe the HI content using a statistical scaling relation between the optical properties and HI mass. This allows us to compute the abundance and clustering properties of HI-selected galaxies and compare with data from the ALFALFA survey. We apply an MCMC-based statistical analysis to constrain the free parameters related to the scaling relation. The resulting best-fit scaling relation identifies massive HI galaxies primarily with optically faint blue centrals, consistent with expectations from galaxy formation models. We compare the Hi-stellar mass relation predicted by our model with independent observations from matched Hi-optical galaxy samples, finding reasonable agreement. As a further application, we make some preliminary forecasts for future observations of HI and optical galaxies in the expected overlap volume of SKA and Euclid/LSST.

  9. Selecting a model of supersymmetry breaking mediation

    International Nuclear Information System (INIS)

    AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.

    2009-01-01

    We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the μ parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.

  10. Selective Oxidation of Lignin Model Compounds.

    Science.gov (United States)

    Gao, Ruili; Li, Yanding; Kim, Hoon; Mobley, Justin K; Ralph, John

    2018-05-02

    Lignin, the planet's most abundant renewable source of aromatic compounds, is difficult to degrade efficiently to welldefined aromatics. We developed a microwave-assisted catalytic Swern oxidation system using an easily prepared catalyst, MoO 2 Cl 2 (DMSO) 2 , and DMSO as the solvent and oxidant. It demonstrated high efficiency in transforming lignin model compounds containing the units and functional groups found in native lignins. The aromatic ring substituents strongly influenced the selectivity of β-ether phenolic dimer cleavage to generate sinapaldehyde and coniferaldehyde, monomers not usually produced by oxidative methods. Time-course studies on two key intermediates provided insight into the reaction pathway. Owing to the broad scope of this oxidation system and the insight gleaned with regard to its mechanism, this strategy could be adapted and applied in a general sense to the production of useful aromatic chemicals from phenolics and lignin. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Estimation of a multivariate mean under model selection uncertainty

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2014-05-01

    Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.

  12. 46 CFR 58.01-50 - Machinery space, noise.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery space, noise. 58.01-50 Section 58.01-50... MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-50 Machinery space, noise. (a) Each machinery space must be designed to minimize the exposure of personnel to noise in accordance with IMO A.468(XII...

  13. Hidden Markov Model for Stock Selection

    Directory of Open Access Journals (Sweden)

    Nguyet Nguyen

    2015-10-01

    Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.

  14. Psyche Mission: Scientific Models and Instrument Selection

    Science.gov (United States)

    Polanskey, C. A.; Elkins-Tanton, L. T.; Bell, J. F., III; Lawrence, D. J.; Marchi, S.; Park, R. S.; Russell, C. T.; Weiss, B. P.

    2017-12-01

    NASA has chosen to explore (16) Psyche with their 14th Discovery-class mission. Psyche is a 226-km diameter metallic asteroid hypothesized to be the exposed core of a planetesimal that was stripped of its rocky mantle by multiple hit and run collisions in the early solar system. The spacecraft launch is planned for 2022 with arrival at the asteroid in 2026 for 21 months of operations. The Psyche investigation has five primary scientific objectives: A. Determine whether Psyche is a core, or if it is unmelted material. B. Determine the relative ages of regions of Psyche's surface. C. Determine whether small metal bodies incorporate the same light elements as are expected in the Earth's high-pressure core. D. Determine whether Psyche was formed under conditions more oxidizing or more reducing than Earth's core. E. Characterize Psyche's topography. The mission's task was to select the appropriate instruments to meet these objectives. However, exploring a metal world, rather than one made of ice, rock, or gas, requires development of new scientific models for Psyche to support the selection of the appropriate instruments for the payload. If Psyche is indeed a planetary core, we expect that it should have a detectable magnetic field. However, the strength of the magnetic field can vary by orders of magnitude depending on the formational history of Psyche. The implications of both the extreme low-end and the high-end predictions impact the magnetometer and mission design. For the imaging experiment, what can the team expect for the morphology of a heavily impacted metal body? Efforts are underway to further investigate the differences in crater morphology between high velocity impacts into metal and rock to be prepared to interpret the images of Psyche when they are returned. Finally, elemental composition measurements at Psyche using nuclear spectroscopy encompass a new and unexplored phase space of gamma-ray and neutron measurements. We will present some end

  15. Molecular building blocks and their architecture in biologically/environmentally compatible soft matter chemical machinery.

    Science.gov (United States)

    Toyota, Taro; Banno, Taisuke; Nitta, Sachiko; Takinoue, Masahiro; Nomoto, Tomonori; Natsume, Yuno; Matsumura, Shuichi; Fujinami, Masanori

    2014-01-01

    This review briefly summarizes recent developments in the construction of biologically/environmentally compatible chemical machinery composed of soft matter. Since environmental and living systems are open systems, chemical machinery must continuously fulfill its functions not only through the influx and generation of molecules but also via the degradation and dissipation of molecules. If the degradation or dissipation of soft matter molecular building blocks and biomaterial molecules/polymers can be achieved, soft matter particles composed of them can be used to realize chemical machinery such as selfpropelled droplets, drug delivery carriers, tissue regeneration scaffolds, protocell models, cell-/tissuemarkers, and molecular computing systems.

  16. Machinery condition monitoring principles and practices

    CERN Document Server

    Mohanty, Amiya Ranjan

    2015-01-01

    Find the Fault in the MachinesDrawing on the author's more than two decades of experience with machinery condition monitoring and consulting for industries in India and abroad, Machinery Condition Monitoring: Principles and Practices introduces the practicing engineer to the techniques used to effectively detect and diagnose faults in machines. Providing the working principle behind the instruments, the important elements of machines as well as the technique to understand their conditions, this text presents every available method of machine fault detection occurring in machines in general, an

  17. A new Russell model for selecting suppliers

    NARCIS (Netherlands)

    Azadi, Majid; Shabani, Amir; Farzipoor Saen, Reza

    2014-01-01

    Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to

  18. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

    Science.gov (United States)

    Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

    2018-05-01

    The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

  19. A new structural framework for integrating replication protein A into DNA processing machinery

    Energy Technology Data Exchange (ETDEWEB)

    Brosey, Chris; Yan, Chunli; Tsutakawa, Susan; Heller, William; Rambo, Robert; Tainer, John; Ivanov, Ivaylo; Chazin, Walter

    2013-01-17

    By coupling the protection and organization of single-stranded DNA (ssDNA) with recruitment and alignment of DNA processing factors, replication protein A (RPA) lies at the heart of dynamic multi-protein DNA processing machinery. Nevertheless, how RPA coordinates biochemical functions of its eight domains remains unknown. We examined the structural biochemistry of RPA's DNA-binding activity, combining small-angle X-ray and neutron scattering with all-atom molecular dynamics simulations to investigate the architecture of RPA's DNA-binding core. The scattering data reveal compaction promoted by DNA binding; DNA-free RPA exists in an ensemble of states with inter-domain mobility and becomes progressively more condensed and less dynamic on binding ssDNA. Our results contrast with previous models proposing RPA initially binds ssDNA in a condensed state and becomes more extended as it fully engages the substrate. Moreover, the consensus view that RPA engages ssDNA in initial, intermediate and final stages conflicts with our data revealing that RPA undergoes two (not three) transitions as it binds ssDNA with no evidence for a discrete intermediate state. These results form a framework for understanding how RPA integrates the ssDNA substrate into DNA processing machinery, provides substrate access to its binding partners and promotes the progression and selection of DNA processing pathways.

  20. A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

    Science.gov (United States)

    Zhang, Bin; Deng, Congying; Zhang, Yi

    2018-03-01

    Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

  1. An integrated model for supplier selection process

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    In today's highly competitive manufacturing environment, the supplier selection process becomes one of crucial activities in supply chain management. In order to select the best supplier(s) it is not only necessary to continuously tracking and benchmarking performance of suppliers but also to make a tradeoff between tangible and intangible factors some of which may conflict. In this paper an integration of case-based reasoning (CBR), analytical network process (ANP) and linear programming (LP) is proposed to solve the supplier selection problem.

  2. CCPIT Machinery Exhibition Succeeded in Kuala Lumpur

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    @@ From August 18 to 20, 2005, China Council for the Promotion of International Trade(CCPIT) held China Machinery and Electronics Trade Exhibition, CME 2005 in Kuala Lumpur, the capital of Malaysia on behalf of China, a good job has been done.

  3. CCPIT Machinery Exhibition Succeeded in Kuala Lumpur

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

      From August 18 to 20, 2005, China Council for the Promotion of International Trade(CCPIT) held China Machinery and Electronics Trade Exhibition, CME 2005 in Kuala Lumpur, the capital of Malaysia on behalf of China, a good job has been done.……

  4. Dealing with selection bias in educational transition models

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads Meier

    2011-01-01

    This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models.......This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...

  5. A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

    Science.gov (United States)

    Thomas, D.L.; Johnson, D.; Griffith, B.

    2006-01-01

    Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

  6. Uncertainty associated with selected environmental transport models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-11-01

    A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation

  7. Quality Quandaries- Time Series Model Selection and Parsimony

    DEFF Research Database (Denmark)

    Bisgaard, Søren; Kulahci, Murat

    2009-01-01

    Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

  8. Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Nanda

    2011-01-01

    Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

  9. Open innovation and supply chain management in food machinery ...

    African Journals Online (AJOL)

    Open innovation and supply chain management in food machinery supply chain: a ... This paradigm describes a new approach to internal R&D management, which ... a picture of the adoption of open innovation in the food machinery industry.

  10. The Autophagic Machinery in Enterovirus Infection.

    Science.gov (United States)

    Lai, Jeffrey K F; Sam, I-Ching; Chan, Yoke Fun

    2016-01-27

    The Enterovirus genus of the Picornaviridae family comprises many important human pathogens, including polioviruses, rhinovirus, enterovirus A71, and enterovirus D68. They cause a wide variety of diseases, ranging from mild to severe life-threatening diseases. Currently, no effective vaccine is available against enteroviruses except for poliovirus. Enteroviruses subvert the autophagic machinery to benefit their assembly, maturation, and exit from host. Some enteroviruses spread between cells via a process described as autophagosome-mediated exit without lysis (AWOL). The early and late phases of autophagy are regulated through various lipids and their metabolizing enzymes. Some of these lipids and enzymes are specifically regulated by enteroviruses. In the present review, we summarize the current understanding of the regulation of autophagic machinery by enteroviruses, and provide updates on recent developments in this field.

  11. Selection of Hydrological Model for Waterborne Release

    International Nuclear Information System (INIS)

    Blanchard, A.

    1999-01-01

    This evaluation will aid in determining the potential impacts of liquid releases to downstream populations on the Savannah River. The purpose of this report is to evaluate the two available models and determine the appropriate model for use in following waterborne release analyses. Additionally, this report will document the Design Basis and Beyond Design Basis accidents to be used in the future study

  12. 46 CFR 109.205 - Inspection of boilers and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Inspection of boilers and machinery. 109.205 Section 109... OPERATIONS Tests, Drills, and Inspections § 109.205 Inspection of boilers and machinery. The chief engineer or engineer in charge, before he assumes charge of the boilers and machinery of a unit shall inspect...

  13. 46 CFR 252.33 - Hull and machinery insurance.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 8 2010-10-01 2010-10-01 false Hull and machinery insurance. 252.33 Section 252.33... Subsidy Rates § 252.33 Hull and machinery insurance. (a) Subsidy items. The fair and reasonable net premium costs (including stamp taxes) of hull and machinery, increased value, excess general average...

  14. 46 CFR 282.23 - Hull and machinery insurance.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 8 2010-10-01 2010-10-01 false Hull and machinery insurance. 282.23 Section 282.23... COMMERCE OF THE UNITED STATES Calculation of Subsidy Rates § 282.23 Hull and machinery insurance. (a) Subsidy items. The fair and reasonable net premium costs (including stamp taxes) of hull and machinery...

  15. 29 CFR 1910.215 - Abrasive wheel machinery.

    Science.gov (United States)

    2010-07-01

    ... be securely fastened to the spindle and the bearing surface shall run true. When more than one wheel... 29 Labor 5 2010-07-01 2010-07-01 false Abrasive wheel machinery. 1910.215 Section 1910.215 Labor... OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.215 Abrasive wheel machinery. (a...

  16. 46 CFR 97.30-5 - Accidents to machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Accidents to machinery. 97.30-5 Section 97.30-5 Shipping... Reports of Accidents, Repairs, and Unsafe Equipment § 97.30-5 Accidents to machinery. (a) In the event of an accident to a boiler, unfired pressure vessel, or machinery tending to render the further use of...

  17. 46 CFR 196.30-5 - Accidents to machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Accidents to machinery. 196.30-5 Section 196.30-5... Reports of Accidents, Repairs, and Unsafe Equipment § 196.30-5 Accidents to machinery. (a) In the event of an accident to a boiler, unfired pressure vessel, or machinery tending to render the further use of...

  18. Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.

    Energy Technology Data Exchange (ETDEWEB)

    Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton

    2018-02-01

    This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.

  19. Machinery prognostics and prognosis oriented maintenance management

    CERN Document Server

    Yan, Jihong

    2014-01-01

    This book gives a complete presentatin of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance.  Latest research results and application methods are introduced for signal processing, reliability moelling, deterioration evaluation, residual life prediction and maintenance-optimization as well as applications of these methods.

  20. Astrophysical Model Selection in Gravitational Wave Astronomy

    Science.gov (United States)

    Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.

    2012-01-01

    Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

  1. Analysis of IgV gene mutations in B cell chronic lymphocytic leukaemia according to antigen-driven selection identifies subgroups with different prognosis and usage of the canonical somatic hypermutation machinery.

    Science.gov (United States)

    Degan, Massimo; Bomben, Riccardo; Bo, Michele Dal; Zucchetto, Antonella; Nanni, Paola; Rupolo, Maurizio; Steffan, Agostino; Attadia, Vincenza; Ballerini, Pier Ferruccio; Damiani, Daniela; Pucillo, Carlo; Poeta, Giovanni Del; Colombatti, Alfonso; Gattei, Valter

    2004-07-01

    Cases of B-cell chronic lymphocytic leukaemia (B-CLL) with mutated (M) IgV(H) genes have a better prognosis than unmutated (UM) cases. We analysed the IgV(H) mutational status of B-CLL according to the features of a canonical somatic hypermutation (SHM) process, correlating this data with survival. In a series of 141 B-CLLs, 124 cases were examined for IgV(H) gene per cent mutations and skewing of replacement/silent mutations in the framework/complementarity-determining regions as evidence of antigen-driven selection; this identified three B-CLL subsets: significantly mutated (sM), with evidence of antigen-driven selection, not significantly mutated (nsM) and UM, without such evidence and IgV(H) gene per cent mutations above or below the 2% cut-off. sM B-CLL patients had longer survival within the good prognosis subgroup that had more than 2% mutations of IgV(H) genes. sM, nsM and UM B-CLL were also characterized for the biased usage of IgV(H) families, intraclonal IgV(H) gene diversification, preference of mutations to target-specific nucleotides or hotspots, and for the expression of enzymes involved in SHM (translesion DNA polymerase zeta and eta and activation-induced cytidine deaminase). These findings indicate the activation of a canonical SHM process in nsM and sM B-CLLs and underscore the role of the antigen in defining the specific clinical and biological features of B-CLL.

  2. A model for selecting leadership styles.

    Science.gov (United States)

    Perkins, V J

    1992-01-01

    Occupational therapists lead a variety of groups during their professional activities. Such groups include therapy groups, treatment teams and management meetings. Therefore it is important for each therapist to understand theories of leadership and be able to select the most effective style for him or herself in specific situations. This paper presents a review of leadership theory and research as well as therapeutic groups. It then integrates these areas to assist students and new therapists in identifying a style that is effective for a particular group.

  3. On Optimal Input Design and Model Selection for Communication Channels

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  4. Model selection in kernel ridge regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    2013-01-01

    Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...

  5. Model Selection in Kernel Ridge Regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

  6. Methods for model selection in applied science and engineering.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2004-10-01

    Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

  7. Selection of Hydrological Model for Waterborne Release

    International Nuclear Information System (INIS)

    Blanchard, A.

    1999-01-01

    Following a request from the States of South Carolina and Georgia, downstream radiological consequences from postulated accidental aqueous releases at the three Savannah River Site nonreactor nuclear facilities will be examined. This evaluation will aid in determining the potential impacts of liquid releases to downstream populations on the Savannah River. The purpose of this report is to evaluate the two available models and determine the appropriate model for use in following waterborne release analyses. Additionally, this report will document the accidents to be used in the future study

  8. Machinery Vibration Monitoring Program at the Savannah River Site

    International Nuclear Information System (INIS)

    Potvin, M.M.

    1990-01-01

    The Reactor Maintenance's Machinery Vibration Monitoring Program (MVMP) plays an essential role in ensuring the safe operation of the three Production Reactors at the Westinghouse Savannah River Company (WRSC) Savannah River Site (SRS). This program has increased machinery availability and reduced maintenance cost by the early detection and determination of machinery problems. This paper presents the Reactor Maintenance's Machinery Vibration Monitoring Program, which has been documented based on Electric Power Research Institute's (EPRI) NP-5311, Utility Machinery Monitoring Guide, and some examples of the successes that it has enjoyed

  9. Random effect selection in generalised linear models

    DEFF Research Database (Denmark)

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

  10. Adapting AIC to conditional model selection

    NARCIS (Netherlands)

    T. van Ommen (Thijs)

    2012-01-01

    textabstractIn statistical settings such as regression and time series, we can condition on observed information when predicting the data of interest. For example, a regression model explains the dependent variables $y_1, \\ldots, y_n$ in terms of the independent variables $x_1, \\ldots, x_n$.

  11. Fault Severity Estimation of Rotating Machinery Based on Residual Signals

    Directory of Open Access Journals (Sweden)

    Fan Jiang

    2012-01-01

    Full Text Available Fault severity estimation is an important part of a condition-based maintenance system, which can monitor the performance of an operation machine and enhance its level of safety. In this paper, a novel method based on statistical property and residual signals is developed for estimating the fault severity of rotating machinery. The fast Fourier transformation (FFT is applied to extract the so-called multifrequency-band energy (MFBE from the vibration signals of rotating machinery with different fault severity levels in the first stage. Usually these features of the working conditions with different fault sensitivities are different. Therefore a sensitive features-selecting algorithm is defined to construct the feature matrix and calculate the statistic parameter (mean in the second stage. In the last stage, the residual signals computed by the zero space vector are used to estimate the fault severity. Simulation and experimental results reveal that the proposed method based on statistics and residual signals is effective and feasible for estimating the severity of a rotating machine fault.

  12. A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

    Science.gov (United States)

    Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

    2015-01-01

    Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

  13. The genealogy of samples in models with selection.

    Science.gov (United States)

    Neuhauser, C; Krone, S M

    1997-02-01

    We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.

  14. Modeling shape selection of buckled dielectric elastomers

    Science.gov (United States)

    Langham, Jacob; Bense, Hadrien; Barkley, Dwight

    2018-02-01

    A dielectric elastomer whose edges are held fixed will buckle, given a sufficiently applied voltage, resulting in a nontrivial out-of-plane deformation. We study this situation numerically using a nonlinear elastic model which decouples two of the principal electrostatic stresses acting on an elastomer: normal pressure due to the mutual attraction of oppositely charged electrodes and tangential shear ("fringing") due to repulsion of like charges at the electrode edges. These enter via physically simplified boundary conditions that are applied in a fixed reference domain using a nondimensional approach. The method is valid for small to moderate strains and is straightforward to implement in a generic nonlinear elasticity code. We validate the model by directly comparing the simulated equilibrium shapes with the experiment. For circular electrodes which buckle axisymetrically, the shape of the deflection profile is captured. Annular electrodes of different widths produce azimuthal ripples with wavelengths that match our simulations. In this case, it is essential to compute multiple equilibria because the first model solution obtained by the nonlinear solver (Newton's method) is often not the energetically favored state. We address this using a numerical technique known as "deflation." Finally, we observe the large number of different solutions that may be obtained for the case of a long rectangular strip.

  15. Modeling HIV-1 drug resistance as episodic directional selection.

    Science.gov (United States)

    Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L; Scheffler, Konrad

    2012-01-01

    The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

  16. Modeling HIV-1 drug resistance as episodic directional selection.

    Directory of Open Access Journals (Sweden)

    Ben Murrell

    Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

  17. Variable selection for mixture and promotion time cure rate models.

    Science.gov (United States)

    Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

    2016-11-16

    Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

  18. Two-step variable selection in quantile regression models

    Directory of Open Access Journals (Sweden)

    FAN Yali

    2015-06-01

    Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

  19. Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain

    OpenAIRE

    Feipeng Guo; Qibei Lu

    2013-01-01

    With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...

  20. Analysis of electric machinery and drive systems

    CERN Document Server

    Krause, Paul C; Sudhoff, Scott D; Pekarek, Steven

    2013-01-01

    Introducing a new edition of the popular reference on machine analysis Now in a fully revised and expanded edition, this widely used reference on machine analysis boasts many changes designed to address the varied needs of engineers in the electric machinery, electric drives, and electric power industries. The authors draw on their own extensive research efforts, bringing all topics up to date and outlining a variety of new approaches they have developed over the past decade. Focusing on reference frame theory that has been at the core of this work since the first edition, th

  1. Electrification of agricultural machinery; Elektrifizierung von Landmaschinen

    Energy Technology Data Exchange (ETDEWEB)

    Goetz, Manuel; Grad, Karl; Weinmann, Olrik [ZF Friedrichshafen AG, Friedrichshafen (Germany)

    2012-10-15

    As early as 2009, ZF exhibited a generator system for agricultural machinery at Agritechnica under the name Terra+ which consisted of an electric motor in the transmission housing. As part of the ElecTra project, the company is now presenting its first tractor prototype with this generator system at Agritechnica 2011. The project involves combining the electrification of the tractor's auxiliary systems with electric drives for an attachment. The electrification of the implement was carried out in cooperation with Amazone, manufacturer of implements for the agricultural industry. (orig.)

  2. Effect of Model Selection on Computed Water Balance Components

    NARCIS (Netherlands)

    Jhorar, R.K.; Smit, A.A.M.F.R.; Roest, C.W.J.

    2009-01-01

    Soil water flow modelling approaches as used in four selected on-farm water management models, namely CROPWAT. FAIDS, CERES and SWAP, are compared through numerical experiments. The soil water simulation approaches used in the first three models are reformulated to incorporate ail evapotranspiration

  3. Ensembling Variable Selectors by Stability Selection for the Cox Model

    Directory of Open Access Journals (Sweden)

    Qing-Yan Yin

    2017-01-01

    Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

  4. Validation of elk resource selection models with spatially independent data

    Science.gov (United States)

    Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

    2011-01-01

    Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

  5. A Working Model of Natural Selection Illustrated by Table Tennis

    Science.gov (United States)

    Dinc, Muhittin; Kilic, Selda; Aladag, Caner

    2013-01-01

    Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…

  6. Augmented Self-Modeling as an Intervention for Selective Mutism

    Science.gov (United States)

    Kehle, Thomas J.; Bray, Melissa A.; Byer-Alcorace, Gabriel F.; Theodore, Lea A.; Kovac, Lisa M.

    2012-01-01

    Selective mutism is a rare disorder that is difficult to treat. It is often associated with oppositional defiant behavior, particularly in the home setting, social phobia, and, at times, autism spectrum disorder characteristics. The augmented self-modeling treatment has been relatively successful in promoting rapid diminishment of selective mutism…

  7. Response to selection in finite locus models with nonadditive effects

    NARCIS (Netherlands)

    Esfandyari, Hadi; Henryon, Mark; Berg, Peer; Thomasen, Jørn Rind; Bijma, Piter; Sørensen, Anders Christian

    2017-01-01

    Under the finite-locus model in the absence of mutation, the additive genetic variation is expected to decrease when directional selection is acting on a population, according to quantitative-genetic theory. However, some theoretical studies of selection suggest that the level of additive

  8. Elementary Teachers' Selection and Use of Visual Models

    Science.gov (United States)

    Lee, Tammy D.; Gail Jones, M.

    2018-02-01

    As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.

  9. A Decision-Analytic Feasibility Study of Upgrading Machinery at a Tools Workshop

    Directory of Open Access Journals (Sweden)

    M. L. Chew Hernandez

    2012-04-01

    Full Text Available This paper presents the evaluation, from a Decision Analysis point of view, of the feasibility of upgrading machinery at an existing metal-forming workshop. The Integral Decision Analysis (IDA methodology is applied to clarify the decision and develop a decision model. One of the key advantages of the IDA is its careful selection of the problem frame, allowing a correct problem definition. While following most of the original IDA methodology, an addition to this methodology is proposed in this work, that of using the strategic Means-Ends Objective Network as a backbone for the development of the decision model. The constructed decision model uses influence diagrams to include factual operator and vendor expertise, simulation to evaluate the alternatives and a utility function to take into account the risk attitude of the decision maker. Three alternatives are considered: Base (no modification, CNC (installing an automatic lathe and CF (installation of an automatic milling machine. The results are presented as a graph showing zones in which a particular alternative should be selected. The results show the potential of IDA to tackle technical decisions that are otherwise approached without the due care.

  10. Target Selection Models with Preference Variation Between Offenders

    NARCIS (Netherlands)

    Townsley, Michael; Birks, Daniel; Ruiter, Stijn; Bernasco, Wim; White, Gentry

    2016-01-01

    Objectives: This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories,

  11. COPS model estimates of LLEA availability near selected reactor sites

    International Nuclear Information System (INIS)

    Berkbigler, K.P.

    1979-11-01

    The COPS computer model has been used to estimate local law enforcement agency (LLEA) officer availability in the neighborhood of selected nuclear reactor sites. The results of these analyses are presented both in graphic and tabular form in this report

  12. Molecular modelling of a chemodosimeter for the selective detection ...

    Indian Academy of Sciences (India)

    Wintec

    Molecular modelling of a chemodosimeter for the selective detection of. As(III) ion in water. † ... high levels of arsenic cause severe skin diseases in- cluding skin cancer ..... Special Attention to Groundwater in SE Asia (eds) D. Chakraborti, A ...

  13. Computer System For Diagnostics of Mobile Machinery Transmission

    Directory of Open Access Journals (Sweden)

    G. L. Antipenko

    2004-01-01

    Full Text Available A new method for diagnostics of mechanical transmissions of mobile machinery is proposed in the paper. The method presupposes an application of computing equipment and its purpose is to decrease labor-consumption of diagnostics procedure and increase diagnostics efficiency.The method is based on comparison of duration of impulse periods picked up at primary transducers which are installed at transmission input and output. A signal picked up at a flywheel ring gear is taken as a reference signal.While selecting clearances of one and then the direction in speed-up - braking transmission regime changes in number of reference impulses at output provide data on angular clearance value in every gearing. As data are supplied registration and processing of results and forecasting of residual resource are to be done with the help of a program on the basis of realized algorithms for every gearing.

  14. Engineering yeast transcription machinery for improved ethanol tolerance and production.

    Science.gov (United States)

    Alper, Hal; Moxley, Joel; Nevoigt, Elke; Fink, Gerald R; Stephanopoulos, Gregory

    2006-12-08

    Global transcription machinery engineering (gTME) is an approach for reprogramming gene transcription to elicit cellular phenotypes important for technological applications. Here we show the application of gTME to Saccharomyces cerevisiae for improved glucose/ethanol tolerance, a key trait for many biofuels programs. Mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype results from the combined effect of three separate mutations in the SPT15 gene [serine substituted for phenylalanine (Phe(177)Ser) and, similarly, Tyr(195)His, and Lys(218)Arg]. Thus, gTME can provide a route to complex phenotypes that are not readily accessible by traditional methods.

  15. Model Selection in Continuous Test Norming With GAMLSS.

    Science.gov (United States)

    Voncken, Lieke; Albers, Casper J; Timmerman, Marieke E

    2017-06-01

    To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.

  16. Selection Criteria in Regime Switching Conditional Volatility Models

    Directory of Open Access Journals (Sweden)

    Thomas Chuffart

    2015-05-01

    Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.

  17. The RNAi Inheritance Machinery of Caenorhabditis elegans.

    Science.gov (United States)

    Spracklin, George; Fields, Brandon; Wan, Gang; Becker, Diveena; Wallig, Ashley; Shukla, Aditi; Kennedy, Scott

    2017-07-01

    Gene silencing mediated by dsRNA (RNAi) can persist for multiple generations in Caenorhabditis elegans (termed RNAi inheritance). Here we describe the results of a forward genetic screen in C. elegans that has identified six factors required for RNAi inheritance: GLH-1/VASA, PUP-1/CDE-1, MORC-1, SET-32, and two novel nematode-specific factors that we term here (heritable RNAi defective) HRDE-2 and HRDE-4 The new RNAi inheritance factors exhibit mortal germline (Mrt) phenotypes, which we show is likely caused by epigenetic deregulation in germ cells. We also show that HRDE-2 contributes to RNAi inheritance by facilitating the binding of small RNAs to the inheritance Argonaute (Ago) HRDE-1 Together, our results identify additional components of the RNAi inheritance machinery whose conservation provides insights into the molecular mechanism of RNAi inheritance, further our understanding of how the RNAi inheritance machinery promotes germline immortality, and show that HRDE-2 couples the inheritance Ago HRDE-1 with the small RNAs it needs to direct RNAi inheritance and germline immortality. Copyright © 2017 by the Genetics Society of America.

  18. Cell secretion machinery: Studies using the AFM

    International Nuclear Information System (INIS)

    Jena, Bhanu P.

    2006-01-01

    A new field in biology, 'nano-cell biology', has emerged from the successful use of force microscopy in understanding the structure and dynamics of cells and biomolecules, at nm resolution and in real time. Atomic force microscopy, in combination with conventional tools and approaches (electron microscopy, electrophysiology, X-ray diffraction, photon correlation spectroscopy, mass spectroscopy, biochemistry, and molecular biology), has revealed for the first time, the universal molecular machinery and mechanism of secretion in cells. Secretion occurs in all living cells and involves the delivery of intracellular products to the cell exterior. Secretory products are packaged and stored in membranous sacs or vesicles within the cell. When the cell needs to secrete these products, the secretory vesicles containing them, dock and fuse at plasma membrane-associated supramolecular structures called Porosome, to release their contents. Specialized cells for neurotransmission, enzyme secretion, or hormone release utilize a highly regulated secretory process. During secretion, swelling of secretory vesicles results in a build-up of intravesicular pressure, allowing expulsion of vesicular contents. The extent of vesicle swelling dictates the amount of vesicular contents expelled. The discovery of the porosome as the universal secretory machinery, its isolation, its structure and dynamics at nm resolution and in real time, its biochemical composition and functional reconstitution into artificial lipid membrane, have been determined. The molecular mechanism of secretory vesicle swelling, and the fusion of opposing bilayers, i.e., the fusion of secretory vesicle membrane at the base of the porosome membrane, has also been resolved

  19. Welding technologies for nuclear machinery and equipment

    International Nuclear Information System (INIS)

    Kobayashi, Masahiro; Yokono, Tomomi.

    1991-01-01

    The main welding methods applied to nuclear machinery and equipment are shielded metal arc welding, submerged arc welding, MAG welding and TIG welding. But in the last 10 years, in order to improve the reliability required for the welding of nuclear machinery and equipment, the welding technologies aiming at the reduction of heat input, the decrease of the number of welding pass and the automatic control of welding factors have been applied for the main purpose of bettering the quality and excluding human errors. The merits and the technology of narrow gap, pulsed MAG welding and melt-through welding are explained. As the automation of TIG welding, image processing type narrow gap, hot wire TIG welding and remote control type automatic TIG welding are described. For the longitudinal welding of active metal sheet products, plasma key-hole welding is applied. Since the concentration of its arc is good, high speed welding with low heat input can be done. For the stainless steel cladding by welding, electroslag welding has become to be employed in place of conventional submerged arc welding. Arc is not generated in the electroslag welding, and the penetration into base metal is small. (K.I.)

  20. Rotating Machinery Predictive Maintenance Through Expert System

    Directory of Open Access Journals (Sweden)

    M. Sarath Kumar

    2000-01-01

    Full Text Available Modern rotating machines such as turbomachines, either produce or absorb huge amount of power. Some of the common applications are: steam turbine-generator and gas turbine-compressor-generator trains produce power and machines, such as pumps, centrifugal compressors, motors, generators, machine tool spindles, etc., are being used in industrial applications. Condition-based maintenance of rotating machinery is a common practice where the machine's condition is monitored constantly, so that timely maintenance can be done. Since modern machines are complex and the amount of data to be interpreted is huge, we need precise and fast methods in order to arrive at the best recommendations to prevent catastrophic failure and to prolong the life of the equipment. In the present work using vibration characteristics of a rotor-bearing system, the condition of a rotating machinery (electrical rotor is predicted using an off-line expert system. The analysis of the problem is carried out in an Object Oriented Programming (OOP framework using the finite element method. The expert system which is also developed in an OOP paradigm gives the type of the malfunctions, suggestions and recommendations. The system is implemented in C++.

  1. The Use of Evolution in a Central Action Selection Model

    Directory of Open Access Journals (Sweden)

    F. Montes-Gonzalez

    2007-01-01

    Full Text Available The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot (animat has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm.

  2. A Hybrid Multiple Criteria Decision Making Model for Supplier Selection

    Directory of Open Access Journals (Sweden)

    Chung-Min Wu

    2013-01-01

    Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.

  3. Variable selection in Logistic regression model with genetic algorithm.

    Science.gov (United States)

    Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi

    2018-02-01

    Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

  4. Applying Four Different Risk Models in Local Ore Selection

    International Nuclear Information System (INIS)

    Richmond, Andrew

    2002-01-01

    Given the uncertainty in grade at a mine location, a financially risk-averse decision-maker may prefer to incorporate this uncertainty into the ore selection process. A FORTRAN program risksel is presented to calculate local risk-adjusted optimal ore selections using a negative exponential utility function and three dominance models: mean-variance, mean-downside risk, and stochastic dominance. All four methods are demonstrated in a grade control environment. In the case study, optimal selections range with the magnitude of financial risk that a decision-maker is prepared to accept. Except for the stochastic dominance method, the risk models reassign material from higher cost to lower cost processing options as the aversion to financial risk increases. The stochastic dominance model usually was unable to determine the optimal local selection

  5. Statistical model selection with “Big Data”

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2015-12-01

    Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

  6. On Development of Agricultural Machinery Operating Service in Chongqing

    Institute of Scientific and Technical Information of China (English)

    Chongjing; TAN; Shi; YANG

    2015-01-01

    Development of agricultural machinery operating service in Chongqing takes on rapid increase in number of service organizations,diversified service methods,improvement in service level,and constant service income. However,there are some problems,including unreasonable composition and small scale of service organization,imbalanced development of four service methods,low service level,and low operating income of agricultural machinery households. To accelerate development of agricultural machinery operating service in Chongqing,it is recommended to take following measures: adjusting subsidy for purchase and operation of agricultural machinery; improving fiscal and taxation and financial system; speeding up infrastructure construction,establishing agricultural machinery information network,and improving organizational form and methods of agricultural machinery operating service.

  7. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Directory of Open Access Journals (Sweden)

    Chen Lu

    Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for

  8. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    Science.gov (United States)

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  9. Selection, calibration, and validation of models of tumor growth.

    Science.gov (United States)

    Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

    2016-11-01

    This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory

  10. Comparison of climate envelope models developed using expert-selected variables versus statistical selection

    Science.gov (United States)

    Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

    2017-01-01

    Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

  11. Models of microbiome evolution incorporating host and microbial selection.

    Science.gov (United States)

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

    2017-09-25

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

  12. Development of an Environment for Software Reliability Model Selection

    Science.gov (United States)

    1992-09-01

    now is directed to other related problems such as tools for model selection, multiversion programming, and software fault tolerance modeling... multiversion programming, 7. Hlardware can be repaired by spare modules, which is not. the case for software, 2-6 N. Preventive maintenance is very important

  13. Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach

    Directory of Open Access Journals (Sweden)

    Haifeng Guo

    2012-01-01

    Full Text Available This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.

  14. Diversified models for portfolio selection based on uncertain semivariance

    Science.gov (United States)

    Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

    2017-02-01

    Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

  15. A Primer for Model Selection: The Decisive Role of Model Complexity

    Science.gov (United States)

    Höge, Marvin; Wöhling, Thomas; Nowak, Wolfgang

    2018-03-01

    Selecting a "best" model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the "best" trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

  16. Selection of climate change scenario data for impact modelling

    DEFF Research Database (Denmark)

    Sloth Madsen, M; Fox Maule, C; MacKellar, N

    2012-01-01

    Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study...... illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make...... the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented...

  17. A SUPPLIER SELECTION MODEL FOR SOFTWARE DEVELOPMENT OUTSOURCING

    Directory of Open Access Journals (Sweden)

    Hancu Lucian-Viorel

    2010-12-01

    Full Text Available This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.

  18. Multiple Cylinder Free-Piston Stirling Machinery

    Science.gov (United States)

    Berchowitz, David M.; Kwon, Yong-Rak

    In order to improve the specific power of piston-cylinder type machinery, there is a point in capacity or power where an advantage accrues with increasing number of piston-cylinder assemblies. In the case of Stirling machinery where primary energy is transferred across the casing wall of the machine, this consideration is even more important. This is due primarily to the difference in scaling of basic power and the required heat transfer. Heat transfer is found to be progressively limited as the size of the machine increases. Multiple cylinder machines tend to preserve the surface area to volume ratio at more favorable levels. In addition, the spring effect of the working gas in the so-called alpha configuration is often sufficient to provide a high frequency resonance point that improves the specific power. There are a number of possible multiple cylinder configurations. The simplest is an opposed pair of piston-displacer machines (beta configuration). A three-cylinder machine requires stepped pistons to obtain proper volume phase relationships. Four to six cylinder configurations are also possible. A small demonstrator inline four cylinder alpha machine has been built to demonstrate both cooling operation and power generation. Data from this machine verifies theoretical expectations and is used to extrapolate the performance of future machines. Vibration levels are discussed and it is argued that some multiple cylinder machines have no linear component to the casing vibration but may have a nutating couple. Example applications are discussed ranging from general purpose coolers, computer cooling, exhaust heat power extraction and some high power engines.

  19. Adverse Selection Models with Three States of Nature

    Directory of Open Access Journals (Sweden)

    Daniela MARINESCU

    2011-02-01

    Full Text Available In the paper we analyze an adverse selection model with three states of nature, where both the Principal and the Agent are risk neutral. When solving the model, we use the informational rents and the efforts as variables. We derive the optimal contract in the situation of asymmetric information. The paper ends with the characteristics of the optimal contract and the main conclusions of the model.

  20. Comparing the staffing models of outsourcing in selected companies

    OpenAIRE

    Chaloupková, Věra

    2010-01-01

    This thesis deals with problems of takeover of employees in outsourcing. The capital purpose is to compare the staffing model of outsourcing in selected companies. To compare in selected companies I chose multi-criteria analysis. This thesis is dividend into six chapters. The first charter is devoted to the theoretical part. In this charter describes the basic concepts as outsourcing, personal aspects, phase of the outsourcing projects, communications and culture. The rest of thesis is devote...

  1. ERP Software Selection Model using Analytic Network Process

    OpenAIRE

    Lesmana , Andre Surya; Astanti, Ririn Diar; Ai, The Jin

    2014-01-01

    During the implementation of Enterprise Resource Planning (ERP) in any company, one of the most important issues is the selection of ERP software that can satisfy the needs and objectives of the company. This issue is crucial since it may affect the duration of ERP implementation and the costs incurred for the ERP implementation. This research tries to construct a model of the selection of ERP software that are beneficial to the company in order to carry out the selection of the right ERP sof...

  2. Economic assessment model architecture for AGC/AVLIS selection

    International Nuclear Information System (INIS)

    Hoglund, R.L.

    1984-01-01

    The economic assessment model architecture described provides the flexibility and completeness in economic analysis that the selection between AGC and AVLIS demands. Process models which are technology-specific will provide the first-order responses of process performance and cost to variations in process parameters. The economics models can be used to test the impacts of alternative deployment scenarios for a technology. Enterprise models provide global figures of merit for evaluating the DOE perspective on the uranium enrichment enterprise, and business analysis models compute the financial parameters from the private investor's viewpoint

  3. IT vendor selection model by using structural equation model & analytical hierarchy process

    Science.gov (United States)

    Maitra, Sarit; Dominic, P. D. D.

    2012-11-01

    Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

  4. Recent evolutions of refrigerating machineries and heat pumps; Evolutions recentes des machines a froid et thermopompes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-12-31

    This book of proceedings reports on 10 papers (or series of transparencies) concerning some recent developments about refrigerating machineries and heat pumps as used in space heating, air-conditioning and industrial refrigeration. Various aspects are developed: thermodynamic cycles, thermal performances, dimensioning, modeling, refrigerants substitution, design of flanged exchangers, compressors etc.. (J.S.)

  5. Developing an Online Learning Media Using Smartphone for the Electrical Machinery Course

    Science.gov (United States)

    Muchlas

    2018-01-01

    This research is aimed to prepare a desktop-based learning media that can be used to support an online lab activities using android smartphones in Electrical Machinery Course at the Department of Electrical Engineering for the undergraduate level. This work uses a conceptual development model which integrates some sub systems of internet…

  6. Recent evolutions of refrigerating machineries and heat pumps; Evolutions recentes des machines a froid et thermopompes

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-12-31

    This book of proceedings reports on 10 papers (or series of transparencies) concerning some recent developments about refrigerating machineries and heat pumps as used in space heating, air-conditioning and industrial refrigeration. Various aspects are developed: thermodynamic cycles, thermal performances, dimensioning, modeling, refrigerants substitution, design of flanged exchangers, compressors etc.. (J.S.)

  7. Model Selection in Historical Research Using Approximate Bayesian Computation

    Science.gov (United States)

    Rubio-Campillo, Xavier

    2016-01-01

    Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. Case Study This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester’s laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Impact Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence. PMID:26730953

  8. Sample selection and taste correlation in discrete choice transport modelling

    DEFF Research Database (Denmark)

    Mabit, Stefan Lindhard

    2008-01-01

    explain counterintuitive results in value of travel time estimation. However, the results also point at the difficulty of finding suitable instruments for the selection mechanism. Taste heterogeneity is another important aspect of discrete choice modelling. Mixed logit models are designed to capture...... the question for a broader class of models. It is shown that the original result may be somewhat generalised. Another question investigated is whether mode choice operates as a self-selection mechanism in the estimation of the value of travel time. The results show that self-selection can at least partly...... of taste correlation in willingness-to-pay estimation are presented. The first contribution addresses how to incorporate taste correlation in the estimation of the value of travel time for public transport. Given a limited dataset the approach taken is to use theory on the value of travel time as guidance...

  9. Short-Run Asset Selection using a Logistic Model

    Directory of Open Access Journals (Sweden)

    Walter Gonçalves Junior

    2011-06-01

    Full Text Available Investors constantly look for significant predictors and accurate models to forecast future results, whose occasional efficacy end up being neutralized by market efficiency. Regardless, such predictors are widely used for seeking better (and more unique perceptions. This paper aims to investigate to what extent some of the most notorious indicators have discriminatory power to select stocks, and if it is feasible with such variables to build models that could anticipate those with good performance. In order to do that, logistical regressions were conducted with stocks traded at Bovespa using the selected indicators as explanatory variables. Investigated in this study were the outputs of Bovespa Index, liquidity, the Sharpe Ratio, ROE, MB, size and age evidenced to be significant predictors. Also examined were half-year, logistical models, which were adjusted in order to check the potential acceptable discriminatory power for the asset selection.

  10. Structural Insights Into The Bacterial Carbon-Phosphorus Lyase Machinery

    DEFF Research Database (Denmark)

    Brodersen, Ditlev Egeskov

    the proteins encoded in the phn operon act in concert to catabolise phosphonate remain unknown. We have determined the crystal structure of a 240 kDa Escherichia coli carbon-phosphorus lyase core complex at 1.7 Å and show that it comprises a highly intertwined network of subunits with several unexpected......Phosphonate compounds act as a nutrient source for some microorganisms when phosphate is limiting but require a specialised enzymatic machinery due to the presence of the highly stable carbon-phosphorus bond. Despite the fundamental importance to microbial metabolism, the details of how...... structural features. The complex contains at least two different active sites and suggest a revision of current models of carbon-phosphorus bond cleavage. Using electron microscopy, we map the binding site of an additional protein subunit, which may use ATP for driving conformational changes during...

  11. C4 photosynthetic machinery: insights from maize chloroplast proteomics

    Directory of Open Access Journals (Sweden)

    Qi eZhao

    2013-04-01

    Full Text Available C4 plants exhibit much higher CO2 assimilation rates than C3 plants. The specialized differentiation of mesophyll cell (M and bundle sheath cell (BS type chloroplasts is unique to C4 plants and improves photosynthesis efficiency. Maize (Zea mays is an important crop and model with C4 photosynthetic machinery. Current high-throughput quantitative proteomics approaches (e.g., 2DE, iTRAQ, and shotgun proteomics have been employed to investigate maize chloroplast structure and function. These proteomic studies have provided valuable information on C4 chloroplast protein components, photosynthesis, and other metabolic mechanisms underlying chloroplast biogenesis, stromal and membrane differentiation, as well as response to salinity, high/low temperature, and light stress. This review presents an overview of proteomics advances in maize chloroplast biology.

  12. Uncertain programming models for portfolio selection with uncertain returns

    Science.gov (United States)

    Zhang, Bo; Peng, Jin; Li, Shengguo

    2015-10-01

    In an indeterminacy economic environment, experts' knowledge about the returns of securities consists of much uncertainty instead of randomness. This paper discusses portfolio selection problem in uncertain environment in which security returns cannot be well reflected by historical data, but can be evaluated by the experts. In the paper, returns of securities are assumed to be given by uncertain variables. According to various decision criteria, the portfolio selection problem in uncertain environment is formulated as expected-variance-chance model and chance-expected-variance model by using the uncertainty programming. Within the framework of uncertainty theory, for the convenience of solving the models, some crisp equivalents are discussed under different conditions. In addition, a hybrid intelligent algorithm is designed in the paper to provide a general method for solving the new models in general cases. At last, two numerical examples are provided to show the performance and applications of the models and algorithm.

  13. Machinery failure analysis and troubleshooting practical machinery management for process plants

    CERN Document Server

    Bloch, Heinz P

    2012-01-01

    Solve the machinery failure problems costing you time and money with this classic, comprehensive guide to analysis and troubleshooting  Provides detailed, complete and accurate information on anticipating risk of component failure and avoiding equipment downtime Includes numerous photographs of failed parts to ensure you are familiar with the visual evidence you need to recognize Covers proven approaches to failure definition and offers failure identification and analysis methods that can be applied to virtually all problem situations Demonstr

  14. The Properties of Model Selection when Retaining Theory Variables

    DEFF Research Database (Denmark)

    Hendry, David F.; Johansen, Søren

    Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....

  15. 46 CFR 169.315 - Ventilation (other than machinery spaces).

    Science.gov (United States)

    2010-10-01

    ... section is satisfied, a vessel having only a natural ventilation system must satisfy the following: V/A≥1... 46 Shipping 7 2010-10-01 2010-10-01 false Ventilation (other than machinery spaces). 169.315... SCHOOL VESSELS Construction and Arrangement Hull Structure § 169.315 Ventilation (other than machinery...

  16. 29 CFR 1915.164 - Ship's propulsion machinery.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 7 2010-07-01 2010-07-01 false Ship's propulsion machinery. 1915.164 Section 1915.164 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR (CONTINUED) OCCUPATIONAL SAFETY AND HEALTH STANDARDS FOR SHIPYARD EMPLOYMENT Ship's Machinery and Piping Systems § 1915.164 Ship's...

  17. A Survey of Fish Production and Processing Machinery in Rivers ...

    African Journals Online (AJOL)

    Survey of fish production and processing machinery in Port Harcourt City Local Government Area of Rivers State, Nigeria was carried out to evaluate the followings: different machines used for fish production and processing, the most acceptable machine, effect of cost of machinery on the fish farmer, whether gender has ...

  18. 46 CFR 78.33-5 - Accidents to machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 3 2010-10-01 2010-10-01 false Accidents to machinery. 78.33-5 Section 78.33-5 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) PASSENGER VESSELS OPERATIONS Reports of Accidents, Repairs, and Unsafe Equipment § 78.33-5 Accidents to machinery. (a) In the event of an accident...

  19. 46 CFR 185.208 - Accidents to machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Accidents to machinery. 185.208 Section 185.208 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) SMALL PASSENGER VESSELS (UNDER 100 GROSS TONS) OPERATIONS Marine Casualties and Voyage Records § 185.208 Accidents to machinery. The owner, managing...

  20. 46 CFR 122.208 - Accidents to machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Accidents to machinery. 122.208 Section 122.208 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) SMALL PASSENGER VESSELS CARRYING MORE THAN 150... Voyage Records § 122.208 Accidents to machinery. The owner, managing operator, or master shall report...

  1. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

    Directory of Open Access Journals (Sweden)

    Chuan Li

    2016-06-01

    Full Text Available Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM. The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  2. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Science.gov (United States)

    Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

    2016-06-17

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  3. Assessment of exposure to manganese in welding operations during the assembly of heavy excavation machinery accessories.

    Science.gov (United States)

    Smargiassi, A; Baldwin, M; Savard, S; Kennedy, G; Mergler, D; Zayed, J

    2000-10-01

    Welder exposure to metals in various industrial sectors is poorly characterized. We had the opportunity to carry out an exploratory study to characterize manganese exposure in welding operations in a recently established Quebec factory that assembled accessories for heavy excavation machinery. Ten workers were sampled for total manganese for at least two consecutive days out of three followed by two consecutive days for respirable manganese (with a size selective sampler with a median cut-off of 4 microns), during a typical week in the summer of 1998. Parts being welded were characterized as large or small. Small parts were those being welded on tables during subassembly. Workers were divided into two groups according to the parts they were welding. Seventy-eight percent of the total manganese exposure levels of welding operations during the assembly of large accessories of heavy excavation machinery exceeded the manganese American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV) of 0.20 mg/m3 (GM 0.24 mg/m3, n = 14) while none exceeded the TLV during the assembly of small pieces (GM 0.06 mg/m3, n = 8). Welding operations during the assembly of large heavy excavation machinery accessories may pose a significant health hazard. Considering the importance of task-related variables affecting exposure among workers, further studies are needed to better characterize exposure determinants of welding operations during the assembly of heavy excavation machinery accessories.

  4. DETERMINATION OF AGRICULTURAL MACHINERY OPERATORS’ OPINIONS ABOUT THE CABIN COMFORT IN ESKİŞEHİR

    Directory of Open Access Journals (Sweden)

    Özge ACARBAŞ BALTACI

    2015-08-01

    Full Text Available Comfort has a great importance in the interior design of tractor and agricultural machinery cabins. Operators are exposed to muscoskeletal system disorders since they spend long time periods during the day in these vehicles. There is a few work in the literature reporting operators’ opinions about cabin comfort of these machineries. In this study, a questionnaire was conducted in order to get information about agricultural machinery operators’ opinions about the comfort of their vehicles. Tractor cabins and combine harvester machine cabins were selected as machineries. The study was conducted in Eskişehir in Turkey. Questionnaire was composed of four groups of questions and five ordered response levels were used in the Likert's scale. Demographic questions, general questions about the machine, personal evaluation questions and open ended questions were asked to the operators. After the questionnaire completed, collected data were classified according to the machine type. Frequency tables were used to present the results. Visibility and the accessibility were the most satisfied issues for the tractor operators with 55.9% and 55.4% percentages, respectively. Seat comfort has the highest satisfaction degree with 43.7% for the combine harvester operators. Cronbach Alpha reliability coefficient was used for the satisfaction questions in the applied questionnaire. The reliability of the study was high with coefficients of 0.878 and 0.940 for the tractor and combine harvester questionnaires, respectively. This study will support design and development process of new products by considering operator opinions.

  5. Fixation probability in a two-locus intersexual selection model.

    Science.gov (United States)

    Durand, Guillermo; Lessard, Sabin

    2016-06-01

    We study a two-locus model of intersexual selection in a finite haploid population reproducing according to a discrete-time Moran model with a trait locus expressed in males and a preference locus expressed in females. We show that the probability of ultimate fixation of a single mutant allele for a male ornament introduced at random at the trait locus given any initial frequency state at the preference locus is increased by weak intersexual selection and recombination, weak or strong. Moreover, this probability exceeds the initial frequency of the mutant allele even in the case of a costly male ornament if intersexual selection is not too weak. On the other hand, the probability of ultimate fixation of a single mutant allele for a female preference towards a male ornament introduced at random at the preference locus is increased by weak intersexual selection and weak recombination if the female preference is not costly, and is strong enough in the case of a costly male ornament. The analysis relies on an extension of the ancestral recombination-selection graph for samples of haplotypes to take into account events of intersexual selection, while the symbolic calculation of the fixation probabilities is made possible in a reasonable time by an optimizing algorithm. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Spatial Fleming-Viot models with selection and mutation

    CERN Document Server

    Dawson, Donald A

    2014-01-01

    This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.

  7. Uniform design based SVM model selection for face recognition

    Science.gov (United States)

    Li, Weihong; Liu, Lijuan; Gong, Weiguo

    2010-02-01

    Support vector machine (SVM) has been proved to be a powerful tool for face recognition. The generalization capacity of SVM depends on the model with optimal hyperparameters. The computational cost of SVM model selection results in application difficulty in face recognition. In order to overcome the shortcoming, we utilize the advantage of uniform design--space filling designs and uniformly scattering theory to seek for optimal SVM hyperparameters. Then we propose a face recognition scheme based on SVM with optimal model which obtained by replacing the grid and gradient-based method with uniform design. The experimental results on Yale and PIE face databases show that the proposed method significantly improves the efficiency of SVM model selection.

  8. How Many Separable Sources? Model Selection In Independent Components Analysis

    DEFF Research Database (Denmark)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian....

  9. Selecting an optimal mixed products using grey relationship model

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2013-06-01

    Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.

  10. Model selection and inference a practical information-theoretic approach

    CERN Document Server

    Burnham, Kenneth P

    1998-01-01

    This book is unique in that it covers the philosophy of model-based data analysis and an omnibus strategy for the analysis of empirical data The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data Kullback-Leibler information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information This leads to Akaike's Information Criterion (AIC) and various extensions and these are relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are ...

  11. Working covariance model selection for generalized estimating equations.

    Science.gov (United States)

    Carey, Vincent J; Wang, You-Gan

    2011-11-20

    We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright © 2011 John Wiley & Sons, Ltd.

  12. Evidence accumulation as a model for lexical selection.

    Science.gov (United States)

    Anders, R; Riès, S; van Maanen, L; Alario, F X

    2015-11-01

    We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Integrated model for supplier selection and performance evaluation

    Directory of Open Access Journals (Sweden)

    Borges de Araújo, Maria Creuza

    2015-08-01

    Full Text Available This paper puts forward a model for selecting suppliers and evaluating the performance of those already working with a company. A simulation was conducted in a food industry. This sector has high significance in the economy of Brazil. The model enables the phases of selecting and evaluating suppliers to be integrated. This is important so that a company can have partnerships with suppliers who are able to meet their needs. Additionally, a group method is used to enable managers who will be affected by this decision to take part in the selection stage. Finally, the classes resulting from the performance evaluation are shown to support the contractor in choosing the most appropriate relationship with its suppliers.

  14. Attention-based Memory Selection Recurrent Network for Language Modeling

    OpenAIRE

    Liu, Da-Rong; Chuang, Shun-Po; Lee, Hung-yi

    2016-01-01

    Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful long-term information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network (AMSRN), in which the model can review the information stored in the memory at each previous time ...

  15. The Selection of ARIMA Models with or without Regressors

    DEFF Research Database (Denmark)

    Johansen, Søren; Riani, Marco; Atkinson, Anthony C.

    We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum...

  16. Model selection for contingency tables with algebraic statistics

    NARCIS (Netherlands)

    Krampe, A.; Kuhnt, S.; Gibilisco, P.; Riccimagno, E.; Rogantin, M.P.; Wynn, H.P.

    2009-01-01

    Goodness-of-fit tests based on chi-square approximations are commonly used in the analysis of contingency tables. Results from algebraic statistics combined with MCMC methods provide alternatives to the chi-square approximation. However, within a model selection procedure usually a large number of

  17. Computationally efficient thermal-mechanical modelling of selective laser melting

    NARCIS (Netherlands)

    Yang, Y.; Ayas, C.; Brabazon, Dermot; Naher, Sumsun; Ul Ahad, Inam

    2017-01-01

    The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is

  18. Multivariate time series modeling of selected childhood diseases in ...

    African Journals Online (AJOL)

    This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...

  19. Rank-based model selection for multiple ions quantum tomography

    International Nuclear Information System (INIS)

    Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian

    2012-01-01

    The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ 2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements. (paper)

  20. Measures and limits of models of fixation selection.

    Directory of Open Access Journals (Sweden)

    Niklas Wilming

    Full Text Available Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure and the KL-divergence (a distance measure of probability distributions combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection. We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced.

  1. Generalized Selectivity Description for Polymeric Ion-Selective Electrodes Based on the Phase Boundary Potential Model.

    Science.gov (United States)

    Bakker, Eric

    2010-02-15

    A generalized description of the response behavior of potentiometric polymer membrane ion-selective electrodes is presented on the basis of ion-exchange equilibrium considerations at the sample-membrane interface. This paper includes and extends on previously reported theoretical advances in a more compact yet more comprehensive form. Specifically, the phase boundary potential model is used to derive the origin of the Nernstian response behavior in a single expression, which is valid for a membrane containing any charge type and complex stoichiometry of ionophore and ion-exchanger. This forms the basis for a generalized expression of the selectivity coefficient, which may be used for the selectivity optimization of ion-selective membranes containing electrically charged and neutral ionophores of any desired stoichiometry. It is shown to reduce to expressions published previously for specialized cases, and may be effectively applied to problems relevant in modern potentiometry. The treatment is extended to mixed ion solutions, offering a comprehensive yet formally compact derivation of the response behavior of ion-selective electrodes to a mixture of ions of any desired charge. It is compared to predictions by the less accurate Nicolsky-Eisenman equation. The influence of ion fluxes or any form of electrochemical excitation is not considered here, but may be readily incorporated if an ion-exchange equilibrium at the interface may be assumed in these cases.

  2. Occupational Accidents with Agricultural Machinery in Austria.

    Science.gov (United States)

    Kogler, Robert; Quendler, Elisabeth; Boxberger, Josef

    2016-01-01

    The number of recognized accidents with fatalities during agricultural and forestry work, despite better technology and coordinated prevention and trainings, is still very high in Austria. The accident scenarios in which people are injured are very different on farms. The common causes of accidents in agriculture and forestry are the loss of control of machine, means of transport or handling equipment, hand-held tool, and object or animal, followed by slipping, stumbling and falling, breakage, bursting, splitting, slipping, fall, and collapse of material agent. In the literature, a number of studies of general (machine- and animal-related accidents) and specific (machine-related accidents) agricultural and forestry accident situations can be found that refer to different databases. From the database Data of the Austrian Workers Compensation Board (AUVA) about occupational accidents with different agricultural machinery over the period 2008-2010 in Austria, main characteristics of the accident, the victim, and the employer as well as variables on causes and circumstances by frequency and contexts of parameters were statistically analyzed by employing the chi-square test and odds ratio. The aim of the study was to determine the information content and quality of the European Statistics on Accidents at Work (ESAW) variables to evaluate safety gaps and risks as well as the accidental man-machine interaction.

  3. The grouting handbook a step-by-step guide for foundation design and machinery installation

    CERN Document Server

    Harrison, Donald M

    2013-01-01

    Minimize loss of revenue and the downtime of critical assets by avoiding foundation cracking, poor bonds, and initial alignment changes. After their successful introduction as a maintenance material, machinery grouts are now being used for equipment placement in new construction. While certainly suitable for both markets and applications, a successful installation depends on proper grout selection, application, foundation preparation, and forming methods. Therefore, guidelines on their uses and limitations are needed for engineers and maintenance personnel. Based on 45 years of field experi

  4. Fisher-Wright model with deterministic seed bank and selection.

    Science.gov (United States)

    Koopmann, Bendix; Müller, Johannes; Tellier, Aurélien; Živković, Daniel

    2017-04-01

    Seed banks are common characteristics to many plant species, which allow storage of genetic diversity in the soil as dormant seeds for various periods of time. We investigate an above-ground population following a Fisher-Wright model with selection coupled with a deterministic seed bank assuming the length of the seed bank is kept constant and the number of seeds is large. To assess the combined impact of seed banks and selection on genetic diversity, we derive a general diffusion model. The applied techniques outline a path of approximating a stochastic delay differential equation by an appropriately rescaled stochastic differential equation. We compute the equilibrium solution of the site-frequency spectrum and derive the times to fixation of an allele with and without selection. Finally, it is demonstrated that seed banks enhance the effect of selection onto the site-frequency spectrum while slowing down the time until the mutation-selection equilibrium is reached. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  6. How Many Separable Sources? Model Selection In Independent Components Analysis

    Science.gov (United States)

    Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

    2015-01-01

    Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

  7. A model for the sustainable selection of building envelope assemblies

    Energy Technology Data Exchange (ETDEWEB)

    Huedo, Patricia, E-mail: huedo@uji.es [Universitat Jaume I (Spain); Mulet, Elena, E-mail: emulet@uji.es [Universitat Jaume I (Spain); López-Mesa, Belinda, E-mail: belinda@unizar.es [Universidad de Zaragoza (Spain)

    2016-02-15

    The aim of this article is to define an evaluation model for the environmental impacts of building envelopes to support planners in the early phases of materials selection. The model is intended to estimate environmental impacts for different combinations of building envelope assemblies based on scientifically recognised sustainability indicators. These indicators will increase the amount of information that existing catalogues show to support planners in the selection of building assemblies. To define the model, first the environmental indicators were selected based on the specific aims of the intended sustainability assessment. Then, a simplified LCA methodology was developed to estimate the impacts applicable to three types of dwellings considering different envelope assemblies, building orientations and climate zones. This methodology takes into account the manufacturing, installation, maintenance and use phases of the building. Finally, the model was validated and a matrix in Excel was created as implementation of the model. - Highlights: • Method to assess the envelope impacts based on a simplified LCA • To be used at an earlier phase than the existing methods in a simple way. • It assigns a score by means of known sustainability indicators. • It estimates data about the embodied and operating environmental impacts. • It compares the investment costs with the costs of the consumed energy.

  8. A model for the sustainable selection of building envelope assemblies

    International Nuclear Information System (INIS)

    Huedo, Patricia; Mulet, Elena; López-Mesa, Belinda

    2016-01-01

    The aim of this article is to define an evaluation model for the environmental impacts of building envelopes to support planners in the early phases of materials selection. The model is intended to estimate environmental impacts for different combinations of building envelope assemblies based on scientifically recognised sustainability indicators. These indicators will increase the amount of information that existing catalogues show to support planners in the selection of building assemblies. To define the model, first the environmental indicators were selected based on the specific aims of the intended sustainability assessment. Then, a simplified LCA methodology was developed to estimate the impacts applicable to three types of dwellings considering different envelope assemblies, building orientations and climate zones. This methodology takes into account the manufacturing, installation, maintenance and use phases of the building. Finally, the model was validated and a matrix in Excel was created as implementation of the model. - Highlights: • Method to assess the envelope impacts based on a simplified LCA • To be used at an earlier phase than the existing methods in a simple way. • It assigns a score by means of known sustainability indicators. • It estimates data about the embodied and operating environmental impacts. • It compares the investment costs with the costs of the consumed energy.

  9. On selection of optimal stochastic model for accelerated life testing

    International Nuclear Information System (INIS)

    Volf, P.; Timková, J.

    2014-01-01

    This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test

  10. Model building strategy for logistic regression: purposeful selection.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-03-01

    Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

  11. Statistical modelling in biostatistics and bioinformatics selected papers

    CERN Document Server

    Peng, Defen

    2014-01-01

    This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...

  12. Modeling and Solving the Liner Shipping Service Selection Problem

    DEFF Research Database (Denmark)

    Karsten, Christian Vad; Balakrishnan, Anant

    We address a tactical planning problem, the Liner Shipping Service Selection Problem (LSSSP), facing container shipping companies. Given estimated demand between various ports, the LSSSP entails selecting the best subset of non-simple cyclic sailing routes from a given pool of candidate routes...... to accurately model transshipment costs and incorporate routing policies such as maximum transit time, maritime cabotage rules, and operational alliances. Our hop-indexed arc flow model is smaller and easier to solve than path flow models. We outline a preprocessing procedure that exploits both the routing...... requirements and the hop limits to reduce problem size, and describe techniques to accelerate the solution procedure. We present computational results for realistic problem instances from the benchmark suite LINER-LIB....

  13. Variable Selection for Regression Models of Percentile Flows

    Science.gov (United States)

    Fouad, G.

    2017-12-01

    Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

  14. Numerical Model based Reliability Estimation of Selective Laser Melting Process

    DEFF Research Database (Denmark)

    Mohanty, Sankhya; Hattel, Jesper Henri

    2014-01-01

    Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....

  15. Modelling Technical and Economic Parameters in Selection of Manufacturing Devices

    Directory of Open Access Journals (Sweden)

    Naqib Daneshjo

    2017-11-01

    Full Text Available Sustainable science and technology development is also conditioned by continuous development of means of production which have a key role in structure of each production system. Mechanical nature of the means of production is complemented by controlling and electronic devices in context of intelligent industry. A selection of production machines for a technological process or technological project has so far been practically resolved, often only intuitively. With regard to increasing intelligence, the number of variable parameters that have to be considered when choosing a production device is also increasing. It is necessary to use computing techniques and decision making methods according to heuristic methods and more precise methodological procedures during the selection. The authors present an innovative model for optimization of technical and economic parameters in the selection of manufacturing devices for industry 4.0.

  16. Selection of Models for Ingestion Pathway and Relocation Radii Determination

    International Nuclear Information System (INIS)

    Blanchard, A.

    1998-01-01

    The distance at which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models were considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities

  17. Selection of Models for Ingestion Pathway and Relocation

    International Nuclear Information System (INIS)

    Blanchard, A.; Thompson, J.M.

    1998-01-01

    The area in which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models are considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities. The most recent Food and Drug Administration Derived Intervention Levels (August 1998) are adopted as evaluation guidelines for ingestion pathways

  18. Selection of Models for Ingestion Pathway and Relocation

    International Nuclear Information System (INIS)

    Blanchard, A.; Thompson, J.M.

    1999-01-01

    The area in which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models are considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities. The most recent Food and Drug Administration Derived Intervention Levels (August 1998) are adopted as evaluation guidelines for ingestion pathways

  19. Predicting artificailly drained areas by means of selective model ensemble

    DEFF Research Database (Denmark)

    Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

    . The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

  20. ASYMMETRIC PRICE TRANSMISSION MODELING: THE IMPORTANCE OF MODEL COMPLEXITY AND THE PERFORMANCE OF THE SELECTION CRITERIA

    Directory of Open Access Journals (Sweden)

    Henry de-Graft Acquah

    2013-01-01

    Full Text Available Information Criteria provides an attractive basis for selecting the best model from a set of competing asymmetric price transmission models or theories. However, little is understood about the sensitivity of the model selection methods to model complexity. This study therefore fits competing asymmetric price transmission models that differ in complexity to simulated data and evaluates the ability of the model selection methods to recover the true model. The results of Monte Carlo experimentation suggest that in general BIC, CAIC and DIC were superior to AIC when the true data generating process was the standard error correction model, whereas AIC was more successful when the true model was the complex error correction model. It is also shown that the model selection methods performed better in large samples for a complex asymmetric data generating process than with a standard asymmetric data generating process. Except for complex models, AIC's performance did not make substantial gains in recovery rates as sample size increased. The research findings demonstrate the influence of model complexity in asymmetric price transmission model comparison and selection.

  1. An Improved Nested Sampling Algorithm for Model Selection and Assessment

    Science.gov (United States)

    Zeng, X.; Ye, M.; Wu, J.; WANG, D.

    2017-12-01

    Multimodel strategy is a general approach for treating model structure uncertainty in recent researches. The unknown groundwater system is represented by several plausible conceptual models. Each alternative conceptual model is attached with a weight which represents the possibility of this model. In Bayesian framework, the posterior model weight is computed as the product of model prior weight and marginal likelihood (or termed as model evidence). As a result, estimating marginal likelihoods is crucial for reliable model selection and assessment in multimodel analysis. Nested sampling estimator (NSE) is a new proposed algorithm for marginal likelihood estimation. The implementation of NSE comprises searching the parameters' space from low likelihood area to high likelihood area gradually, and this evolution is finished iteratively via local sampling procedure. Thus, the efficiency of NSE is dominated by the strength of local sampling procedure. Currently, Metropolis-Hasting (M-H) algorithm and its variants are often used for local sampling in NSE. However, M-H is not an efficient sampling algorithm for high-dimensional or complex likelihood function. For improving the performance of NSE, it could be feasible to integrate more efficient and elaborated sampling algorithm - DREAMzs into the local sampling. In addition, in order to overcome the computation burden problem of large quantity of repeating model executions in marginal likelihood estimation, an adaptive sparse grid stochastic collocation method is used to build the surrogates for original groundwater model.

  2. Stochastic isotropic hyperelastic materials: constitutive calibration and model selection

    Science.gov (United States)

    Mihai, L. Angela; Woolley, Thomas E.; Goriely, Alain

    2018-03-01

    Biological and synthetic materials often exhibit intrinsic variability in their elastic responses under large strains, owing to microstructural inhomogeneity or when elastic data are extracted from viscoelastic mechanical tests. For these materials, although hyperelastic models calibrated to mean data are useful, stochastic representations accounting also for data dispersion carry extra information about the variability of material properties found in practical applications. We combine finite elasticity and information theories to construct homogeneous isotropic hyperelastic models with random field parameters calibrated to discrete mean values and standard deviations of either the stress-strain function or the nonlinear shear modulus, which is a function of the deformation, estimated from experimental tests. These quantities can take on different values, corresponding to possible outcomes of the experiments. As multiple models can be derived that adequately represent the observed phenomena, we apply Occam's razor by providing an explicit criterion for model selection based on Bayesian statistics. We then employ this criterion to select a model among competing models calibrated to experimental data for rubber and brain tissue under single or multiaxial loads.

  3. Modeling selective pressures on phytoplankton in the global ocean.

    Directory of Open Access Journals (Sweden)

    Jason G Bragg

    Full Text Available Our view of marine microbes is transforming, as culture-independent methods facilitate rapid characterization of microbial diversity. It is difficult to assimilate this information into our understanding of marine microbe ecology and evolution, because their distributions, traits, and genomes are shaped by forces that are complex and dynamic. Here we incorporate diverse forces--physical, biogeochemical, ecological, and mutational--into a global ocean model to study selective pressures on a simple trait in a widely distributed lineage of picophytoplankton: the nitrogen use abilities of Synechococcus and Prochlorococcus cyanobacteria. Some Prochlorococcus ecotypes have lost the ability to use nitrate, whereas their close relatives, marine Synechococcus, typically retain it. We impose mutations for the loss of nitrogen use abilities in modeled picophytoplankton, and ask: in which parts of the ocean are mutants most disadvantaged by losing the ability to use nitrate, and in which parts are they least disadvantaged? Our model predicts that this selective disadvantage is smallest for picophytoplankton that live in tropical regions where Prochlorococcus are abundant in the real ocean. Conversely, the selective disadvantage of losing the ability to use nitrate is larger for modeled picophytoplankton that live at higher latitudes, where Synechococcus are abundant. In regions where we expect Prochlorococcus and Synechococcus populations to cycle seasonally in the real ocean, we find that model ecotypes with seasonal population dynamics similar to Prochlorococcus are less disadvantaged by losing the ability to use nitrate than model ecotypes with seasonal population dynamics similar to Synechococcus. The model predictions for the selective advantage associated with nitrate use are broadly consistent with the distribution of this ability among marine picocyanobacteria, and at finer scales, can provide insights into interactions between temporally varying

  4. Modeling selective pressures on phytoplankton in the global ocean.

    Science.gov (United States)

    Bragg, Jason G; Dutkiewicz, Stephanie; Jahn, Oliver; Follows, Michael J; Chisholm, Sallie W

    2010-03-10

    Our view of marine microbes is transforming, as culture-independent methods facilitate rapid characterization of microbial diversity. It is difficult to assimilate this information into our understanding of marine microbe ecology and evolution, because their distributions, traits, and genomes are shaped by forces that are complex and dynamic. Here we incorporate diverse forces--physical, biogeochemical, ecological, and mutational--into a global ocean model to study selective pressures on a simple trait in a widely distributed lineage of picophytoplankton: the nitrogen use abilities of Synechococcus and Prochlorococcus cyanobacteria. Some Prochlorococcus ecotypes have lost the ability to use nitrate, whereas their close relatives, marine Synechococcus, typically retain it. We impose mutations for the loss of nitrogen use abilities in modeled picophytoplankton, and ask: in which parts of the ocean are mutants most disadvantaged by losing the ability to use nitrate, and in which parts are they least disadvantaged? Our model predicts that this selective disadvantage is smallest for picophytoplankton that live in tropical regions where Prochlorococcus are abundant in the real ocean. Conversely, the selective disadvantage of losing the ability to use nitrate is larger for modeled picophytoplankton that live at higher latitudes, where Synechococcus are abundant. In regions where we expect Prochlorococcus and Synechococcus populations to cycle seasonally in the real ocean, we find that model ecotypes with seasonal population dynamics similar to Prochlorococcus are less disadvantaged by losing the ability to use nitrate than model ecotypes with seasonal population dynamics similar to Synechococcus. The model predictions for the selective advantage associated with nitrate use are broadly consistent with the distribution of this ability among marine picocyanobacteria, and at finer scales, can provide insights into interactions between temporally varying ocean processes and

  5. Automation facilities for agricultural machinery control

    Directory of Open Access Journals (Sweden)

    A. Yu. Izmaylov

    2017-01-01

    Full Text Available The possibility of use of the automation equipment for agricultural machinery control is investigated. The authors proposed solutions on creation of the centralized unified automated information system for mobile aggregates management. In accordance with the modern requirements this system should be open, integrated into the general schema of agricultural enterprise control. Standard hardware, software and communicative features should be realized in tasks of monitoring and control. Therefore the schema should be get with use the unified modules and Russian standards. The complex multivariate unified automated control system for different objects of agricultural purpose based on block and modular creation should correspond to the following principles: high reliability, simplicity of service, low expenses in case of operation, the short payback period connected to increase in productivity, the reduced losses when harvesting, postharvest processing and storage, the improved energetic indices. Technological processes control in agricultural production is exercised generally with feedback. The example without feedback is program control by temperature in storage in case of the cooling mode. Feedback at technological processes control in agricultural production allows to optimally solve a problem of rational distribution of functions in man-distributed systems and forming the intelligent ergonomic interfaces, consistent with professional perceptions of decision-makers. The negative feedback created by the control unit allows to support automatically a quality index of technological process at the set level. The quantitative analysis of a production situation base itself upon deeply formalized basis of computer facilities that promotes making of the optimal solution. Information automated control system introduction increases labor productivity by 40 percent, reduces energetic costs by 25 percent. Improvement of quality of the executed technological

  6. Modeling selective attention using a neuromorphic analog VLSI device.

    Science.gov (United States)

    Indiveri, G

    2000-12-01

    Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.

  7. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

    Science.gov (United States)

    Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K

    2017-11-01

    Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. A Model of Social Selection and Successful Altruism

    Science.gov (United States)

    1989-10-07

    D., The evolution of social behavior. Annual Reviews of Ecological Systems, 5:325-383 (1974). 2. Dawkins , R., The selfish gene . Oxford: Oxford...alive and well. it will be important to re- examine this striking historical experience,-not in terms o, oversimplified models of the " selfish gene ," but...Darwinian Analysis The acceptance by many modern geneticists of the axiom that the basic unit of selection Is the " selfish gene " quickly led to the

  9. Pareto-Optimal Model Selection via SPRINT-Race.

    Science.gov (United States)

    Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2018-02-01

    In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

  10. Establishment of selected acute pulmonary thromboembolism model in experimental sheep

    International Nuclear Information System (INIS)

    Fan Jihai; Gu Xiulian; Chao Shengwu; Zhang Peng; Fan Ruilin; Wang Li'na; Wang Lulu; Wang Ling; Li Bo; Chen Taotao

    2010-01-01

    Objective: To establish a selected acute pulmonary thromboembolism model in experimental sheep suitable for animal experiment. Methods: By using Seldinger's technique the catheter sheath was placed in both the femoral vein and femoral artery in ten sheep. Under C-arm DSA guidance the catheter was inserted through the catheter sheath into the pulmonary artery. Via the catheter appropriate amount of sheep autologous blood clots was injected into the selected pulmonary arteries. The selected acute pulmonary thromboembolism model was thus established. Pulmonary angiography was performed to check the results. The pulmonary arterial pressure, femoral artery pressure,heart rates and partial pressure of oxygen in arterial blood (PaO 2 ) were determined both before and after the treatment. The above parameters obtained after the procedure were compared with the recorded parameters measured before the procedure, and the sheep model quality was evaluated. Results: The baseline of pulmonary arterial pressure was (27.30 ± 9.58) mmHg,femoral artery pressure was (126.4 ± 13.72) mmHg, heart rate was (103 ± 15) bpm and PaO 2 was (87.7 ± 12.04) mmHg. Sixty minutes after the injection of (30 ± 5) ml thrombotic agglomerates, the pulmonary arterial pressures rose to (52 ± 49) mmHg, femoral artery pressures dropped to (100 ± 21) mmHg. The heart rates went up to (150 ± 26) bpm. The PaO 2 fell to (25.3 ± 11.2) mmHg. After the procedure the above parameters were significantly different from that measured before the procedure in all ten animals (P < 0.01). The pulmonary arteriography clearly demonstrated that the selected pulmonary arteries were successfully embolized. Conclusion: The anatomy of sheep's femoral veins,vena cava system, pulmonary artery and right heart system are suitable for the establishment of the catheter passage, for this reason, selected acute pulmonary thromboembolism model can be easily created in experimental sheep. The technique is feasible and the model

  11. Selection of key terrain attributes for SOC model

    DEFF Research Database (Denmark)

    Greve, Mogens Humlekrog; Adhikari, Kabindra; Chellasamy, Menaka

    As an important component of the global carbon pool, soil organic carbon (SOC) plays an important role in the global carbon cycle. SOC pool is the basic information to carry out global warming research, and needs to sustainable use of land resources. Digital terrain attributes are often use...... was selected, total 2,514,820 data mining models were constructed by 71 differences grid from 12m to 2304m and 22 attributes, 21 attributes derived by DTM and the original elevation. Relative importance and usage of each attributes in every model were calculated. Comprehensive impact rates of each attribute...

  12. A decision model for energy resource selection in China

    International Nuclear Information System (INIS)

    Wang Bing; Kocaoglu, Dundar F.; Daim, Tugrul U.; Yang Jiting

    2010-01-01

    This paper evaluates coal, petroleum, natural gas, nuclear energy and renewable energy resources as energy alternatives for China through use of a hierarchical decision model. The results indicate that although coal is still the major preferred energy alternative, it is followed closely by renewable energy. The sensitivity analysis indicates that the most critical criterion for energy selection is the current energy infrastructure. A hierarchical decision model is used, and expert judgments are quantified, to evaluate the alternatives. Criteria used for the evaluations are availability, current energy infrastructure, price, safety, environmental impacts and social impacts.

  13. Covariate selection for the semiparametric additive risk model

    DEFF Research Database (Denmark)

    Martinussen, Torben; Scheike, Thomas

    2009-01-01

    This paper considers covariate selection for the additive hazards model. This model is particularly simple to study theoretically and its practical implementation has several major advantages to the similar methodology for the proportional hazards model. One complication compared...... and study their large sample properties for the situation where the number of covariates p is smaller than the number of observations. We also show that the adaptive Lasso has the oracle property. In many practical situations, it is more relevant to tackle the situation with large p compared with the number...... of observations. We do this by studying the properties of the so-called Dantzig selector in the setting of the additive risk model. Specifically, we establish a bound on how close the solution is to a true sparse signal in the case where the number of covariates is large. In a simulation study, we also compare...

  14. Optimal foraging in marine ecosystem models: selectivity, profitability and switching

    DEFF Research Database (Denmark)

    Visser, Andre W.; Fiksen, Ø.

    2013-01-01

    ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...

  15. Selection of productivity improvement techniques via mathematical modeling

    Directory of Open Access Journals (Sweden)

    Mahassan M. Khater

    2011-07-01

    Full Text Available This paper presents a new mathematical model to select an optimal combination of productivity improvement techniques. The proposed model of this paper considers four-stage cycle productivity and the productivity is assumed to be a linear function of fifty four improvement techniques. The proposed model of this paper is implemented for a real-world case study of manufacturing plant. The resulted problem is formulated as a mixed integer programming which can be solved for optimality using traditional methods. The preliminary results of the implementation of the proposed model of this paper indicate that the productivity can be improved through a change on equipments and it can be easily applied for both manufacturing and service industries.

  16. An Introduction to Model Selection: Tools and Algorithms

    Directory of Open Access Journals (Sweden)

    Sébastien Hélie

    2006-03-01

    Full Text Available Model selection is a complicated matter in science, and psychology is no exception. In particular, the high variance in the object of study (i.e., humans prevents the use of Popper’s falsification principle (which is the norm in other sciences. Therefore, the desirability of quantitative psychological models must be assessed by measuring the capacity of the model to fit empirical data. In the present paper, an error measure (likelihood, as well as five methods to compare model fits (the likelihood ratio test, Akaike’s information criterion, the Bayesian information criterion, bootstrapping and cross-validation, are presented. The use of each method is illustrated by an example, and the advantages and weaknesses of each method are also discussed.

  17. The kinematics of machinery outlines of a theory of machines

    CERN Document Server

    Reuleaux, Franz

    2012-01-01

    A classic on the kinematics of machinery, this volume was written by the Father of Kinematics. Reuleaux writes with authority and precision, developing the subject from its fundamentals. 450 figures. 1876 edition.

  18. Targeting Cell Polarity Machinery to Exhaust Breast Cancer Stem Cells

    Science.gov (United States)

    2017-10-01

    AWARD NUMBER: W81XWH-15-1-0644 TITLE: Targeting Cell Polarity Machinery to Exhaust Breast Cancer Stem Cells PRINCIPAL INVESTIGATOR: Chun-Ju...Targeting Cell Polarity Machinery to Exhaust Breast Cancer Stem Cells 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-15-1-0644 5c. PROGRAM ELEMENT...Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Cancer stem cells (CSCs), a cell population with acquired perpetuating self-renewal properties which

  19. Intelligence and the machinery of government: conceptualizing the intelligence community

    OpenAIRE

    Davies, PHJ

    2010-01-01

    This article argues that the failure to address intelligence agencies as public organizations part and parcel with the overt machinery of government constitutes a significant lacuna both in the specialist study of intelligence and the broader discipline of public administration studies. The role and status of intelligence institutions as aspects of the machinery of central government is examined, along with the prospects of certain key paradigms in the field for understanding those institutio...

  20. The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection

    Science.gov (United States)

    Whittaker, Tiffany A.; Chang, Wanchen; Dodd, Barbara G.

    2013-01-01

    Whittaker, Chang, and Dodd compared the performance of model selection criteria when selecting among mixed-format IRT models and found that the criteria did not perform adequately when selecting the more parameterized models. It was suggested by M. S. Johnson that the problems when selecting the more parameterized models may be because of the low…

  1. Wind scatterometry with improved ambiguity selection and rain modeling

    Science.gov (United States)

    Draper, David Willis

    Although generally accurate, the quality of SeaWinds on QuikSCAT scatterometer ocean vector winds is compromised by certain natural phenomena and retrieval algorithm limitations. This dissertation addresses three main contributors to scatterometer estimate error: poor ambiguity selection, estimate uncertainty at low wind speeds, and rain corruption. A quality assurance (QA) analysis performed on SeaWinds data suggests that about 5% of SeaWinds data contain ambiguity selection errors and that scatterometer estimation error is correlated with low wind speeds and rain events. Ambiguity selection errors are partly due to the "nudging" step (initialization from outside data). A sophisticated new non-nudging ambiguity selection approach produces generally more consistent wind than the nudging method in moderate wind conditions. The non-nudging method selects 93% of the same ambiguities as the nudged data, validating both techniques, and indicating that ambiguity selection can be accomplished without nudging. Variability at low wind speeds is analyzed using tower-mounted scatterometer data. According to theory, below a threshold wind speed, the wind fails to generate the surface roughness necessary for wind measurement. A simple analysis suggests the existence of the threshold in much of the tower-mounted scatterometer data. However, the backscatter does not "go to zero" beneath the threshold in an uncontrolled environment as theory suggests, but rather has a mean drop and higher variability below the threshold. Rain is the largest weather-related contributor to scatterometer error, affecting approximately 4% to 10% of SeaWinds data. A simple model formed via comparison of co-located TRMM PR and SeaWinds measurements characterizes the average effect of rain on SeaWinds backscatter. The model is generally accurate to within 3 dB over the tropics. The rain/wind backscatter model is used to simultaneously retrieve wind and rain from SeaWinds measurements. The simultaneous

  2. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  3. Multilevel selection in a resource-based model

    Science.gov (United States)

    Ferreira, Fernando Fagundes; Campos, Paulo R. A.

    2013-07-01

    In the present work we investigate the emergence of cooperation in a multilevel selection model that assumes limiting resources. Following the work by R. J. Requejo and J. Camacho [Phys. Rev. Lett.0031-900710.1103/PhysRevLett.108.038701 108, 038701 (2012)], the interaction among individuals is initially ruled by a prisoner's dilemma (PD) game. The payoff matrix may change, influenced by the resource availability, and hence may also evolve to a non-PD game. Furthermore, one assumes that the population is divided into groups, whose local dynamics is driven by the payoff matrix, whereas an intergroup competition results from the nonuniformity of the growth rate of groups. We study the probability that a single cooperator can invade and establish in a population initially dominated by defectors. Cooperation is strongly favored when group sizes are small. We observe the existence of a critical group size beyond which cooperation becomes counterselected. Although the critical size depends on the parameters of the model, it is seen that a saturation value for the critical group size is achieved. The results conform to the thought that the evolutionary history of life repeatedly involved transitions from smaller selective units to larger selective units.

  4. Neural networks for the monitoring of rotating machinery

    International Nuclear Information System (INIS)

    Alguindigue, I.E.; Loskiewicz-Buczak

    1991-01-01

    Vibration monitoring of components in engineering systems and plants involves the collection of vibration data and detailed analysis to detect features which reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper describes a methodology for the automation of some of the activities related to motion and vibration monitoring in these systems. The technique involves training a neural network to model the inter- relationship between signals from two related sensors mounted on an engineering system or component at a time when it is known to be operating properly. Then one signal (or its characteristics) is put into the neural network model to predict the second signal (or its characteristics). This predicted signal is continuously compared with the actual signal A deviation between the predicted and actual signal indicates a changing relationship, usually failure of the component or system. This deviation may be quantified and provides meaningful information about the degree of degradation and deterioration of the component

  5. METHODS OF SELECTING THE EFFECTIVE MODELS OF BUILDINGS REPROFILING PROJECTS

    Directory of Open Access Journals (Sweden)

    Александр Иванович МЕНЕЙЛЮК

    2016-02-01

    Full Text Available The article highlights the important task of project management in reprofiling of buildings. It is expedient to pay attention to selecting effective engineering solutions to reduce the duration and cost reduction at the project management in the construction industry. This article presents a methodology for the selection of efficient organizational and technical solutions for the reconstruction of buildings reprofiling. The method is based on a compilation of project variants in the program Microsoft Project and experimental statistical analysis using the program COMPEX. The introduction of this technique in the realigning of buildings allows choosing efficient models of projects, depending on the given constraints. Also, this technique can be used for various construction projects.

  6. Applying a Hybrid MCDM Model for Six Sigma Project Selection

    Directory of Open Access Journals (Sweden)

    Fu-Kwun Wang

    2014-01-01

    Full Text Available Six Sigma is a project-driven methodology; the projects that provide the maximum financial benefits and other impacts to the organization must be prioritized. Project selection (PS is a type of multiple criteria decision making (MCDM problem. In this study, we present a hybrid MCDM model combining the decision-making trial and evaluation laboratory (DEMATEL technique, analytic network process (ANP, and the VIKOR method to evaluate and improve Six Sigma projects for reducing performance gaps in each criterion and dimension. We consider the film printing industry of Taiwan as an empirical case. The results show that our study not only can use the best project selection, but can also be used to analyze the gaps between existing performance values and aspiration levels for improving the gaps in each dimension and criterion based on the influential network relation map.

  7. A Reliability Based Model for Wind Turbine Selection

    Directory of Open Access Journals (Sweden)

    A.K. Rajeevan

    2013-06-01

    Full Text Available A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

  8. Automation of Endmember Pixel Selection in SEBAL/METRIC Model

    Science.gov (United States)

    Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.

    2015-12-01

    The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.

  9. Fuzzy Goal Programming Approach in Selective Maintenance Reliability Model

    Directory of Open Access Journals (Sweden)

    Neha Gupta

    2013-12-01

    Full Text Available 800x600 In the present paper, we have considered the allocation problem of repairable components for a parallel-series system as a multi-objective optimization problem and have discussed two different models. In first model the reliability of subsystems are considered as different objectives. In second model the cost and time spent on repairing the components are considered as two different objectives. These two models is formulated as multi-objective Nonlinear Programming Problem (MONLPP and a Fuzzy goal programming method is used to work out the compromise allocation in multi-objective selective maintenance reliability model in which we define the membership functions of each objective function and then transform membership functions into equivalent linear membership functions by first order Taylor series and finally by forming a fuzzy goal programming model obtain a desired compromise allocation of maintenance components. A numerical example is also worked out to illustrate the computational details of the method.  Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4

  10. Development of Solar Drying Model for Selected Cambodian Fish Species

    Science.gov (United States)

    Hubackova, Anna; Kucerova, Iva; Chrun, Rithy; Chaloupkova, Petra; Banout, Jan

    2014-01-01

    A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h−1. Based on coefficient of determination (R 2), chi-square (χ 2) test, and root-mean-square error (RMSE), the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing. PMID:25250381

  11. Development of Solar Drying Model for Selected Cambodian Fish Species

    Directory of Open Access Journals (Sweden)

    Anna Hubackova

    2014-01-01

    Full Text Available A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h−1. Based on coefficient of determination (R2, chi-square (χ2 test, and root-mean-square error (RMSE, the most suitable models describing natural convection solar drying kinetics were Logarithmic model, Diffusion approximate model, and Two-term model for climbing perch and Nile tilapia, swamp eel and walking catfish and Channa fish, respectively. In case of electric oven drying, the Modified Page 1 model shows the best results for all investigated fish species except Channa fish where the two-term model is the best one. Sensory evaluation shows that most preferable fish is climbing perch, followed by Nile tilapia and walking catfish. This study brings new knowledge about drying kinetics of fresh water fish species in Cambodia and confirms the solar drying as acceptable technology for fish processing.

  12. Continuum model for chiral induced spin selectivity in helical molecules

    Energy Technology Data Exchange (ETDEWEB)

    Medina, Ernesto [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France); Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); González-Arraga, Luis A. [IMDEA Nanoscience, Cantoblanco, 28049 Madrid (Spain); Finkelstein-Shapiro, Daniel; Mujica, Vladimiro [Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); Berche, Bertrand [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France)

    2015-05-21

    A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.

  13. Selection of models to calculate the LLW source term

    International Nuclear Information System (INIS)

    Sullivan, T.M.

    1991-10-01

    Performance assessment of a LLW disposal facility begins with an estimation of the rate at which radionuclides migrate out of the facility (i.e., the source term). The focus of this work is to develop a methodology for calculating the source term. In general, the source term is influenced by the radionuclide inventory, the wasteforms and containers used to dispose of the inventory, and the physical processes that lead to release from the facility (fluid flow, container degradation, wasteform leaching, and radionuclide transport). In turn, many of these physical processes are influenced by the design of the disposal facility (e.g., infiltration of water). The complexity of the problem and the absence of appropriate data prevent development of an entirely mechanistic representation of radionuclide release from a disposal facility. Typically, a number of assumptions, based on knowledge of the disposal system, are used to simplify the problem. This document provides a brief overview of disposal practices and reviews existing source term models as background for selecting appropriate models for estimating the source term. The selection rationale and the mathematical details of the models are presented. Finally, guidance is presented for combining the inventory data with appropriate mechanisms describing release from the disposal facility. 44 refs., 6 figs., 1 tab

  14. Selection Strategies for Social Influence in the Threshold Model

    Science.gov (United States)

    Karampourniotis, Panagiotis; Szymanski, Boleslaw; Korniss, Gyorgy

    The ubiquity of online social networks makes the study of social influence extremely significant for its applications to marketing, politics and security. Maximizing the spread of influence by strategically selecting nodes as initiators of a new opinion or trend is a challenging problem. We study the performance of various strategies for selection of large fractions of initiators on a classical social influence model, the Threshold model (TM). Under the TM, a node adopts a new opinion only when the fraction of its first neighbors possessing that opinion exceeds a pre-assigned threshold. The strategies we study are of two kinds: strategies based solely on the initial network structure (Degree-rank, Dominating Sets, PageRank etc.) and strategies that take into account the change of the states of the nodes during the evolution of the cascade, e.g. the greedy algorithm. We find that the performance of these strategies depends largely on both the network structure properties, e.g. the assortativity, and the distribution of the thresholds assigned to the nodes. We conclude that the optimal strategy needs to combine the network specifics and the model specific parameters to identify the most influential spreaders. Supported in part by ARL NS-CTA, ARO, and ONR.

  15. Research on a Rotating Machinery Fault Prognosis Method Using Three-Dimensional Spatial Representations

    Directory of Open Access Journals (Sweden)

    Xiaoni Dong

    2016-01-01

    Full Text Available Process models and parameters are two critical steps for fault prognosis in the operation of rotating machinery. Due to the requirement for a short and rapid response, it is important to study robust sensor data representation schemes. However, the conventional holospectrum defined by one-dimensional or two-dimensional methods does not sufficiently present this information in both the frequency and time domains. To supply a complete holospectrum model, a new three-dimensional spatial representation method is proposed. This method integrates improved three-dimensional (3D holospectra and 3D filtered orbits, leading to the integration of radial and axial vibration features in one bearing section. The results from simulation and experimental analysis on a complex compressor show that the proposed method can present the real operational status and clearly reveal early faults, thus demonstrating great potential for condition-based maintenance prediction in industrial machinery.

  16. A Dual-Stage Two-Phase Model of Selective Attention

    Science.gov (United States)

    Hubner, Ronald; Steinhauser, Marco; Lehle, Carola

    2010-01-01

    The dual-stage two-phase (DSTP) model is introduced as a formal and general model of selective attention that includes both an early and a late stage of stimulus selection. Whereas at the early stage information is selected by perceptual filters whose selectivity is relatively limited, at the late stage stimuli are selected more efficiently on a…

  17. Estimation and variable selection for generalized additive partial linear models

    KAUST Repository

    Wang, Li

    2011-08-01

    We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.

  18. Quantitative modeling of selective lysosomal targeting for drug design

    DEFF Research Database (Denmark)

    Trapp, Stefan; Rosania, G.; Horobin, R.W.

    2008-01-01

    log K ow. These findings were validated with experimental results and by a comparison to the properties of antimalarial drugs in clinical use. For ten active compounds, nine were predicted to accumulate to a greater extent in lysosomes than in other organelles, six of these were in the optimum range...... predicted by the model and three were close. Five of the antimalarial drugs were lipophilic weak dibasic compounds. The predicted optimum properties for a selective accumulation of weak bivalent bases in lysosomes are consistent with experimental values and are more accurate than any prior calculation...

  19. FOREWORD: The XXV IAHR Symposium on Hydraulic Machinery and Systems marks half a century tradition

    Science.gov (United States)

    Susan-Resiga, Romeo

    2010-05-01

    results in rising global temperatures and dramatic changes in climate which may also result in flooding in parts of our globe. Energy conservation together with replacement of coal and oil-fired power plants are, therefore, needed. The development and installation of more efficient hydroelectric power plants which work hand in hand with water storage and flood protection is part of this strategy. Waterpower is the most significant 'renewable resource'. The goals of this IAHR - Committee on Hydraulic Machinery and Systems are to improve the value of hydraulic machinery to the end user and to society and to improve society's understanding and appreciation of that value. The series of IAHR Symposia on Hydraulic Machinery and Cavitation started with the 1st edition in Nice in 1960 in France. Within the past decade, all the symposia were focused on an extended portfolio of topics under the name of 'Hydraulic Machinery and Systems', such as the 20th edition in 2000, Charlotte, USA, the 21st in 2002, Lausanne, Switzerland, the 22nd in 2004, Stockholm, Sweden, the 23rd in 2006, Yokohama, Japan, and the 24th in 2008, Foz do Iguassu, Brasil. The 25th IAHR Symposium on Hydraulic Machinery and Systems brings together more than 150 scientists and researchers from 24 countries, affiliated with universities , technology centres and industry to debate topics related to advanced technologies for hydraulic machinery and systems, which will enhance the sustainable development of water resources and hydropower production. The Scientific Committee has selected 118 papers, out of 238 abstracts submitted, on the following topics: (i) Hydraulic Turbines and Pumps, (ii) Sustainable hydropower, (iii) Hydraulic Systems, (iv) Advances in Computational and Experimental Techniques, (v) Innovative Technology, to be presented at the symposium and to be included in the proceedings. All papers published in this Volume 12 of IOP Conference Series: Earth and Environmental Science have been peer reviewed

  20. A Neuronal Network Model for Pitch Selectivity and Representation.

    Science.gov (United States)

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among convergent auditory nerve fibers across frequency channels. Their selectivity for only very fast rising slopes of convergent input enables these slope-detectors to distinguish the most prominent coincidences in multi-peaked input time courses. Pitch can then be estimated from the first-order interspike intervals of the slope-detectors. The regular firing pattern of the slope-detector neurons are similar for sounds sharing the same pitch despite the distinct timbres. The decoded pitch strengths also correlate well with the salience of pitch perception as reported by human listeners. Therefore, our model can serve as a neural representation for pitch. Our model performs successfully in estimating the pitch of missing fundamental complexes and reproducing the pitch variation with respect to the frequency shift of inharmonic complexes. It also accounts for the phase sensitivity of pitch perception in the cases of Schroeder phase, alternating phase and random phase relationships. Moreover, our model can also be applied to stochastic sound stimuli, iterated-ripple-noise, and account for their multiple pitch perceptions.

  1. On the selection of ordinary differential equation models with application to predator-prey dynamical models.

    Science.gov (United States)

    Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

    2015-03-01

    We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models. © 2014, The International Biometric Society.

  2. 46 CFR 91.15-1 - Standards in inspection of hulls, boilers, and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Standards in inspection of hulls, boilers, and machinery... hulls, boilers, and machinery. In the inspection of hulls, boilers, and machinery of vessels, the..., respecting material and inspection of hulls, boilers, and machinery, and the certificate of classification...

  3. 46 CFR 189.15-1 - Standards in inspection of hulls, boilers, and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Standards in inspection of hulls, boilers, and machinery... inspection of hulls, boilers, and machinery. In the inspection of hulls, boilers, and machinery of vessels... chapter, respecting material and construction of hulls, boilers, and machinery, and certificate of...

  4. 46 CFR 71.15-1 - Standards in inspection of hulls, boilers, and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 3 2010-10-01 2010-10-01 false Standards in inspection of hulls, boilers, and machinery..., boilers, and machinery. In the inspection of hulls, boilers, and machinery of vessels, the standards... and inspection of hulls, boilers, and machinery, and the certificate of classification referring...

  5. Acute leukemia classification by ensemble particle swarm model selection.

    Science.gov (United States)

    Escalante, Hugo Jair; Montes-y-Gómez, Manuel; González, Jesús A; Gómez-Gil, Pilar; Altamirano, Leopoldo; Reyes, Carlos A; Reta, Carolina; Rosales, Alejandro

    2012-07-01

    Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant

  6. Implementation competences as an attribute of executive employees of the flexible organisation – an attempt of their assessment among manufacturers of the agricultural machinery sector

    Directory of Open Access Journals (Sweden)

    Nogalski Bogdan

    2016-12-01

    Full Text Available Based on theoretical knowledge, own professional experience and conducted research, according to the authors, the paper’s objective is to develop and empirically verify the theoretical model of implementation competences of the executive employees of manufacturing companies of the agricultural machinery sector. The main objective achievement required to formulate and reach partial objectives, which include: a discussion and organisation of terminological issues in terms of understanding the term of implementation competences, b development of a general model of the executive employees’ implementation competences, which is a sign of knowledge, skills, personality features, attitudes and values, c empirical verification of the theoretical model; prioritisation of individual implementation competences in the assessment of executive employees or owners of selected companies and determination of competence weaknesses, which are characteristic of the executive personnel of these companies.

  7. Radial Domany-Kinzel models with mutation and selection

    Science.gov (United States)

    Lavrentovich, Maxim O.; Korolev, Kirill S.; Nelson, David R.

    2013-01-01

    We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R0 expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t*=R0/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island.

  8. A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Hao; Ping, Xueliang; Cao, Yi; Lie, Ke [Jiangnan University, Wuxi (China); Chen, Peng [Mie University, Mie (Japan); Wang, Huaqing [Beijing University, Beijing (China)

    2014-04-15

    This study proposes a novel intelligent fault diagnosis method for rotating machinery using ant colony optimization (ACO) and possibility theory. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using principal component analysis (PCA) is proposed for detecting and distinguishing faults in rotating machinery. By using ACO clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. A fuzzy diagnosis method using sequential inference and possibility theory is also proposed, by which the conditions of the machinery can be identified sequentially. Lastly, the proposed method is compared with a conventional neural networks (NN) method. Practical examples of diagnosis for a V-belt driving equipment used in a centrifugal fan are provided to verify the effectiveness of the proposed method. The results verify that the faults that often occur in V-belt driving equipment, such as a pulley defect state, a belt defect state and a belt looseness state, are effectively identified by the proposed method, while these faults are difficult to detect using conventional NN.

  9. Developing a conceptual model for selecting and evaluating online markets

    Directory of Open Access Journals (Sweden)

    Sadegh Feizollahi

    2013-04-01

    Full Text Available There are many evidences, which emphasis on the benefits of using new technologies of information and communication in international business and many believe that E-Commerce can help satisfy customer explicit and implicit requirements. Internet shopping is a concept developed after the introduction of electronic commerce. Information technology (IT and its applications, specifically in the realm of the internet and e-mail promoted the development of e-commerce in terms of advertising, motivating and information. However, with the development of new technologies, credit and financial exchange on the internet websites were constructed so to facilitate e-commerce. The proposed study sends a total of 200 questionnaires to the target group (teachers - students - professionals - managers of commercial web sites and it manages to collect 130 questionnaires for final evaluation. Cronbach's alpha test is used for measuring reliability and to evaluate the validity of measurement instruments (questionnaires, and to assure construct validity, confirmatory factor analysis is employed. In addition, in order to analyze the research questions based on the path analysis method and to determine markets selection models, a regular technique is implemented. In the present study, after examining different aspects of e-commerce, we provide a conceptual model for selecting and evaluating online marketing in Iran. These findings provide a consistent, targeted and holistic framework for the development of the Internet market in the country.

  10. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Bijay Neupane

    2017-01-01

    Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.

  11. A Network Analysis Model for Selecting Sustainable Technology

    Directory of Open Access Journals (Sweden)

    Sangsung Park

    2015-09-01

    Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.

  12. Cliff-edge model of obstetric selection in humans.

    Science.gov (United States)

    Mitteroecker, Philipp; Huttegger, Simon M; Fischer, Barbara; Pavlicev, Mihaela

    2016-12-20

    The strikingly high incidence of obstructed labor due to the disproportion of fetal size and the mother's pelvic dimensions has puzzled evolutionary scientists for decades. Here we propose that these high rates are a direct consequence of the distinct characteristics of human obstetric selection. Neonatal size relative to the birth-relevant maternal dimensions is highly variable and positively associated with reproductive success until it reaches a critical value, beyond which natural delivery becomes impossible. As a consequence, the symmetric phenotype distribution cannot match the highly asymmetric, cliff-edged fitness distribution well: The optimal phenotype distribution that maximizes population mean fitness entails a fraction of individuals falling beyond the "fitness edge" (i.e., those with fetopelvic disproportion). Using a simple mathematical model, we show that weak directional selection for a large neonate, a narrow pelvic canal, or both is sufficient to account for the considerable incidence of fetopelvic disproportion. Based on this model, we predict that the regular use of Caesarean sections throughout the last decades has led to an evolutionary increase of fetopelvic disproportion rates by 10 to 20%.

  13. Addressing selected problems of the modelling of digital control systems

    International Nuclear Information System (INIS)

    Sedlak, J.

    2004-12-01

    The introduction of digital systems to practical activities at nuclear power plants brings about new requirements for their modelling for the purposes of reliability analyses required for plant licensing as well as for inclusion into PSA studies and subsequent use in applications for the assessment of events, limits and conditions, and risk monitoring. It is very important to assess, both qualitatively and quantitatively, the effect of this change on operational safety. The report describes selected specific features of reliability analysis of digital system and recommends methodological procedures. The chapters of the report are as follows: (1) Flexibility and multifunctionality of the system. (2) General framework of reliability analyses (Understanding the system; Qualitative analysis; Quantitative analysis; Assessment of results, comparison against criteria; Documenting system reliability analyses; Asking for comments and their evaluation); and (3) Suitable reliability models (Reliability models of basic events; Monitored components with repair immediately following defect or failure; Periodically tested components; Constant unavailability (probability of failure to demand); Application of reliability models for electronic components; Example of failure rate decomposition; Example modified for diagnosis successfulness; Transfer of reliability analyses to PSA; Common cause failures - CCF; Software backup and CCF type failures, software versus hardware). (P.A.)

  14. A CONCEPTUAL MODEL FOR IMPROVED PROJECT SELECTION AND PRIORITISATION

    Directory of Open Access Journals (Sweden)

    P. J. Viljoen

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: Project portfolio management processes are often designed and operated as a series of stages (or project phases and gates. However, the flow of such a process is often slow, characterised by queues waiting for a gate decision and by repeated work from previous stages waiting for additional information or for re-processing. In this paper the authors propose a conceptual model that applies supply chain and constraint management principles to the project portfolio management process. An advantage of the proposed model is that it provides the ability to select and prioritise projects without undue changes to project schedules. This should result in faster flow through the system.

    AFRIKAANSE OPSOMMING: Prosesse om portefeuljes van projekte te bestuur word normaalweg ontwerp en bedryf as ’n reeks fases en hekke. Die vloei deur so ’n proses is dikwels stadig en word gekenmerk deur toue wat wag vir besluite by die hekke en ook deur herwerk van vorige fases wat wag vir verdere inligting of vir herprosessering. In hierdie artikel word ‘n konseptuele model voorgestel. Die model berus op die beginsels van voorsieningskettings sowel as van beperkingsbestuur, en bied die voordeel dat projekte geselekteer en geprioritiseer kan word sonder onnodige veranderinge aan projekskedules. Dit behoort te lei tot versnelde vloei deur die stelsel.

  15. Selection of an appropriately simple storm runoff model

    Directory of Open Access Journals (Sweden)

    A. I. J. M. van Dijk

    2010-03-01

    Full Text Available An appropriately simple event runoff model for catchment hydrological studies was derived. The model was selected from several variants as having the optimum balance between simplicity and the ability to explain daily observations of streamflow from 260 Australian catchments (23–1902 km2. Event rainfall and runoff were estimated from the observations through a combination of baseflow separation and storm flow recession analysis, producing a storm flow recession coefficient (kQF. Various model structures with up to six free parameters were investigated, covering most of the equations applied in existing lumped catchment models. The performance of alternative structures and free parameters were expressed in Aikake's Final Prediction Error Criterion (FPEC and corresponding Nash-Sutcliffe model efficiencies (NSME for event runoff totals. For each model variant, the number of free parameters was reduced in steps based on calculated parameter sensitivity. The resulting optimal model structure had two or three free parameters; the first describing the non-linear relationship between event rainfall and runoff (Smax, the second relating runoff to antecedent groundwater storage (CSg, and a third that described initial rainfall losses (Li, but which could be set at 8 mm without affecting model performance too much. The best three parameter model produced a median NSME of 0.64 and outperformed, for example, the Soil Conservation Service Curve Number technique (median NSME 0.30–0.41. Parameter estimation in ungauged catchments is likely to be challenging: 64% of the variance in kQF among stations could be explained by catchment climate indicators and spatial correlation, but corresponding numbers were a modest 45% for CSg, 21% for Smax and none for Li, respectively. In gauged catchments, better

  16. Impact of selected troposphere models on Precise Point Positioning convergence

    Science.gov (United States)

    Kalita, Jakub; Rzepecka, Zofia

    2016-04-01

    The Precise Point Positioning (PPP) absolute method is currently intensively investigated in order to reach fast convergence time. Among various sources that influence the convergence of the PPP, the tropospheric delay is one of the most important. Numerous models of tropospheric delay are developed and applied to PPP processing. However, with rare exceptions, the quality of those models does not allow fixing the zenith path delay tropospheric parameter, leaving difference between nominal and final value to the estimation process. Here we present comparison of several PPP result sets, each of which based on different troposphere model. The respective nominal values are adopted from models: VMF1, GPT2w, MOPS and ZERO-WET. The PPP solution admitted as reference is based on the final troposphere product from the International GNSS Service (IGS). The VMF1 mapping function was used for all processing variants in order to provide capability to compare impact of applied nominal values. The worst case initiates zenith wet delay with zero value (ZERO-WET). Impact from all possible models for tropospheric nominal values should fit inside both IGS and ZERO-WET border variants. The analysis is based on data from seven IGS stations located in mid-latitude European region from year 2014. For the purpose of this study several days with the most active troposphere were selected for each of the station. All the PPP solutions were determined using gLAB open-source software, with the Kalman filter implemented independently by the authors of this work. The processing was performed on 1 hour slices of observation data. In addition to the analysis of the output processing files, the presented study contains detailed analysis of the tropospheric conditions for the selected data. The overall results show that for the height component the VMF1 model outperforms GPT2w and MOPS by 35-40% and ZERO-WET variant by 150%. In most of the cases all solutions converge to the same values during first

  17. On model selections for repeated measurement data in clinical studies.

    Science.gov (United States)

    Zou, Baiming; Jin, Bo; Koch, Gary G; Zhou, Haibo; Borst, Stephen E; Menon, Sandeep; Shuster, Jonathan J

    2015-05-10

    Repeated measurement designs have been widely used in various randomized controlled trials for evaluating long-term intervention efficacies. For some clinical trials, the primary research question is how to compare two treatments at a fixed time, using a t-test. Although simple, robust, and convenient, this type of analysis fails to utilize a large amount of collected information. Alternatively, the mixed-effects model is commonly used for repeated measurement data. It models all available data jointly and allows explicit assessment of the overall treatment effects across the entire time spectrum. In this paper, we propose an analytic strategy for longitudinal clinical trial data where the mixed-effects model is coupled with a model selection scheme. The proposed test statistics not only make full use of all available data but also utilize the information from the optimal model deemed for the data. The performance of the proposed method under various setups, including different data missing mechanisms, is evaluated via extensive Monte Carlo simulations. Our numerical results demonstrate that the proposed analytic procedure is more powerful than the t-test when the primary interest is to test for the treatment effect at the last time point. Simulations also reveal that the proposed method outperforms the usual mixed-effects model for testing the overall treatment effects across time. In addition, the proposed framework is more robust and flexible in dealing with missing data compared with several competing methods. The utility of the proposed method is demonstrated by analyzing a clinical trial on the cognitive effect of testosterone in geriatric men with low baseline testosterone levels. Copyright © 2015 John Wiley & Sons, Ltd.

  18. Computationally efficient thermal-mechanical modelling of selective laser melting

    Science.gov (United States)

    Yang, Yabin; Ayas, Can

    2017-10-01

    The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is anticipated to be instrumental for understanding and predicting the development of residual stress field during the build process. However, SLM process modelling requires determination of the heat transients within the part being built which is coupled to a mechanical boundary value problem to calculate displacement and residual stress fields. Thermal models associated with SLM are typically complex and computationally demanding. In this paper, we present a simple semi-analytical thermal-mechanical model, developed for SLM that represents the effect of laser scanning vectors with line heat sources. The temperature field within the part being build is attained by superposition of temperature field associated with line heat sources in a semi-infinite medium and a complimentary temperature field which accounts for the actual boundary conditions. An analytical solution of a line heat source in a semi-infinite medium is first described followed by the numerical procedure used for finding the complimentary temperature field. This analytical description of the line heat sources is able to capture the steep temperature gradients in the vicinity of the laser spot which is typically tens of micrometers. In turn, semi-analytical thermal model allows for having a relatively coarse discretisation of the complimentary temperature field. The temperature history determined is used to calculate the thermal strain induced on the SLM part. Finally, a mechanical model governed by elastic-plastic constitutive rule having isotropic hardening is used to predict the residual stresses.

  19. 4th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations

    CERN Document Server

    Zimroz, Radoslaw; Bartelmus, Walter; Haddar, Mohamed

    2016-01-01

    The book provides readers with a snapshot of recent research and technological trends in the field of condition monitoring of machinery working under a broad range of operating conditions. Each chapter, accepted after a rigorous peer-review process, reports on an original piece of work presented and discussed at the 4th International Conference on Condition Monitoring of Machinery in Non-stationary Operations, CMMNO 2014, held on December 15-16, 2014, in Lyon, France. The contributions have been grouped into three different sections according to the main subfield (signal processing, data mining, or condition monitoring techniques) they are related to. The book includes both theoretical developments as well as a number of industrial case studies, in different areas including, but not limited to: noise and vibration; vibro-acoustic diagnosis; signal processing techniques; diagnostic data analysis; instantaneous speed identification; monitoring and diagnostic systems; and dynamic and fault modeling. This book no...

  20. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

    Science.gov (United States)

    Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

    2018-01-24

    Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

  1. Multicriteria decision group model for the selection of suppliers

    Directory of Open Access Journals (Sweden)

    Luciana Hazin Alencar

    2008-08-01

    Full Text Available Several authors have been studying group decision making over the years, which indicates how relevant it is. This paper presents a multicriteria group decision model based on ELECTRE IV and VIP Analysis methods, to those cases where there is great divergence among the decision makers. This model includes two stages. In the first, the ELECTRE IV method is applied and a collective criteria ranking is obtained. In the second, using criteria ranking, VIP Analysis is applied and the alternatives are selected. To illustrate the model, a numerical application in the context of the selection of suppliers in project management is used. The suppliers that form part of the project team have a crucial role in project management. They are involved in a network of connected activities that can jeopardize the success of the project, if they are not undertaken in an appropriate way. The question tackled is how to select service suppliers for a project on behalf of an enterprise that assists the multiple objectives of the decision-makers.Vários autores têm estudado decisão em grupo nos últimos anos, o que indica a relevância do assunto. Esse artigo apresenta um modelo multicritério de decisão em grupo baseado nos métodos ELECTRE IV e VIP Analysis, adequado aos casos em que se tem uma grande divergência entre os decisores. Esse modelo é composto por dois estágios. No primeiro, o método ELECTRE IV é aplicado e uma ordenação dos critérios é obtida. No próximo estágio, com a ordenação dos critérios, o método VIP Analysis é aplicado e as alternativas são selecionadas. Para ilustrar o modelo, uma aplicação numérica no contexto da seleção de fornecedores em projetos é realizada. Os fornecedores que fazem parte da equipe do projeto têm um papel fundamental no gerenciamento de projetos. Eles estão envolvidos em uma rede de atividades conectadas que, caso não sejam executadas de forma apropriada, podem colocar em risco o sucesso do

  2. Improving permafrost distribution modelling using feature selection algorithms

    Science.gov (United States)

    Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

    2016-04-01

    The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its

  3. A Model for Selection of Eyespots on Butterfly Wings.

    Science.gov (United States)

    Sekimura, Toshio; Venkataraman, Chandrasekhar; Madzvamuse, Anotida

    2015-01-01

    The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins). A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not. We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed) boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus point distributions observed in

  4. A Model for Selection of Eyespots on Butterfly Wings.

    Directory of Open Access Journals (Sweden)

    Toshio Sekimura

    Full Text Available The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins. A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not.We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus point distributions

  5. Multiphysics modeling of selective laser sintering/melting

    Science.gov (United States)

    Ganeriwala, Rishi Kumar

    A significant percentage of total global employment is due to the manufacturing industry. However, manufacturing also accounts for nearly 20% of total energy usage in the United States according to the EIA. In fact, manufacturing accounted for 90% of industrial energy consumption and 84% of industry carbon dioxide emissions in 2002. Clearly, advances in manufacturing technology and efficiency are necessary to curb emissions and help society as a whole. Additive manufacturing (AM) refers to a relatively recent group of manufacturing technologies whereby one can 3D print parts, which has the potential to significantly reduce waste, reconfigure the supply chain, and generally disrupt the whole manufacturing industry. Selective laser sintering/melting (SLS/SLM) is one type of AM technology with the distinct advantage of being able to 3D print metals and rapidly produce net shape parts with complicated geometries. In SLS/SLM parts are built up layer-by-layer out of powder particles, which are selectively sintered/melted via a laser. However, in order to produce defect-free parts of sufficient strength, the process parameters (laser power, scan speed, layer thickness, powder size, etc.) must be carefully optimized. Obviously, these process parameters will vary depending on material, part geometry, and desired final part characteristics. Running experiments to optimize these parameters is costly, energy intensive, and extremely material specific. Thus a computational model of this process would be highly valuable. In this work a three dimensional, reduced order, coupled discrete element - finite difference model is presented for simulating the deposition and subsequent laser heating of a layer of powder particles sitting on top of a substrate. Validation is provided and parameter studies are conducted showing the ability of this model to help determine appropriate process parameters and an optimal powder size distribution for a given material. Next, thermal stresses upon

  6. Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage

    DEFF Research Database (Denmark)

    Yang, Ziheng; Nielsen, Rasmus

    2008-01-01

    Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we impl...... codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed.......Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... implement a few population genetics models of codon substitution that explicitly consider mutation bias and natural selection at the DNA level. Selection on codon usage is modeled by introducing codon-fitness parameters, which together with mutation-bias parameters, predict optimal codon frequencies...

  7. Multiscale singular value manifold for rotating machinery fault diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Yi; Lu, BaoChun; Zhang, Deng Feng [School of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing (United States)

    2017-01-15

    Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.

  8. Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects

    Directory of Open Access Journals (Sweden)

    Guangjie Li

    2015-07-01

    Full Text Available We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002 does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE. We also study the implications of different levels of inclusion probabilities by simulations.

  9. Hyperopt: a Python library for model selection and hyperparameter optimization

    Science.gov (United States)

    Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

    2015-01-01

    Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

  10. Model catalysis by size-selected cluster deposition

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, Scott [Univ. of Utah, Salt Lake City, UT (United States)

    2015-11-20

    This report summarizes the accomplishments during the last four years of the subject grant. Results are presented for experiments in which size-selected model catalysts were studied under surface science and aqueous electrochemical conditions. Strong effects of cluster size were found, and by correlating the size effects with size-dependent physical properties of the samples measured by surface science methods, it was possible to deduce mechanistic insights, such as the factors that control the rate-limiting step in the reactions. Results are presented for CO oxidation, CO binding energetics and geometries, and electronic effects under surface science conditions, and for the electrochemical oxygen reduction reaction, ethanol oxidation reaction, and for oxidation of carbon by water.

  11. Quantitative genetic models of sexual selection by male choice.

    Science.gov (United States)

    Nakahashi, Wataru

    2008-09-01

    There are many examples of male mate choice for female traits that tend to be associated with high fertility. I develop quantitative genetic models of a female trait and a male preference to show when such a male preference can evolve. I find that a disagreement between the fertility maximum and the viability maximum of the female trait is necessary for directional male preference (preference for extreme female trait values) to evolve. Moreover, when there is a shortage of available male partners or variance in male nongenetic quality, strong male preference can evolve. Furthermore, I also show that males evolve to exhibit a stronger preference for females that are more feminine (less resemblance to males) than the average female when there is a sexual dimorphism caused by fertility selection which acts only on females.

  12. Analytical Modelling Of Milling For Tool Design And Selection

    International Nuclear Information System (INIS)

    Fontaine, M.; Devillez, A.; Dudzinski, D.

    2007-01-01

    This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools

  13. Structural insights into the bacterial carbon - phosphorus lyase machinery

    DEFF Research Database (Denmark)

    Seweryn, Paulina; Van, Lan Bich; Kjeldgaard, Morten

    2015-01-01

    Phosphorus is required for all life and microorganisms can extract it from their environment through several metabolic pathways. When phosphate is in limited supply, some bacteria are able to use phosphonate compounds, which require specialized enzymatic machinery to break the stable carbon......–phosphorus (C–P) bond. Despite its importance, the details of how this machinery catabolizes phosphonates remain unknown. Here we determine the crystal structure of the 240-kilodalton Escherichia coli C–P lyase core complex (PhnG–PhnH–PhnI–PhnJ; PhnGHIJ), and show that it is a two-fold symmetric hetero...

  14. Densification of chipper harvested SRC using on-farm machinery

    Energy Technology Data Exchange (ETDEWEB)

    Paulson, M.

    2003-07-01

    This report gives details of a project to density wood chips using on-farm machinery in order to avoid problems encountered in bulk handling and storage of low density short rotation cultivation (SRC) wood chips - especially as some customers can only accept baled material. Trials using different lengths of chips produced by a standard SRC harvester are described, and the failure to produce acceptable bales is reported. The potential cost of modifying equipment is deemed to make the baling of SRC chips using standard farm machinery currently not viable.

  15. ModelMage: a tool for automatic model generation, selection and management.

    Science.gov (United States)

    Flöttmann, Max; Schaber, Jörg; Hoops, Stephan; Klipp, Edda; Mendes, Pedro

    2008-01-01

    Mathematical modeling of biological systems usually involves implementing, simulating, and discriminating several candidate models that represent alternative hypotheses. Generating and managing these candidate models is a tedious and difficult task and can easily lead to errors. ModelMage is a tool that facilitates management of candidate models. It is designed for the easy and rapid development, generation, simulation, and discrimination of candidate models. The main idea of the program is to automatically create a defined set of model alternatives from a single master model. The user provides only one SBML-model and a set of directives from which the candidate models are created by leaving out species, modifiers or reactions. After generating models the software can automatically fit all these models to the data and provides a ranking for model selection, in case data is available. In contrast to other model generation programs, ModelMage aims at generating only a limited set of models that the user can precisely define. ModelMage uses COPASI as a simulation and optimization engine. Thus, all simulation and optimization features of COPASI are readily incorporated. ModelMage can be downloaded from http://sysbio.molgen.mpg.de/modelmage and is distributed as free software.

  16. Within-host selection of drug resistance in a mouse model reveals dose-dependent selection of atovaquone resistance mutations

    NARCIS (Netherlands)

    Nuralitha, Suci; Murdiyarso, Lydia S.; Siregar, Josephine E.; Syafruddin, Din; Roelands, Jessica; Verhoef, Jan; Hoepelman, Andy I.M.; Marzuki, Sangkot

    2017-01-01

    The evolutionary selection of malaria parasites within an individual host plays a critical role in the emergence of drug resistance. We have compared the selection of atovaquone resistance mutants in mouse models reflecting two different causes of failure of malaria treatment, an inadequate

  17. The adaptive response of lichens to mercury exposure involves changes in the photosynthetic machinery

    International Nuclear Information System (INIS)

    Nicolardi, Valentina; Cai, Giampiero; Parrotta, Luigi; Puglia, Michele; Bianchi, Laura; Bini, Luca; Gaggi, Carlo

    2012-01-01

    Lichens are an excellent model to study the bioaccumulation of heavy metals but limited information is available on the molecular mechanisms occurring during bioaccumulation. We investigated the changes of the lichen proteome during exposure to constant concentrations of mercury. We found that most of changes involves proteins of the photosynthetic pathway, such as the chloroplastic photosystem I reaction center subunit II, the oxygen-evolving protein and the chloroplastic ATP synthase β-subunit. This suggests that photosynthesis is a target of the toxic effects of mercury. These findings are also supported by changes in the content of photosynthetic pigments (chlorophyll a and b, and β-carotene). Alterations to the photosynthetic machinery also reflect on the structure of thylakoid membranes of algal cells. Response of lichens to mercury also involves stress-related proteins (such as Hsp70) but not cytoskeletal proteins. Results suggest that lichens adapt to mercury exposure by changing the metabolic production of energy. - Highlights: ► Lichens exposed to Hg° vapors accumulate this metal irreversibly. ► Hg° interferes with physiological processes of the epiphytic lichen Evernia prunastri. ► Hg° promotes changes in the concentration of photosynthetic pigments. ► Hg° treatment causes changes in the ultrastructure of the photobiont plastids. ► Hg° induces changes in the protein machinery involved in the photosynthesis pathway. - Mercury affects the photosynthetic protein machinery of lichens.

  18. The iron-sulfur cluster assembly machineries in plants: current knowledge and open questions

    Science.gov (United States)

    Couturier, Jérémy; Touraine, Brigitte; Briat, Jean-François; Gaymard, Frédéric; Rouhier, Nicolas

    2013-01-01

    Many metabolic pathways and cellular processes occurring in most sub-cellular compartments depend on the functioning of iron-sulfur (Fe-S) proteins, whose cofactors are assembled through dedicated protein machineries. Recent advances have been made in the knowledge of the functions of individual components through a combination of genetic, biochemical and structural approaches, primarily in prokaryotes and non-plant eukaryotes. Whereas most of the components of these machineries are conserved between kingdoms, their complexity is likely increased in plants owing to the presence of additional assembly proteins and to the existence of expanded families for several assembly proteins. This review focuses on the new actors discovered in the past few years, such as glutaredoxin, BOLA and NEET proteins as well as MIP18, MMS19, TAH18, DRE2 for the cytosolic machinery, which are integrated into a model for the plant Fe-S cluster biogenesis systems. It also discusses a few issues currently subjected to an intense debate such as the role of the mitochondrial frataxin and of glutaredoxins, the functional separation between scaffold, carrier and iron-delivery proteins and the crosstalk existing between different organelles. PMID:23898337

  19. CHAIN-WISE GENERALIZATION OF ROAD NETWORKS USING MODEL SELECTION

    Directory of Open Access Journals (Sweden)

    D. Bulatov

    2017-05-01

    Full Text Available Streets are essential entities of urban terrain and their automatized extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological and semantic aspects. Given a binary image, representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. We propose the fusion of raw segments based on similarity criteria; the output of this process are the so-called chains which better match to the intuitive perception of what a street is. Further, we propose a two-step approach for chain-wise generalization. First, the chain is pre-segmented using circlePeucker and finally, model selection is used to decide whether two neighboring segments should be fused to a new geometric entity. Thereby, we consider both variance-covariance analysis of residuals and model complexity. The results on a complex data-set with many traffic roundabouts indicate the benefits of the proposed procedure.

  20. A computational neural model of goal-directed utterance selection.

    Science.gov (United States)

    Klein, Michael; Kamp, Hans; Palm, Guenther; Doya, Kenji

    2010-06-01

    It is generally agreed that much of human communication is motivated by extra-linguistic goals: we often make utterances in order to get others to do something, or to make them support our cause, or adopt our point of view, etc. However, thus far a computational foundation for this view on language use has been lacking. In this paper we propose such a foundation using Markov Decision Processes. We borrow computational components from the field of action selection and motor control, where a neurobiological basis of these components has been established. In particular, we make use of internal models (i.e., next-state transition functions defined on current state action pairs). The internal model is coupled with reinforcement learning of a value function that is used to assess the desirability of any state that utterances (as well as certain non-verbal actions) can bring about. This cognitive architecture is tested in a number of multi-agent game simulations. In these computational experiments an agent learns to predict the context-dependent effects of utterances by interacting with other agents that are already competent speakers. We show that the cognitive architecture can account for acquiring the capability of deciding when to speak in order to achieve a certain goal (instead of performing a non-verbal action or simply doing nothing), whom to address and what to say. Copyright 2010 Elsevier Ltd. All rights reserved.

  1. Selection Bias in Educational Transition Models: Theory and Empirical Evidence

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads

    variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...

  2. Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

    Science.gov (United States)

    Wentworth, Mami Tonoe

    Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification

  3. Liquid metal current collectors for high-speed rotating machinery

    International Nuclear Information System (INIS)

    Carr, S.L.

    1976-01-01

    Recent interest in superconducting motors and generators has created a renewed interest in homopolar machinery. Homopolar machine designs have always been limited by the need for compact, high-current, low-voltage, sliding electrical curent collectors. Conventional graphite-based solid brushes are inadequate for use in homopolar machines. Liquid metals, under certain conditions of relative sliding velocities, electrical currents, and magnetic fields are known to be capable of performing well in homopolar machines. An effort to explore the capabilities and limits of a tongue-and-groove style current collector, utilizing sodium-potassium eutectic alloy (NaK) as the working fluid in high sliding speed operation is reported here. A double current collector generator model with a 14.5-cm maximum rotor diameter, 20,000 rpm rotational capability, and electrical current carrying ability was constructed and operated successfully at a peripheral velocity of 125 m/s. The limiting factor in these experiments was a high-speed fluid-flow instability resulting in the ejection of the working fluid from the operating portions of the collectors. The effects of collector size and geometry, working fluid (NaK or water), and cover gas pressure are reported. Hydrodynamic frictional torque-speed curves are given for the two fluids and for several geometries. Electrical resistances as a function of peripheral velocity at 60 amperes are reported, and the phenomenology of the high-speed fluid-flow instabilities is discussed. The possibility of long-term high-speed operation of current collectors of the tongue-and-groove type, along with experimental and theoretical hydrodynamic friction losses at high peripheral velocities, is considered

  4. The Machinery for Enforcement of Domestic Arbitral Awards in Nigeria

    African Journals Online (AJOL)

    Nnamdi Azikiwe University Journal of International Law and Jurisprudence ... Arbitration is a private means of resolving dispute which is resorted to, chiefly because the parties choose to avoid as much as possible employing the state machinery for dispute resolution, namely the court and its dreaded time consuming ...

  5. The capacity of sugar beet farms’ machinery and equipment

    Directory of Open Access Journals (Sweden)

    Małgorzata BZOWSKA – BAKALARZ

    2012-12-01

    Full Text Available The survey investigates into equipment of sugar beet farms of the Lublin region, Poland, with machinery – with reference to plantation size and yields. To assess the production potential of the farms, the authors determined the age structure of the machinery owned by the farmers and established the scale of investment in new equipment. The machinery most important for sugar beet production are pre-sowing and post-harvest tillage units, sprayers, seed drills, combine harvesters and self-unloading trailers. In most cases, the surveyed farmers own most of these machines, but they are often obsolete: 37% of them is in operation for more than 15 years. As for the machines dedicated solely to sugar beet growing (harvesters and seeders, their age structure is most unfavourable – 70% of them have been used for over 15 years. A trend towards increasing plantation sizes provides incentives for introducing innovation to cultivation methods. However, the scale of investment in new machinery is small, especially in the case of small and medium-sized farms that dominate in the region. The authors surveyed also the scale of using professional services in the field of tillage processes to determine changes in farming practices.

  6. Web-based Interactive Simulator for Rotating Machinery.

    Science.gov (United States)

    Sirohi, Vijayalaxmi

    1999-01-01

    Baroma (Balance of Rotating Machinery), the Web-based educational engineering interactive software for teaching/learning combines didactical and software ergonomical approaches. The software in tutorial form simulates a problem using Visual Interactive Simulation in graphic display, and animation is brought about through graphical user interface…

  7. The turbulent viscosity models and their experimental validation; Les modeles de viscosite turbulente et leur validation experimentale

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-12-31

    This workshop on turbulent viscosity models and on their experimental validation was organized by the `convection` section of the French society of thermal engineers. From the 9 papers presented during this workshop, 8 deal with the modeling of turbulent flows inside combustion chambers, turbo-machineries or in other energy-related applications, and have been selected for ETDE. (J.S.)

  8. How Parkinsonian Toxins Dysregulate the Autophagy Machinery

    Directory of Open Access Journals (Sweden)

    Ruben K. Dagda

    2013-11-01

    Full Text Available Since their discovery, Parkinsonian toxins (6-hydroxydopamine, MPP+, paraquat, and rotenone have been widely employed as in vivo and in vitro chemical models of Parkinson’s disease (PD. Alterations in mitochondrial homeostasis, protein quality control pathways, and more recently, autophagy/mitophagy have been implicated in neurotoxin models of PD. Here, we highlight the molecular mechanisms by which different PD toxins dysregulate autophagy/mitophagy and how alterations of these pathways play beneficial or detrimental roles in dopamine neurons. The convergent and divergent effects of PD toxins on mitochondrial function and autophagy/mitophagy are also discussed in this review. Furthermore, we propose new diagnostic tools and discuss how pharmacological modulators of autophagy/mitophagy can be developed as disease-modifying treatments for PD. Finally, we discuss the critical need to identify endogenous and synthetic forms of PD toxins and develop efficient health preventive programs to mitigate the risk of developing PD.

  9. Heat transfer modelling and stability analysis of selective laser melting

    International Nuclear Information System (INIS)

    Gusarov, A.V.; Yadroitsev, I.; Bertrand, Ph.; Smurov, I.

    2007-01-01

    The process of direct manufacturing by selective laser melting basically consists of laser beam scanning over a thin powder layer deposited on a dense substrate. Complete remelting of the powder in the scanned zone and its good adhesion to the substrate ensure obtaining functional parts with improved mechanical properties. Experiments with single-line scanning indicate, that an interval of scanning velocities exists where the remelted tracks are uniform. The tracks become broken if the scanning velocity is outside this interval. This is extremely undesirable and referred to as the 'balling' effect. A numerical model of coupled radiation and heat transfer is proposed to analyse the observed instability. The 'balling' effect at high scanning velocities (above ∼20 cm/s for the present conditions) can be explained by the Plateau-Rayleigh capillary instability of the melt pool. Two factors stabilize the process with decreasing the scanning velocity: reducing the length-to-width ratio of the melt pool and increasing the width of its contact with the substrate

  10. Optimization of advanced liquid natural gas-fuelled machineries for a high-speed ferry

    DEFF Research Database (Denmark)

    Tveitaskog, Kari Anne; Haglind, Fredrik

    -based optimization routine are used. The top cycle is modeled as the aero-derivative gas turbine LM2500, while the following five options for bottoming cycles are modeled: ∙ Single pressure steam cycle ∙ Dual-pressure steam cycle ∙ ORC using Toluene as the working fluid with an intermediate oil loop ∙ ABC with inter......This report is aimed at designing and optimizing combined cycles in order to define the most suitable machinery system for the future high-speed Incat ferry operated by Mols-Linien. For this purpose, an in-house numerical simulation tool called DNA (Dynamic Network Analysis) and a genetic algorithm...

  11. In vitro efficacy, resistance selection, and structural modeling studies implicate the malarial parasite apicoplast as the target of azithromycin.

    Science.gov (United States)

    Sidhu, Amar Bir Singh; Sun, Qingan; Nkrumah, Louis J; Dunne, Michael W; Sacchettini, James C; Fidock, David A

    2007-01-26

    Azithromycin (AZ), a broad-spectrum antibacterial macrolide that inhibits protein synthesis, also manifests reasonable efficacy as an antimalarial. Its mode of action against malarial parasites, however, has remained undefined. Our in vitro investigations with the human malarial parasite Plasmodium falciparum document a remarkable increase in AZ potency when exposure is prolonged from one to two generations of intraerythrocytic growth, with AZ producing 50% inhibition of parasite growth at concentrations in the mid to low nanomolar range. In our culture-adapted lines, AZ displayed no synergy with chloroquine (CQ), amodiaquine, or artesunate. AZ activity was also unaffected by mutations in the pfcrt (P. falciparum chloroquine resistance transporter) or pfmdr1 (P. falciparum multidrug resistance-1) drug resistance loci, as determined using transgenic lines. We have selected mutant, AZ-resistant 7G8 and Dd2 parasite lines. In the AZ-resistant 7G8 line, the bacterial-like apicoplast large subunit ribosomal RNA harbored a U438C mutation in domain I. Both AZ-resistant lines revealed a G76V mutation in a conserved region of the apicoplast-encoded P. falciparum ribosomal protein L4 (PfRpl4). This protein is predicted to associate with the nuclear genome-encoded P. falciparum ribosomal protein L22 (PfRpl22) and the large subunit rRNA to form the 50 S ribosome polypeptide exit tunnel that can be occupied by AZ. The PfRpl22 sequence remained unchanged. Molecular modeling of mutant PfRpl4 with AZ suggests an altered orientation of the L75 side chain that could preclude AZ binding. These data imply that AZ acts on the apicoplast bacterial-like translation machinery and identify Pfrpl4 as a potential marker of resistance.

  12. Equilibrium and nonequilibrium attractors for a discrete, selection-migration model

    Science.gov (United States)

    James F. Selgrade; James H. Roberds

    2003-01-01

    This study presents a discrete-time model for the effects of selection and immigration on the demographic and genetic compositions of a population. Under biologically reasonable conditions, it is shown that the model always has an equilibrium. Although equilibria for similar models without migration must have real eigenvalues, for this selection-migration model we...

  13. Performance Measurement Model for the Supplier Selection Based on AHP

    Directory of Open Access Journals (Sweden)

    Fabio De Felice

    2015-10-01

    Full Text Available The performance of the supplier is a crucial factor for the success or failure of any company. Rational and effective decision making in terms of the supplier selection process can help the organization to optimize cost and quality functions. The nature of supplier selection processes is generally complex, especially when the company has a large variety of products and vendors. Over the years, several solutions and methods have emerged for addressing the supplier selection problem (SSP. Experience and studies have shown that there is no best way for evaluating and selecting a specific supplier process, but that it varies from one organization to another. The aim of this research is to demonstrate how a multiple attribute decision making approach can be effectively applied for the supplier selection process.

  14. The Research of Computer Aided Farm Machinery Designing Method Based on Ergonomics

    Science.gov (United States)

    Gao, Xiyin; Li, Xinling; Song, Qiang; Zheng, Ying

    Along with agricultural economy development, the farm machinery product type Increases gradually, the ergonomics question is also getting more and more prominent. The widespread application of computer aided machinery design makes it possible that farm machinery design is intuitive, flexible and convenient. At present, because the developed computer aided ergonomics software has not suitable human body database, which is needed in view of farm machinery design in China, the farm machinery design have deviation in ergonomics analysis. This article puts forward that using the open database interface procedure in CATIA to establish human body database which aims at the farm machinery design, and reading the human body data to ergonomics module of CATIA can product practical application virtual body, using human posture analysis and human activity analysis module to analysis the ergonomics in farm machinery, thus computer aided farm machinery designing method based on engineering can be realized.

  15. Selecting representative climate models for climate change impact studies : An advanced envelope-based selection approach

    NARCIS (Netherlands)

    Lutz, Arthur F.; ter Maat, Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.|info:eu-repo/dai/nl/290472113

    2016-01-01

    Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change

  16. Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach

    NARCIS (Netherlands)

    Lutz, Arthur F.; Maat, ter Herbert W.; Biemans, Hester; Shrestha, Arun B.; Wester, Philippus; Immerzeel, Walter W.

    2016-01-01

    Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change

  17. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

    Science.gov (United States)

    Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

    2018-05-01

    This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

  18. 46 CFR 182.465 - Ventilation of spaces containing diesel machinery.

    Science.gov (United States)

    2010-10-01

    ... furnish natural or powered supply and exhaust ventilation. The total inlet area and the total outlet area... 46 Shipping 7 2010-10-01 2010-10-01 false Ventilation of spaces containing diesel machinery. 182... Ventilation of spaces containing diesel machinery. (a) A space containing diesel machinery must be fitted with...

  19. 49 CFR 393.130 - What are the rules for securing heavy vehicles, equipment and machinery?

    Science.gov (United States)

    2010-10-01

    ... heavy vehicles, equipment and machinery? (a) Applicability. The rules in this section apply to the transportation of heavy vehicles, equipment and machinery which operate on wheels or tracks, such as front end... heavy vehicles, equipment or machinery with crawler tracks or wheels. (1) In addition to the...

  20. 46 CFR 97.15-15 - Examination of boilers and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Examination of boilers and machinery. 97.15-15 Section... VESSELS OPERATIONS Tests, Drills, and Inspections § 97.15-15 Examination of boilers and machinery. It shall be the duty of the chief engineer when assuming charge of the boilers and machinery of a vessel to...

  1. 46 CFR 32.35-1 - Boilers and machinery-TB/ALL.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 1 2010-10-01 2010-10-01 false Boilers and machinery-TB/ALL. 32.35-1 Section 32.35-1 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY TANK VESSELS SPECIAL EQUIPMENT, MACHINERY, AND HULL REQUIREMENTS Main and Auxiliary Machinery § 32.35-1 Boilers and machinery—TB/ALL. Boilers, main and auxiliary...

  2. 46 CFR 78.17-30 - Examination of boilers and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 3 2010-10-01 2010-10-01 false Examination of boilers and machinery. 78.17-30 Section... OPERATIONS Tests, Drills, and Inspections § 78.17-30 Examination of boilers and machinery. It shall be the duty of the chief engineer when assuming charge of the boilers and machinery of a vessel to examine...

  3. 46 CFR 196.15-15 - Examination of boilers and machinery.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Examination of boilers and machinery. 196.15-15 Section... VESSELS OPERATIONS Test, Drills, and Inspections § 196.15-15 Examination of boilers and machinery. (a) It shall be the duty of the chief engineer when he assumes charge of the boilers and machinery of a vessel...

  4. 46 CFR 169.631 - Separation of machinery and fuel tank spaces from accommodation spaces.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Separation of machinery and fuel tank spaces from accommodation spaces. 169.631 Section 169.631 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED... machinery and fuel tank spaces from accommodation spaces. (a) Machinery and fuel tank spaces must be...

  5. 46 CFR 116.620 - Ventilation of machinery and fuel tank spaces.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Ventilation of machinery and fuel tank spaces. 116.620... AND ARRANGEMENT Ventilation § 116.620 Ventilation of machinery and fuel tank spaces. In addition to the requirements of this subpart, ventilation systems for spaces containing machinery or fuel tanks...

  6. 46 CFR 169.629 - Compartments containing gasoline machinery or fuel tanks.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 7 2010-10-01 2010-10-01 false Compartments containing gasoline machinery or fuel tanks. 169.629 Section 169.629 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) NAUTICAL... gasoline machinery or fuel tanks. Spaces containing gasoline machinery or fuel tanks must have natural...

  7. 40 CFR 180.521 - Fumigants for grain-mill machinery; tolerances for residues.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Fumigants for grain-mill machinery... Tolerances § 180.521 Fumigants for grain-mill machinery; tolerances for residues. (a) General. Fumigants may be safely used in or on grain-mill machinery in accordance with the following prescribed conditions...

  8. An Integrated DEMATEL-QFD Model for Medical Supplier Selection

    OpenAIRE

    Mehtap Dursun; Zeynep Şener

    2014-01-01

    Supplier selection is considered as one of the most critical issues encountered by operations and purchasing managers to sharpen the company’s competitive advantage. In this paper, a novel fuzzy multi-criteria group decision making approach integrating quality function deployment (QFD) and decision making trial and evaluation laboratory (DEMATEL) method is proposed for supplier selection. The proposed methodology enables to consider the impacts of inner dependence among supplier assessment cr...

  9. National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

    Science.gov (United States)

    Hogan, Daniel R; Salomon, Joshua A; Canning, David; Hammitt, James K; Zaslavsky, Alan M; Bärnighausen, Till

    2012-01-01

    Objectives Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. Methods For 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals. Results Selection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5. Conclusions Our analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence. PMID:23172342

  10. Dedicated monitoring and machinery protection systems on reciprocating compressors

    Energy Technology Data Exchange (ETDEWEB)

    Grande, Alvaro; Wenisch, Markus [Hoerbiger Ventilwerke GmbH and Co KG, Wien (Austria); Jacobs, Denis [HOERBIGER do Brasil Industria de Equipamentos, Cajamar, SP (Brazil)

    2012-07-01

    Growing demands on reciprocating compressors (recips) in the process gas industry require particular solutions for machinery protection and performance monitoring systems. Compared to rotating equipment, monitoring systems for recips have to consider the special mechanical and physical characteristics, such as oscillating masses, variable vibration behaviour and varying operating conditions. Furthermore, they provide valuable information about the performance of cylinder related components allowing the operator the optimization of efficiency and availability, and therefore increase production. (author)

  11. Abrasive Wear of Alloyed Cast Steels Applied for Heavy Machinery

    Directory of Open Access Journals (Sweden)

    Studnicki A.

    2015-03-01

    Full Text Available In the paper the results and analysis of abrasive wear studies were shown for two grades of cast steels: low-alloyed cast steel applied for heavy machinery parts such as housing, covers etc. and chromium cast steels applied for kinetic nodes of pin-sleeve type. Studies were performed using the modified in Department of Foundry pin-on-disc method.

  12. Targeting Transcription Elongation Machinery for Breast Cancer Therapy

    Science.gov (United States)

    2017-05-01

    ABSTRACT: This project focuses on the important but under-studied role of the P-TEFb- dependent transcription elongation machinery in human breast...molecule CDK9 inhibitors can be used to halt breast cancer metastasis. 8 experimental groups to test various drug dosage and frequency regimes will...tumor cells, which are said to be ’ addicted ’ to this protein. Consistently, pharmacological inhibition of Hsp90 has demonstrated great promise in

  13. Electric machinery and drives in thermal power stations

    International Nuclear Information System (INIS)

    1974-01-01

    The following subjects were dealt with during the VDE meeting: 1) Requirements made by the electric network on the generators and their excitation equipment, and the influence thereof on their design; 2) requirements made by the power station process on the electric drives and the influence thereof on type and design; 3) requirements made on protective measures from the point of the electric power station machinery. (TK) [de

  14. Performance of machinery in potato production in one growing season

    Directory of Open Access Journals (Sweden)

    Kun Zhou

    2015-12-01

    Full Text Available Statistics on the machinery performance are essential for farm managers to make better decisions. In this paper, the performance of all machineries in five sequential operations, namely bed forming, stone separation, planting, spraying and harvesting in the potato production system, were investigated during one growing season. In order to analyse and decompose the recorded GPS data into various time and distance elements for estimation of the machinery performance, an automatic GPS analysis tool was developed. The field efficiency and field capacity were estimated for each operation. Specifically, the measured average field efficiency was 71.3% for bed forming, 68.5% for stone separation, 40.3% for planting, 69.7% for spraying, and 67.4% for harvesting. The measured average field capacities were 1.46 ha/h, 0.53 ha/h, 0.47 ha/h, 10.21 ha/h, 0.51 ha/h, for the bed forming, stone separation, planting, spraying, and harvesting operations, respectively. These results deviate from the corresponding estimations calculated based on norm data from the American Society of Agricultural and Biological Engineers (ASABE. The deviations indicate that norms provided by ASABE cannot be used directly for the prediction of performance of the machinery used in this work. Moreover, the measured data of bed forming and stone separation could be used as supplementary data for the ASABE which does not provide performance norms for these two operations. The gained results can help farm managers to make better management and operational decisions that result in potential improvement in productivity and profitability as well as in potential environmental benefits.

  15. Progress in control equipment for fuel-handling machinery

    International Nuclear Information System (INIS)

    Nutting, B.A.

    1986-01-01

    The paper outlines the development of the equipment used to control the fuel-handling machinery associated with nuclear reactors, from the early electromechanical equipment, through solid-state switching logic to programmable controllers and microprocessors. The control techniques have developed along with the technology, and modern systems offer versatility, reliability and ease of design, operation and maintenance. Future trends and developments are discussed together with possible limiting factors. (author)

  16. Designing a machinery control system (MCS) security testbed

    OpenAIRE

    Desso, Nathan H.

    2014-01-01

    Approved for public release; distribution is unlimited Industrial control systems (ICS) face daily cyber security threats, can have a significant impact to the security of our nation, and present a difficult challenge to defend. Critical infrastructures, including military systems like the machinery control systems (MCS) found onboard modern U.S. warships, are affected because of their use of commercial automation solutions. The increase of automated control systems within the U.S. Navy sa...

  17. Performance of machinery in potato production in one growing season

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, K.; Jensen, A.L.; Bochtis, D.D.; Sørensen, C.G.

    2015-07-01

    Statistics on the machinery performance are essential for farm managers to make better decisions. In this paper, the performance of all machineries in five sequential operations, namely bed forming, stone separation, planting, spraying and harvesting in the potato production system, were investigated during one growing season. In order to analyse and decompose the recorded GPS data into various time and distance elements for estimation of the machinery performance, an automatic GPS analysis tool was developed. The field efficiency and field capacity were estimated for each operation. Specifically, the measured average field efficiency was 71.3% for bed forming, 68.5% for stone separation, 40.3% for planting, 69.7% for spraying, and 67.4% for harvesting. The measured average field capacities were 1.46 ha/h, 0.53 ha/h, 0.47 ha/h, 10.21 ha/h, 0.51 ha/h, for the bed forming, stone separation, planting, spraying, and harvesting operations, respectively. These results deviate from the corresponding estimations calculated based on norm data from the American Society of Agricultural and Biological Engineers (ASABE). The deviations indicate that norms provided by ASABE cannot be used directly for the prediction of performance of the machinery used in this work. Moreover, the measured data of bed forming and stone separation could be used as supplementary data for the ASABE which does not provide performance norms for these two operations. The gained results can help farm managers to make better management and operational decisions that result in potential improvement in productivity and profitability as well as in potential environmental benefits. (Author)

  18. Fault diagnosis in rotating machinery by vibration analysis

    International Nuclear Information System (INIS)

    Behzad, M.; Asayesh, M.

    2002-01-01

    Dynamic behavior of unbalanced bent shaft has been investigated in this research. Finite element method is used for unbalance response calculation of a bent shaft. The result shows the effect of bent on the unbalance response. The angle between bent vector and unbalance force, position and type of supports, shaft diameter and disk position can affect the outcome. The results of this research can significantly help in fault diagnosis in rotating machinery

  19. Evaluation of uncertainties in selected environmental dispersion models

    International Nuclear Information System (INIS)

    Little, C.A.; Miller, C.W.

    1979-01-01

    Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations

  20. Production method of hydrogen jet plasma process in hydro machinery

    International Nuclear Information System (INIS)

    Amini, F.

    2007-01-01

    The purpose of present paper is to the process of plasma formation in hydro machinery when a hydro turbine operates at various conditions and load rejection. By investigation the power, shock pressure , and impact effects of hydro machinery, it is revealed that energy and hydrogen are generated by the plasma process. The investigation on several turbines of various hydro power plants reveals that cold fusion process in hydro machinery generates hydrogen. The hypothesis concerning the participation of alkaline metals in river water and the atomic nuclei of the runner blade material in the formation of hydrogen are considered. It is possible to assume hydrogen, deuterium, helium, and tritium atoms (based on Dr. Mizuno and Dr. Kanarev theories) that are formed, diffuse into cavitation bubbles. The plasma is generated during the collapse of the bubble; thus, the quantity of burnt hydrogen determine the volume of generating hydrogen and the impact force caused by hydrogen explosion (noise).There are five main notions, which can determine hydrogen and plasma process: (1) turbine power effect, (2) high shock pressure, (3) crack on turbine parts, (4) impacts effects and (4) the lift of rotating parts. The frequency of the excitation lies in a range from 0.786 to 1.095 Hz.In future, it may be possible to design hydro turbines based on the plasma process that generates hydrogen; or there may exist turbines that rotate with a mixture of hydrogen explosion and water energies

  1. On the reversed Brayton cycle with high speed machinery

    Energy Technology Data Exchange (ETDEWEB)

    Backman, J.

    1996-12-31

    This work was carried out in the laboratory of Fluid Dynamics, at Lappeenranta University of Technology during the years 1991-1996. The research was a part of larger high speed technology development research. First, there was the idea of making high speed machinery applications with the Brayton cycle. There was a clear need to deepen the knowledge of the cycle itself and to make a new approach in the field of the research. Also, the removal of water from the humid air seemed very interesting. The goal of this work was to study methods of designing high speed machinery for the reversed Brayton cycle, from theoretical principles to practical applications. The reversed Brayton cycle can be employed as an air dryer, a heat pump or a refrigerating machine. In this research the use of humid air as a working fluid has an environmental advantage, as well. A new calculation method for the Brayton cycle is developed. In this method especially the expansion process in the turbine is important because of the condensation of the water vapour in the humid air. This physical phenomena can have significant effects on the level of performance of the application. Also, the influence of calculating the process with actual, achievable process equipment efficiencies is essential for the development of future machinery. The above theoretical calculations are confirmed with two different laboratory prototypes. (53 refs.)

  2. On the reversed Brayton cycle with high speed machinery

    Energy Technology Data Exchange (ETDEWEB)

    Backman, J

    1997-12-31

    This work was carried out in the laboratory of Fluid Dynamics, at Lappeenranta University of Technology during the years 1991-1996. The research was a part of larger high speed technology development research. First, there was the idea of making high speed machinery applications with the Brayton cycle. There was a clear need to deepen the knowledge of the cycle itself and to make a new approach in the field of the research. Also, the removal of water from the humid air seemed very interesting. The goal of this work was to study methods of designing high speed machinery for the reversed Brayton cycle, from theoretical principles to practical applications. The reversed Brayton cycle can be employed as an air dryer, a heat pump or a refrigerating machine. In this research the use of humid air as a working fluid has an environmental advantage, as well. A new calculation method for the Brayton cycle is developed. In this method especially the expansion process in the turbine is important because of the condensation of the water vapour in the humid air. This physical phenomena can have significant effects on the level of performance of the application. Also, the influence of calculating the process with actual, achievable process equipment efficiencies is essential for the development of future machinery. The above theoretical calculations are confirmed with two different laboratory prototypes. (53 refs.)

  3. Structural design of nuclear reactor machinery and equipment

    International Nuclear Information System (INIS)

    Hara, Hideki

    1992-01-01

    Since the machinery, equipment and piping which compose nuclear power station facilities are diverse, when those are designed, consideration is given sufficiently to the objective of use and the importance of the object machinery and equipment so that those can maintain the soundness over the design life. In this report, on the contents and the design standard in the design techniques for nuclear reactor machinery and equipment, the way of thinking is shown, taking an example of reactor pressure vessel which is stipulated as the vessel kind 1 in the 'Technical standard of structures and others regarding nuclear facilities for electric power generation', Notice No. 501 of the Ministry of International Trade and Industry. The reactor pressure vessel of 1350 MWe improved type BWR (ABWR) is used under the condition of 87.9 kg/cm 2 and 302 degC, and the inside diameter is about 7.2 m, the inside height is about 21 m, and the wall thickness is about 170 mm. The design standard for reactor pressure vessels and its way of thinking, breakdown prevention design and the design techniques for reactor pressure vessels are described. (K.I.)

  4. Rotating machinery surveillance system reduces plant downtime and radiation exposure

    International Nuclear Information System (INIS)

    Bohanick, J.S.; Robinson, J.C.; Allen, J.W.

    1988-01-01

    A rotating machinery surveillance system (RMSS) was permanently installed at Grand Gulf nuclear station (GGNS) as part of a program sponsored by the US Department of Energy whose goal was to reduce radiation exposure to power plant personnel resulting from the inspection, maintenance, and repair of rotating machinery. The RMSS was installed at GGNS in 1983 to continuously monitor 173 analog vibration signals from proximity probes mounted on 26 machine trains and ∼450 process data points via a computer data link. Vibration frequency spectra, i.e., the vibration amplitude versus frequency of vibration, and various characterizations of these spectra are the fundamental data collected by the RMSS for performing machinery diagnostics. The RMSS collects vibration frequency spectra on a daily basis for all the monitored rotating equipment and automatically stores the collected spectra for review by the vibration engineer. Vibration spectra automatically stored by the RMSS fall into categories that include the last normal, alarm, minimum and maximum, past three-day data set, baseline, current, and user-saved spectra. During first and second fuel-cycle operation at GGNS, several significant vibration problems were detected by the RMSS. Two of these are presented in this paper: recirculation pumps and turbine-generator bearing degradation. The total reduction in personnel radiation exposure at GGNS from 1985 to 1987 due to the presence of the RMSS was estimated to be in the range from 49 to 54 person-rem

  5. Spatiotemporal Regulation of Nuclear Transport Machinery and Microtubule Organization

    Science.gov (United States)

    Okada, Naoyuki; Sato, Masamitsu

    2015-01-01

    Spindle microtubules capture and segregate chromosomes and, therefore, their assembly is an essential event in mitosis. To carry out their mission, many key players for microtubule formation need to be strictly orchestrated. Particularly, proteins that assemble the spindle need to be translocated at appropriate sites during mitosis. A small GTPase (hydrolase enzyme of guanosine triphosphate), Ran, controls this translocation. Ran plays many roles in many cellular events: nucleocytoplasmic shuttling through the nuclear envelope, assembly of the mitotic spindle, and reorganization of the nuclear envelope at the mitotic exit. Although these events are seemingly distinct, recent studies demonstrate that the mechanisms underlying these phenomena are substantially the same as explained by molecular interplay of the master regulator Ran, the transport factor importin, and its cargo proteins. Our review focuses on how the transport machinery regulates mitotic progression of cells. We summarize translocation mechanisms governed by Ran and its regulatory proteins, and particularly focus on Ran-GTP targets in fission yeast that promote spindle formation. We also discuss the coordination of the spatial and temporal regulation of proteins from the viewpoint of transport machinery. We propose that the transport machinery is an essential key that couples the spatial and temporal events in cells. PMID:26308057

  6. Bayesian model selection of template forward models for EEG source reconstruction.

    Science.gov (United States)

    Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan

    2014-06-01

    Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Global economic consequences of selected surgical diseases: a modelling study.

    Science.gov (United States)

    Alkire, Blake C; Shrime, Mark G; Dare, Anna J; Vincent, Jeffrey R; Meara, John G

    2015-04-27

    The surgical burden of disease is substantial, but little is known about the associated economic consequences. We estimate the global macroeconomic impact of the surgical burden of disease due to injury, neoplasm, digestive diseases, and maternal and neonatal disorders from two distinct economic perspectives. We obtained mortality rate estimates for each disease for the years 2000 and 2010 from the Institute of Health Metrics and Evaluation Global Burden of Disease 2010 study, and estimates of the proportion of the burden of the selected diseases that is surgical from a paper by Shrime and colleagues. We first used the value of lost output (VLO) approach, based on the WHO's Projecting the Economic Cost of Ill-Health (EPIC) model, to project annual market economy losses due to these surgical diseases during 2015-30. EPIC attempts to model how disease affects a country's projected labour force and capital stock, which in turn are related to losses in economic output, or gross domestic product (GDP). We then used the value of lost welfare (VLW) approach, which is conceptually based on the value of a statistical life and is inclusive of non-market losses, to estimate the present value of long-run welfare losses resulting from mortality and short-run welfare losses resulting from morbidity incurred during 2010. Sensitivity analyses were performed for both approaches. During 2015-30, the VLO approach projected that surgical conditions would result in losses of 1·25% of potential GDP, or $20·7 trillion (2010 US$, purchasing power parity) in the 128 countries with data available. When expressed as a proportion of potential GDP, annual GDP losses were greatest in low-income and middle-income countries, with up to a 2·5% loss in output by 2030. When total welfare losses are assessed (VLW), the present value of economic losses is estimated to be equivalent to 17% of 2010 GDP, or $14·5 trillion in the 175 countries assessed with this approach. Neoplasm and injury account

  8. 78 FR 20148 - Reporting Procedure for Mathematical Models Selected To Predict Heated Effluent Dispersion in...

    Science.gov (United States)

    2013-04-03

    ... procedure acceptable to the NRC staff for providing summary details of mathematical modeling methods used in... NUCLEAR REGULATORY COMMISSION [NRC-2013-0062] Reporting Procedure for Mathematical Models Selected... Regulatory Guide (RG) 4.4, ``Reporting Procedure for Mathematical Models Selected to Predict Heated Effluent...

  9. Enhanced Expression of Interferon-γ-Induced Antigen-Processing Machinery Components in a Spontaneously Occurring Cancer

    Directory of Open Access Journals (Sweden)

    Fulvia Cerruti

    2007-11-01

    Full Text Available In human tumors, changes in the surface expression and/or function of major histocompatibility complex (MHC class I antigens are frequently found and may provide malignant cells with a mechanism to escape control of the immune system. This altered human lymphocyte antigen (HLA class I phenotype can be caused by either structural alterations or dysregulation of genes encoding subunits of HLA class I antigens and/or components of the MHC class I antigen-processing machinery (APM. Herein we analyze the expression of several proteins involved in the generation of MHC class I epitopes in feline injection site sarcoma, a spontaneously occurring tumor in cats that is an informative model for the study of tumor biology in other species, including humans. Eighteen surgically removed primary fibrosarcoma lesions were analyzed, and an enhanced expression of two catalytic subunits of immunoproteasomes, PA28 and leucine aminopeptidase, was found in tumors compared to matched normal tissues. As a functional counterpart of these changes in protein levels, proteasomal activities were increased in tissue extracts from fibrosarcomas. Taken together, these results suggest that alterations in the APM system may account for reduced processing of selected tumor antigens and may potentially provide neoplastic fibroblasts with a mechanism for escape from T-cell recognition and destruction.

  10. Small-molecule intramimics of formin autoinhibition: a new strategy to target the cytoskeletal remodeling machinery in cancer cells.

    Science.gov (United States)

    Lash, L Leanne; Wallar, Bradley J; Turner, Julie D; Vroegop, Steven M; Kilkuskie, Robert E; Kitchen-Goosen, Susan M; Xu, H Eric; Alberts, Arthur S

    2013-11-15

    Although the cancer cell cytoskeleton is a clinically validated target, few new strategies have emerged for selectively targeting cell division by modulating the cytoskeletal structure, particularly ways that could avoid the cardiotoxic and neurotoxic effects of current agents such as taxanes. We address this gap by describing a novel class of small-molecule agonists of the mammalian Diaphanous (mDia)-related formins, which act downstream of Rho GTPases to assemble actin filaments, and their organization with microfilaments to establish and maintain cell polarity during migration and asymmetric division. GTP-bound Rho activates mDia family members by disrupting the interaction between the DID and DAD autoregulatory domains, which releases the FH2 domain to modulate actin and microtubule dynamics. In screening for DID-DAD disruptors that activate mDia, we identified two molecules called intramimics (IMM-01 and -02) that were sufficient to trigger actin assembly and microtubule stabilization, serum response factor-mediated gene expression, cell-cycle arrest, and apoptosis. In vivo analysis of IMM-01 and -02 established their ability to slow tumor growth in a mouse xenograft model of colon cancer. Taken together, our work establishes the use of intramimics and mDia-related formins as a new general strategy for therapeutic targeting of the cytoskeletal remodeling machinery of cancer cells. ©2013 AACR

  11. Bootstrap-after-bootstrap model averaging for reducing model uncertainty in model selection for air pollution mortality studies.

    Science.gov (United States)

    Roberts, Steven; Martin, Michael A

    2010-01-01

    Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context. To propose an extension (double BOOT) to a previously described bootstrap model-averaging procedure (BOOT) for use in time series studies of the association between PM and mortality. We compared double BOOT and BOOT with Bayesian model averaging (BMA) and a standard method of model selection [standard Akaike's information criterion (AIC)]. Actual time series data from the United States are used to conduct a simulation study to compare and contrast the performance of double BOOT, BOOT, BMA, and standard AIC. Double BOOT produced estimates of the effect of PM on mortality that have had smaller root mean squared error than did those produced by BOOT, BMA, and standard AIC. This performance boost resulted from estimates produced by double BOOT having smaller variance than those produced by BOOT and BMA. Double BOOT is a viable alternative to BOOT and BMA for producing estimates of the mortality effect of PM.

  12. Model-independent plot of dynamic PET data facilitates data interpretation and model selection.

    Science.gov (United States)

    Munk, Ole Lajord

    2012-02-21

    When testing new PET radiotracers or new applications of existing tracers, the blood-tissue exchange and the metabolism need to be examined. However, conventional plots of measured time-activity curves from dynamic PET do not reveal the inherent kinetic information. A novel model-independent volume-influx plot (vi-plot) was developed and validated. The new vi-plot shows the time course of the instantaneous distribution volume and the instantaneous influx rate. The vi-plot visualises physiological information that facilitates model selection and it reveals when a quasi-steady state is reached, which is a prerequisite for the use of the graphical analyses by Logan and Gjedde-Patlak. Both axes of the vi-plot have direct physiological interpretation, and the plot shows kinetic parameter in close agreement with estimates obtained by non-linear kinetic modelling. The vi-plot is equally useful for analyses of PET data based on a plasma input function or a reference region input function. The vi-plot is a model-independent and informative plot for data exploration that facilitates the selection of an appropriate method for data analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. The development of natural circulation operation support program for ship nuclear power machinery

    International Nuclear Information System (INIS)

    Hao, Jianli; Chen, Wenzhen; Chen, Zhiyun

    2012-01-01

    Highlights: ► The natural circulation under various ocean and ship motion conditions is studied. ► A natural circulation operation support computer program (NCOSP) is developed with Simulink. ► The NCOSP program has the merit of easy input preparation, fast and accurate simulation. ► The NCOSP is suitable for the fast parameter simulation of ship nuclear power machinery. -- Abstract: The existing simulation program of ship nuclear power machinery (SNPM) cannot adequately deal with the natural circulation (NC) operation and the effects of various ocean conditions and ship motion. Aiming at the problem, the natural circulation operation support computer program for SNPM is developed, in which the momentum conservation equation of the primary loop, some heat transfer and flow resistance models and equations are modified for the various ocean conditions and ship motion. The additional pressure loss model and effective height model for the control volume in the gyration movement, simple harmonic rolling and pitching movements are also discussed in the paper. Furthermore, the transient response to load change under NC conditions is analyzed by the developed program. The results are compared with those predicted by the modified RELAP5/mod3.2 code. It is shown that the natural circulation operation support program (NCOSP) is simple in the input preparation, runs fast and has a satisfactory precision, and is therefore very suitable for the operating field support of SNPM under the conditions of NC.

  14. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

    Science.gov (United States)

    Lee, Jay; Wu, Fangji; Zhao, Wenyu; Ghaffari, Masoud; Liao, Linxia; Siegel, David

    2014-01-01

    Much research has been conducted in prognostics and health management (PHM), an emerging field in mechanical engineering that is gaining interest from both academia and industry. Most of these efforts have been in the area of machinery PHM, resulting in the development of many algorithms for this particular application. The majority of these algorithms concentrate on applications involving common rotary machinery components, such as bearings and gears. Knowledge of this prior work is a necessity for any future research efforts to be conducted; however, there has not been a comprehensive overview that details previous and on-going efforts in PHM. In addition, a systematic method for developing and deploying a PHM system has yet to be established. Such a method would enable rapid customization and integration of PHM systems for diverse applications. To address these gaps, this paper provides a comprehensive review of the PHM field, followed by an introduction of a systematic PHM design methodology, 5S methodology, for converting data to prognostics information. This methodology includes procedures for identifying critical components, as well as tools for selecting the most appropriate algorithms for specific applications. Visualization tools are presented for displaying prognostics information in an appropriate fashion for quick and accurate decision making. Industrial case studies are included in this paper to show how this methodology can help in the design of an effective PHM system.

  15. Improved spectral kurtosis with adaptive redundant multiwavelet packet and its applications for rotating machinery fault detection

    International Nuclear Information System (INIS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Yuan, Jing

    2012-01-01

    Rotating machinery fault detection is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of non-stationarity and nonlinearity, the detection and extraction of the fault feature turn into a challenging task. Therefore, a novel method called improved spectral kurtosis (ISK) with adaptive redundant multiwavelet packet (ARMP) is proposed for this task. Spectral kurtosis (SK) has been proved to be a powerful tool to detect and characterize the non-stationary signal. To improve the SK in filter limitation and enhance the resolution of spectral analysis as well as match fault feature optimally, the ARMP is introduced into the SK. Moreover, since kurtosis does not reflect the actual trend of periodic impulses, the SK is improved by incorporating an evaluation index called envelope spectrum entropy as supplement. The proposed method is applied to the rolling element bearing and gear fault detection to validate its reliability and effectiveness. Compared with the conventional frequency spectrum, envelope spectrum, original SK and some single wavelet methods, the results indicate that it could improve the accuracy of frequency-band selection and enhance the ability of rotating machinery fault detection. (paper)

  16. The Selection of Turbulence Models for Prediction of Room Airflow

    DEFF Research Database (Denmark)

    Nielsen, Peter V.

    This paper discusses the use of different turbulence models and their advantages in given situations. As an example, it is shown that a simple zero-equation model can be used for the prediction of special situations as flow with a low level of turbulence. A zero-equation model with compensation...

  17. Stem biomass and volume models of selected tropical tree species ...

    African Journals Online (AJOL)

    Stem biomass and stem volume were modelled as a function of diameter (at breast height; Dbh) and stem height (height to the crown base). Logarithmic models are presented that utilise Dbh and height data to predict tree component biomass and stem volumes. Alternative models are given that afford prediction based on ...

  18. Model selection criteria : how to evaluate order restrictions

    NARCIS (Netherlands)

    Kuiper, R.M.

    2012-01-01

    Researchers often have ideas about the ordering of model parameters. They frequently have one or more theories about the ordering of the group means, in analysis of variance (ANOVA) models, or about the ordering of coefficients corresponding to the predictors, in regression models.A researcher might

  19. The Living Dead: Transformative Experiences in Modelling Natural Selection

    Science.gov (United States)

    Petersen, Morten Rask

    2017-01-01

    This study considers how students change their coherent conceptual understanding of natural selection through a hands-on simulation. The results show that most students change their understanding. In addition, some students also underwent a transformative experience and used their new knowledge in a leisure time activity. These transformative…

  20. Modelling the negative effects of landscape fragmentation on habitat selection

    NARCIS (Netherlands)

    Langevelde, van F.

    2015-01-01

    Landscape fragmentation constrains movement of animals between habitat patches. Fragmentation may, therefore, limit the possibilities to explore and select the best habitat patches, and some animals may have to cope with low-quality patches due to these movement constraints. If so, these individuals

  1. Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions

    DEFF Research Database (Denmark)

    Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.

    2011-01-01

    the attributes in the database into small, usually two-dimensional distributions. We describe several optimizations that can make selectivity estimation highly efficient, and we present a complete implementation inside PostgreSQL’s query optimizer. Experimental results indicate an order of magnitude better...

  2. Selection bias in species distribution models: An econometric approach on forest trees based on structural modeling

    Science.gov (United States)

    Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.

    2014-12-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents

  3. Induction of rapid and selective cell necrosis in Drosophila using Bacillus thuringiensis Cry toxin and its silkworm receptor

    OpenAIRE

    Obata, Fumiaki; Tanaka, Shiho; Kashio, Soshiro; Tsujimura, Hidenobu; Sato, Ryoichi; Miura, Masayuki

    2015-01-01

    Background Genetic ablation of target cells is a powerful tool to study the origins and functions of cells, tissue regeneration, or pathophysiology in a human disease model in vivo. Several methods for selective cell ablation by inducing apoptosis have been established, using exogenous toxins or endogenous proapoptotic genes. However, their application is limited to cells with intact apoptotic machinery. Results Herein, we established a method for inducing rapid and selective cell necrosis by...

  4. Model selection for integrated pest management with stochasticity.

    Science.gov (United States)

    Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel

    2018-04-07

    In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

    Directory of Open Access Journals (Sweden)

    Mark N Read

    2016-09-01

    Full Text Available The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto

  6. Risk assessments and safe machinery ensuring compliance with the EU directives

    CERN Document Server

    Jespen, Torben

    2016-01-01

    This book describes the prerequisites for the placing on the market and the safe use of machinery in compliance with the relevant EU Directives, especially the Machinery Directive 2006/42. It provides readers with high-level knowledge concerning the Essential Health and Safety Requirements (EHSR) that machinery must fulfill. The approach and principles of the Machinery Directive were most recently made worldwide acknowledged in the ILO code of practice on safe machinery, released in 2013. The book addresses that code, as well as providing valuable insight into other EU Product and Workplace legislation. Focusing on the key aspect of safe machinery, the “machinery safety risk assessment”, which allows readers to better understand the more difficult aspects of risk assessments, the book equips readers to tackle problems at the manufacturing stage and in different use scenarios, introducing them to risk reduction techniques and functional safety aspects.

  7. Vibration vector monitoring of rotating machinery: A predictive/preventative maintenance technique

    International Nuclear Information System (INIS)

    Humes, B.R.

    1990-01-01

    Monitoring of overall vibration amplitudes to indicate machinery faults is a standard practice in most industries. The appearance of shaft cracks in machines retrofitted for extended life have prompted development of higher levels of machinery monitoring. Part 1 of this paper discusses vibration vector monitoring for machinery malfunction prediction and failure prevention. Machinery faults which can be diagnosed by this type of monitoring, such as rotor rubs, loose parts, shaft cracks, ..., are presented along with their most common characteristics. The newest, most effective methods of permanent machinery monitoring are presented and critiqued. An extensive case history is presented in Part 2 in which a potentially disastrous machinery fault was predicted using vibration vector monitoring and analysis. The addition of vector monitoring to the normal, overall vibration monitoring proved more effective in diagnosing the machinery fault and predicting impending failure

  8. Investigation and Development of the Thermal Preparation System of the Trailbuilder Machinery Hydraulic Actuator

    Science.gov (United States)

    Konev, V.; Polovnikov, E.; Krut, O.; Merdanov, Sh; Zakirzakov, G.

    2017-07-01

    It’s determined that the main part of trailbuilders operated in the North is the technology equipped by the hydraulic actuator. Further development of the northern territories will demand using of various means and ways machinery thermal preparation, and also the machinery of the northern fulfillment. On this basis problems in equipment operation are defined. One of the main is efficiency supplying of a hydraulic actuator. On the basis of the operating conditions’ analysis of trailbuilder hydraulic actuator operation it is determined, that under low negative temperatures the means of thermal preparation are necessary. The existing systems warm up only a hydraulic tank or warming up of the hydro equipment before the machinery operation is carried out under loading with intensive wears. Thus, with the purpose to raise the efficiency of thermal hydraulic actuator, operated far from stationary bases autonomous, energy saving, not expensive in creation and operation systems are necessary. In accordance with the analysis of means and ways of the thermal preparation of the hydraulic actuator and the thermal balance calculations of the (internal) combustion engine the system of the hydraulic actuator heating is offered and is being investigated. It contains a local hydraulic actuator warming up and the system of internal combustion engine heat utilization. Within research operation conditions of the local hydraulic actuator heating are viewed and determined, taking into account constructive changes to the local hydraulic actuator heating. Mathematical modelling of the heat technical process in the modernized hydraulic actuator is considered. As a result temperature changes of the heat-transfer and the hydraulic cylinder in time are determined. To check the theoretical researches and to define dependences on hydraulic actuator warming up, the experimental installation is made. It contains the measuring equipment, a small tank with the heat exchanger of the burnt gases

  9. Selected Constitutive Models for Simulating the Hygromechanical Response of Wood

    DEFF Research Database (Denmark)

    Frandsen, Henrik Lund

    , the boundary conditions are discussed based on discrepancies found for similar research on moisture transport in paper stacks. Paper III: A new sorption hysteresis model suitable for implementation into a numerical method is developed. The prevailing so-called scanning curves are modeled by closed......-form expressions, which only depend on the current relative humidity of the air and current moisture content of the wood. Furthermore, the expressions for the scanning curves are formulated independent of the temperature and species-dependent boundary curves. Paper IV: The sorption hysteresis model developed...... are discussed. The constitutive moisture transport models are coupled with a heat transport model yielding terms that describe the so-called Dufour and Sorret effects, however, with multiple phases and hysteresis included. Paper VII: In this paper the modeling of transverse couplings in creep of wood...

  10. Sequential Markov chain Monte Carlo filter with simultaneous model selection for electrocardiogram signal modeling.

    Science.gov (United States)

    Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia

    2012-01-01

    Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.

  11. Selected Aspects of Computer Modeling of Reinforced Concrete Structures

    Directory of Open Access Journals (Sweden)

    Szczecina M.

    2016-03-01

    Full Text Available The paper presents some important aspects concerning material constants of concrete and stages of modeling of reinforced concrete structures. The problems taken into account are: a choice of proper material model for concrete, establishing of compressive and tensile behavior of concrete and establishing the values of dilation angle, fracture energy and relaxation time for concrete. Proper values of material constants are fixed in simple compression and tension tests. The effectiveness and correctness of applied model is checked on the example of reinforced concrete frame corners under opening bending moment. Calculations are performed in Abaqus software using Concrete Damaged Plasticity model of concrete.

  12. Bayesian model selection validates a biokinetic model for zirconium processing in humans

    Science.gov (United States)

    2012-01-01

    Background In radiation protection, biokinetic models for zirconium processing are of crucial importance in dose estimation and further risk analysis for humans exposed to this radioactive substance. They provide limiting values of detrimental effects and build the basis for applications in internal dosimetry, the prediction for radioactive zirconium retention in various organs as well as retrospective dosimetry. Multi-compartmental models are the tool of choice for simulating the processing of zirconium. Although easily interpretable, determining the exact compartment structure and interaction mechanisms is generally daunting. In the context of observing the dynamics of multiple compartments, Bayesian methods provide efficient tools for model inference and selection. Results We are the first to apply a Markov chain Monte Carlo approach to compute Bayes factors for the evaluation of two competing models for zirconium processing in the human body after ingestion. Based on in vivo measurements of human plasma and urine levels we were able to show that a recently published model is superior to the standard model of the International Commission on Radiological Protection. The Bayes factors were estimated by means of the numerically stable thermodynamic integration in combination with a recently developed copula-based Metropolis-Hastings sampler. Conclusions In contrast to the standard model the novel model predicts lower accretion of zirconium in bones. This results in lower levels of noxious doses for exposed individuals. Moreover, the Bayesian approach allows for retrospective dose assessment, including credible intervals for the initially ingested zirconium, in a significantly more reliable fashion than previously possible. All methods presented here are readily applicable to many modeling tasks in systems biology. PMID:22863152

  13. A model selection support system for numerical simulations of nuclear thermal-hydraulics

    International Nuclear Information System (INIS)

    Gofuku, Akio; Shimizu, Kenji; Sugano, Keiji; Yoshikawa, Hidekazu; Wakabayashi, Jiro

    1990-01-01

    In order to execute efficiently a dynamic simulation of a large-scaled engineering system such as a nuclear power plant, it is necessary to develop intelligent simulation support system for all phases of the simulation. This study is concerned with the intelligent support for the program development phase and is engaged in the adequate model selection support method by applying AI (Artificial Intelligence) techniques to execute a simulation consistent with its purpose and conditions. A proto-type expert system to support the model selection for numerical simulations of nuclear thermal-hydraulics in the case of cold leg small break loss-of-coolant accident of PWR plant is now under development on a personal computer. The steps to support the selection of both fluid model and constitutive equations for the drift flux model have been developed. Several cases of model selection were carried out and reasonable model selection results were obtained. (author)

  14. Research Using Virtual Reality: Mobile Machinery Safety in the 21st Century

    Directory of Open Access Journals (Sweden)

    Giselle P. Delgado

    2013-04-01

    Full Text Available Whole-body vibration is a significant health risk for between 4% and 7% of the work force in North America. In addition, many factors compound the health risks of heavy machinery operators. For example, twisted trunk and neck postures stiffen the spine and increase the transmission of vibration to the head. Similarly, workers adopt awkward postures in order to gain appropriate lines of sight for machine operations. Although the relative contribution of these various issues can be evaluated in field studies and models, we propose that virtual reality is a powerful medium for investigating issues related to health and safety in mining machine operators. We have collected field data of posture and vibration, as well as visual environment, for a forklift operating in a warehouse. This paper describes the process and outcome of this field data collection, and provides a discussion on the next steps to develop and test the virtual reality model to enable laboratory testing. Our ongoing studies will evaluate the interplay between posture and vibration under conditions replicating routine heavy machinery operations, such as underground mining.

  15. Optimal selection of Orbital Replacement Unit on-orbit spares - A Space Station system availability model

    Science.gov (United States)

    Schwaab, Douglas G.

    1991-01-01

    A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.

  16. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

    Science.gov (United States)

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

  17. Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems

    KAUST Repository

    Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim

    2014-01-01

    A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using

  18. Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

    Science.gov (United States)

    Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

    2017-12-27

    Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP

  19. Varying Coefficient Panel Data Model in the Presence of Endogenous Selectivity and Fixed Effects

    OpenAIRE

    Malikov, Emir; Kumbhakar, Subal C.; Sun, Yiguo

    2013-01-01

    This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the selection equation obta...

  20. Required experimental accuracy to select between supersymmetrical models

    Science.gov (United States)

    Grellscheid, David

    2004-03-01

    We will present a method to decide a priori whether various supersymmetrical scenarios can be distinguished based on sparticle mass data alone. For each model, a scan over all free SUSY breaking parameters reveals the extent of that model's physically allowed region of sparticle-mass-space. Based on the geometrical configuration of these regions in mass-space, it is possible to obtain an estimate of the required accuracy of future sparticle mass measurements to distinguish between the models. We will illustrate this algorithm with an example. This talk is based on work done in collaboration with B C Allanach (LAPTH, Annecy) and F Quevedo (DAMTP, Cambridge).

  1. Development of Solar Drying Model for Selected Cambodian Fish Species

    OpenAIRE

    Hubackova, Anna; Kucerova, Iva; Chrun, Rithy; Chaloupkova, Petra; Banout, Jan

    2014-01-01

    A solar drying was investigated as one of perspective techniques for fish processing in Cambodia. The solar drying was compared to conventional drying in electric oven. Five typical Cambodian fish species were selected for this study. Mean solar drying temperature and drying air relative humidity were 55.6°C and 19.9%, respectively. The overall solar dryer efficiency was 12.37%, which is typical for natural convection solar dryers. An average evaporative capacity of solar dryer was 0.049 kg·h...

  2. Identification of landscape features influencing gene flow: How useful are habitat selection models?

    Science.gov (United States)

    Gretchen H. Roffler; Michael K. Schwartz; Kristine Pilgrim; Sandra L. Talbot; George K. Sage; Layne G. Adams; Gordon Luikart

    2016-01-01

    Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is...

  3. Human Machine Interaction by Simulation of Dynamics of Construction Machinery

    DEFF Research Database (Denmark)

    Langer, Thomas Heegaard

    -body vibration exposure was more than 20 percent and at the same time the fuel consumption was reduced significant. Training of operators is hence beneficial for both employees and employers of the construction industry. The whole-body vibration exposure on operators of dump trucks are dominated by off-road......This industrial Ph.D. project concerns whole-body vibrations in human operated construction machinery. The emissions of these vibrations is closely related to the subjective experience of comfort and in some cases these vibrations can occur in a level which can cause the operator back disorders...

  4. Recent Advances in Precision Machinery and Manufacturing Technology

    DEFF Research Database (Denmark)

    Liu, Chien-Hung; Hsieh, Wen-Hsiang; Chang, Zong-Yu

    2014-01-01

    Precision machinery and manufacturing technology are be- coming more important in current and future technologies. New knowledge in this field will aid in the advancement of various technologies that are needed to gain industrial competitiveness. To this end, the special issue aims to disseminate...... the latest advancements of relevant fundamental and applied research works of high quality to the inter- national community. The topics of the accepted articles in the special issue include precision manufacturing pro- cesses, measurements and control, robotics and automation, machine tools, advanced...

  5. Optimal covariance selection for estimation using graphical models

    OpenAIRE

    Vichik, Sergey; Oshman, Yaakov

    2011-01-01

    We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditiona...

  6. A Neuronal Network Model for Pitch Selectivity and Representation

    OpenAIRE

    Huang, Chengcheng; Rinzel, John

    2016-01-01

    Pitch is a perceptual correlate of periodicity. Sounds with distinct spectra can elicit the same pitch. Despite the importance of pitch perception, understanding the cellular mechanism of pitch perception is still a major challenge and a mechanistic model of pitch is lacking. A multi-stage neuronal network model is developed for pitch frequency estimation using biophysically-based, high-resolution coincidence detector neurons. The neuronal units respond only to highly coincident input among c...

  7. Fuzzy Multicriteria Model for Selection of Vibration Technology

    Directory of Open Access Journals (Sweden)

    María Carmen Carnero

    2016-01-01

    Full Text Available The benefits of applying the vibration analysis program are well known and have been so for decades. A large number of contributions have been produced discussing new diagnostic, signal treatment, technical parameter analysis, and prognosis techniques. However, to obtain the expected benefits from a vibration analysis program, it is necessary to choose the instrumentation which guarantees the best results. Despite its importance, in the literature, there are no models to assist in taking this decision. This research describes an objective model using Fuzzy Analytic Hierarchy Process (FAHP to make a choice of the most suitable technology among portable vibration analysers. The aim is to create an easy-to-use model for processing, manufacturing, services, and research organizations, to guarantee adequate decision-making in the choice of vibration analysis technology. The model described recognises that judgements are often based on ambiguous, imprecise, or inadequate information that cannot provide precise values. The model incorporates judgements from several decision-makers who are experts in the field of vibration analysis, maintenance, and electronic devices. The model has been applied to a Health Care Organization.

  8. Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

    Science.gov (United States)

    Harwati

    2017-06-01

    Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

  9. Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

    Science.gov (United States)

    Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

    2018-07-12

    selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Selecting a climate model subset to optimise key ensemble properties

    Directory of Open Access Journals (Sweden)

    N. Herger

    2018-02-01

    Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  11. Selecting a climate model subset to optimise key ensemble properties

    Science.gov (United States)

    Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

    2018-02-01

    End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  12. Selected developments and applications of Leontief models in industrial ecology

    International Nuclear Information System (INIS)

    Stroemman, Anders Hammer

    2005-01-01

    Thesis Outline: This thesis investigates issues of environmental repercussions on processes of three spatial scales; a single process plant, a regional value chain and the global economy. The first paper investigates environmental repercussions caused by a single process plant using an open Leontief model with combined physical and monetary units in what is commonly referred to as a hybrid life cycle model. Physical capital requirements are treated as any other good. Resources and environmental stressors, thousands in total, are accounted for and assessed by aggregation using standard life cycle impact assessment methods. The second paper presents a methodology for establishing and combining input-output matrices and life-cycle inventories in a hybrid life cycle inventory. Information contained within different requirements matrices are combined and issues of double counting that arise are addressed and methods for eliminating these are developed and presented. The third paper is an extension of the first paper. Here the system analyzed is increased from a single plant and component in the production network to a series of nodes, constituting a value chain. The hybrid framework proposed in paper two is applied to analyze the use of natural gas, methanol and hydrogen as transportation fuels. The fourth paper presents the development of a World Trade Model with Bilateral Trade, an extension of the World Trade Model (Duchin, 2005). The model is based on comparative advantage and is formulated as a linear program. It endogenously determines the regional output of sectors and bilateral trade flows between regions. The model may be considered a Leontief substitution model where substitution of production is allowed between regions. The primal objective of the model requires the minimization of global factor costs. The fifth paper demonstrates how the World Trade Model with Bilateral Trade can be applied to address questions relevant for industrial ecology. The model is

  13. Model validity and frequency band selection in operational modal analysis

    Science.gov (United States)

    Au, Siu-Kui

    2016-12-01

    Experimental modal analysis aims at identifying the modal properties (e.g., natural frequencies, damping ratios, mode shapes) of a structure using vibration measurements. Two basic questions are encountered when operating in the frequency domain: Is there a mode near a particular frequency? If so, how much spectral data near the frequency can be included for modal identification without incurring significant modeling error? For data with high signal-to-noise (s/n) ratios these questions can be addressed using empirical tools such as singular value spectrum. Otherwise they are generally open and can be challenging, e.g., for modes with low s/n ratios or close modes. In this work these questions are addressed using a Bayesian approach. The focus is on operational modal analysis, i.e., with 'output-only' ambient data, where identification uncertainty and modeling error can be significant and their control is most demanding. The approach leads to 'evidence ratios' quantifying the relative plausibility of competing sets of modeling assumptions. The latter involves modeling the 'what-if-not' situation, which is non-trivial but is resolved by systematic consideration of alternative models and using maximum entropy principle. Synthetic and field data are considered to investigate the behavior of evidence ratios and how they should be interpreted in practical applications.

  14. Selecting salient frames for spatiotemporal video modeling and segmentation.

    Science.gov (United States)

    Song, Xiaomu; Fan, Guoliang

    2007-12-01

    We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

  15. Statistical mechanics of sparse generalization and graphical model selection

    International Nuclear Information System (INIS)

    Lage-Castellanos, Alejandro; Pagnani, Andrea; Weigt, Martin

    2009-01-01

    One of the crucial tasks in many inference problems is the extraction of an underlying sparse graphical model from a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penalty term, the L p norm of the model parameters, with p≤1 for efficient dilution. Here we propose a statistical mechanics analysis of the problem in the setting of perceptron memorization and generalization. Using a replica approach, we are able to evaluate the relative performance of naive dilution (obtained by learning without dilution, following by applying a threshold to the model parameters), L 1 dilution (which is frequently used in convex optimization) and L 0 dilution (which is optimal but computationally hard to implement). Whereas both L p diluted approaches clearly outperform the naive approach, we find a small region where L 0 works almost perfectly and strongly outperforms the simpler to implement L 1 dilution

  16. Selection of References in Wind Turbine Model Predictive Control Design

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Hovgaard, Tobias

    2015-01-01

    a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...

  17. SELECT NUMERICAL METHODS FOR MODELING THE DYNAMICS SYSTEMS

    Directory of Open Access Journals (Sweden)

    Tetiana D. Panchenko

    2016-07-01

    Full Text Available The article deals with the creation of methodical support for mathematical modeling of dynamic processes in elements of the systems and complexes. As mathematical models ordinary differential equations have been used. The coefficients of the equations of the models can be nonlinear functions of the process. The projection-grid method is used as the main tool. It has been described iterative method algorithms taking into account the approximate solution prior to the first iteration and proposed adaptive control computing process. The original method of estimation error in the calculation solutions as well as for a given level of error of the technique solutions purpose adaptive method for solving configuration parameters is offered. A method for setting an adaptive method for solving the settings for a given level of error is given. The proposed method can be used for distributed computing.

  18. Mathematical Model of the Emissions of a selected vehicle

    Directory of Open Access Journals (Sweden)

    Matušů Radim

    2014-10-01

    Full Text Available The article addresses the quantification of exhaust emissions from gasoline engines during transient operation. The main targeted emissions are carbon monoxide and carbon dioxide. The result is a mathematical model describing the production of individual emissions components in all modes (static and dynamic. It also describes the procedure for the determination of emissions from the engine’s operating parameters. The result is compared with other possible methods of measuring emissions. The methodology is validated using the data from an on-road measurement. The mathematical model was created on the first route and validated on the second route.

  19. An Optimization Model For Strategy Decision Support to Select Kind of CPO’s Ship

    Science.gov (United States)

    Suaibah Nst, Siti; Nababan, Esther; Mawengkang, Herman

    2018-01-01

    The selection of marine transport for the distribution of crude palm oil (CPO) is one of strategy that can be considered in reducing cost of transport. The cost of CPO’s transport from one area to CPO’s factory located at the port of destination may affect the level of CPO’s prices and the number of demands. In order to maintain the availability of CPO a strategy is required to minimize the cost of transporting. In this study, the strategy used to select kind of charter ships as barge or chemical tanker. This study aims to determine an optimization model for strategy decision support in selecting kind of CPO’s ship by minimizing costs of transport. The select of ship was done randomly, so that two-stage stochastic programming model was used to select the kind of ship. Model can help decision makers to select either barge or chemical tanker to distribute CPO.

  20. Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction.

    Science.gov (United States)

    Tetteh, Antonia Y; Sun, Katherine H; Hung, Chiu-Yueh; Kittur, Farooqahmed S; Ibeanu, Gordon C; Williams, Daniel; Xie, Jiahua

    2014-01-01

    Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se(0)), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼ 50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼ 30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.

  1. Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

    Directory of Open Access Journals (Sweden)

    Antonia Y. Tetteh

    2014-01-01

    Full Text Available Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0, but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3 treatment and then used quantitative real-time PCR (qRT-PCR to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.

  2. Effects of high-gradient magnetic fields on living cell machinery

    International Nuclear Information System (INIS)

    Zablotskii, V; Lunov, O; Kubinova, S; Polyakova, T; Dejneka, A; Sykova, E

    2016-01-01

    A general interest in biomagnetic effects is related to fundamental studies of the influence of magnetic fields on living objects on the cellular and whole organism levels. Emerging technologies offer new directions for the use of high-gradient magnetic fields to control cell machinery and to understand the intracellular biological processes of the emerging field of nanomedicine. In this review we aim at highlighting recent advances made in identifying fundamental mechanisms by which magnetic gradient forces act on cell fate specification and cell differentiation. The review also provides an analysis of the currently available magnetic systems capable of generating magnetic fields with spatial gradients of up to 10 MT m −1 , with the focus on their suitability for use in cell therapy. Relationships between experimental factors and underlying biophysical mechanisms and assumptions that would ultimately lead to a deeper understanding of cell machinery and the development of more predictive models for the evaluation of the effects of magnetic fields on cells, tissue and organisms are comprehensively discussed. (topical review)

  3. The pupylation machinery is involved in iron homeostasis by targeting the iron storage protein ferritin.

    Science.gov (United States)

    Küberl, Andreas; Polen, Tino; Bott, Michael

    2016-04-26

    The balance of sufficient iron supply and avoidance of iron toxicity by iron homeostasis is a prerequisite for cellular metabolism and growth. Here we provide evidence that, in Actinobacteria, pupylation plays a crucial role in this process. Pupylation is a posttranslational modification in which the prokaryotic ubiquitin-like protein Pup is covalently attached to a lysine residue in target proteins, thus resembling ubiquitination in eukaryotes. Pupylated proteins are recognized and unfolded by a dedicated AAA+ ATPase (Mycobacterium proteasomal AAA+ ATPase; ATPase forming ring-shaped complexes). In Mycobacteria, degradation of pupylated proteins by the proteasome serves as a protection mechanism against several stress conditions. Other bacterial genera capable of pupylation such as Corynebacterium lack a proteasome, and the fate of pupylated proteins is unknown. We discovered that Corynebacterium glutamicum mutants lacking components of the pupylation machinery show a strong growth defect under iron limitation, which was caused by the absence of pupylation and unfolding of the iron storage protein ferritin. Genetic and biochemical data support a model in which the pupylation machinery is responsible for iron release from ferritin independent of degradation.

  4. The Optimal Portfolio Selection Model under g-Expectation

    Directory of Open Access Journals (Sweden)

    Li Li

    2014-01-01

    complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.

  5. An ecosystem model for tropical forest disturbance and selective logging

    Science.gov (United States)

    Maoyi Huang; Gregory P. Asner; Michael Keller; Joseph A. Berry

    2008-01-01

    [1] A new three-dimensional version of the Carnegie-Ames-Stanford Approach (CASA) ecosystem model (CASA-3D) was developed to simulate regional carbon cycling in tropical forest ecosystems after disturbances such as logging. CASA-3D has the following new features: (1) an alternative approach for calculating absorbed photosynthetically active radiation (APAR) using new...

  6. Process chain modeling and selection in an additive manufacturing context

    DEFF Research Database (Denmark)

    Thompson, Mary Kathryn; Stolfi, Alessandro; Mischkot, Michael

    2016-01-01

    This paper introduces a new two-dimensional approach to modeling manufacturing process chains. This approach is used to consider the role of additive manufacturing technologies in process chains for a part with micro scale features and no internal geometry. It is shown that additive manufacturing...... evolving fields like additive manufacturing....

  7. Selecting Tools to Model Integer and Binomial Multiplication

    Science.gov (United States)

    Pratt, Sarah Smitherman; Eddy, Colleen M.

    2017-01-01

    Mathematics teachers frequently provide concrete manipulatives to students during instruction; however, the rationale for using certain manipulatives in conjunction with concepts may not be explored. This article focuses on area models that are currently used in classrooms to provide concrete examples of integer and binomial multiplication. The…

  8. Modeling Selected Climatic Variables in Ibadan, Oyo State, Nigeria ...

    African Journals Online (AJOL)

    PROF. O. E. OSUAGWU

    2013-09-01

    Sep 1, 2013 ... The aim of this study was fitting the modified generalized burr density function to total rainfall and temperature data obtained from the meteorological unit in the Department of. Environmental Modelling and Management of the Forestry Research Institute of Nigeria. (FRIN) in Ibadan, Oyo State, Nigeria.

  9. Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials

    DEFF Research Database (Denmark)

    Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2010-01-01

    Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficul...

  10. THERMODYNAMIC MODEL AND VISCOSITY OF SELECTED ZIRCONIA CONTAINING SILICATE GLASSES

    Directory of Open Access Journals (Sweden)

    MÁRIA CHROMČÍKOVÁ

    2013-03-01

    Full Text Available The compositional dependence of viscosity, and viscous flow activation energy of glasses with composition xNa2O∙(15-x K2O∙yCaO∙(10-yZnO∙zZrO2∙(75-zSiO2 (x = 0, 7.5, 15; y = 0, 5, 10; z = 0, 1, 3, 5, 7 was analyzed. The studied glasses were described by the thermodynamic model of Shakhmatkin and Vedishcheva considering the glass as an equilibrium ideal solution of species with stoichiometry given by the composition of stable crystalline phases of respective glass forming system. Viscosity-composition relationships were described by the regression approach considering the viscous flow activation energy and the particular isokome temperature as multilinear function of equilibrium molar amounts of system components. The classical approach where the mole fractions of individual oxides are considered as independent variables was compared with the thermodynamic model. On the basis of statistical analysis there was proved that the thermodynamic model is able to describe the composition property relationships with higher reliability. Moreover, due its better physical justification, thermodynamic model can be even used for predictive purposes.

  11. Transportation of part supply improvement in agricultural machinery assembly plant

    Science.gov (United States)

    Saysaman, Anusit; Chutima, Parames

    2018-02-01

    This research focused on the problem caused by the transportation of part supply in agricultural machinery assembly plant in Thailand, which is one of the processes that are critical to the whole production process. If poorly managed, it will affect transportation of part supply, the emergence of sink cost, quality problems, and the ability to respond to the needs of the customers in time. Since the competition in the agricultural machinery market is more intense, the efficiency of part transportation process has to be improved. In this study, the process of transporting parts of the plant was studied and it was found that the efficiency of the process of transporting parts from the sub assembly line to its main assembly line was 83%. The approach to the performance improvement is done by using the Lean tool to limit wastes based on the ECRS principle and applying pull production system by changing the transportation method to operate as milkrun for transportation of parts to synchronize with the part demands of the main assembly line. After the transportation of parts from sub-assembly line to the main assembly line was improved, the efficiency raised to 98% and transportation process cost was saved to 540,000 Baht per year.

  12. Model Selection and Accounting for Model Uncertainty in Graphical Models Using OCCAM’s Window

    Science.gov (United States)

    1991-07-22

    mental work; C, strenuous physical work; D, systolic blood pressure: E. ratio of 13 and Qt proteins; F, family anamnesis of coronary heart disease...of F, family anamnesis . The models are shown in Figure 4. 12 Table 1: Risk factors for Coronary lfeart Disea:W B No Yes A No Yes No Yes F E D C...a link from smoking (A) to systolic blood pressure (D). There is decisive evidence in favour of the marginal independence of family anamnesis of

  13. On a Robust MaxEnt Process Regression Model with Sample-Selection

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2018-04-01

    Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

  14. A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

    Science.gov (United States)

    Zhao, Yize; Kang, Jian; Yu, Tianwei

    2014-06-01

    It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.

  15. A model of directional selection applied to the evolution of drug resistance in HIV-1.

    Science.gov (United States)

    Seoighe, Cathal; Ketwaroo, Farahnaz; Pillay, Visva; Scheffler, Konrad; Wood, Natasha; Duffet, Rodger; Zvelebil, Marketa; Martinson, Neil; McIntyre, James; Morris, Lynn; Hide, Winston

    2007-04-01

    Understanding how pathogens acquire resistance to drugs is important for the design of treatment strategies, particularly for rapidly evolving viruses such as HIV-1. Drug treatment can exert strong selective pressures and sites within targeted genes that confer resistance frequently evolve far more rapidly than the neutral rate. Rapid evolution at sites that confer resistance to drugs can be used to help elucidate the mechanisms of evolution of drug resistance and to discover or corroborate novel resistance mutations. We have implemented standard maximum likelihood methods that are used to detect diversifying selection and adapted them for use with serially sampled reverse transcriptase (RT) coding sequences isolated from a group of 300 HIV-1 subtype C-infected women before and after single-dose nevirapine (sdNVP) to prevent mother-to-child transmission. We have also extended the standard models of codon evolution for application to the detection of directional selection. Through simulation, we show that the directional selection model can provide a substantial improvement in sensitivity over models of diversifying selection. Five of the sites within the RT gene that are known to harbor mutations that confer resistance to nevirapine (NVP) strongly supported the directional selection model. There was no evidence that other mutations that are known to confer NVP resistance were selected in this cohort. The directional selection model, applied to serially sampled sequences, also had more power than the diversifying selection model to detect selection resulting from factors other than drug resistance. Because inference of selection from serial samples is unlikely to be adversely affected by recombination, the methods we describe may have general applicability to the analysis of positive selection affecting recombining coding sequences when serially sampled data are available.

  16. Parameter Selection and Performance Analysis of Mobile Terminal Models Based on Unity3D

    Institute of Scientific and Technical Information of China (English)

    KONG Li-feng; ZHAO Hai-ying; XU Guang-mei

    2014-01-01

    Mobile platform is now widely seen as a promising multimedia service with a favorable user group and market prospect. To study the influence of mobile terminal models on the quality of scene roaming, a parameter setting platform of mobile terminal models is established to select the parameter selection and performance index on different mobile platforms in this paper. This test platform is established based on model optimality principle, analyzing the performance curve of mobile terminals in different scene models and then deducing the external parameter of model establishment. Simulation results prove that the established test platform is able to analyze the parameter and performance matching list of a mobile terminal model.

  17. Evaluation and comparison of alternative fleet-level selective maintenance models

    International Nuclear Information System (INIS)

    Schneider, Kellie; Richard Cassady, C.

    2015-01-01

    Fleet-level selective maintenance refers to the process of identifying the subset of maintenance actions to perform on a fleet of repairable systems when the maintenance resources allocated to the fleet are insufficient for performing all desirable maintenance actions. The original fleet-level selective maintenance model is designed to maximize the probability that all missions in a future set are completed successfully. We extend this model in several ways. First, we consider a cost-based optimization model and show that a special case of this model maximizes the expected value of the number of successful missions in the future set. We also consider the situation in which one or more of the future missions may be canceled. These models and the original fleet-level selective maintenance optimization models are nonlinear. Therefore, we also consider an alternative model in which the objective function can be linearized. We show that the alternative model is a good approximation to the other models. - Highlights: • Investigate nonlinear fleet-level selective maintenance optimization models. • A cost based model is used to maximize the expected number of successful missions. • Another model is allowed to cancel missions if reliability is sufficiently low. • An alternative model has an objective function that can be linearized. • We show that the alternative model is a good approximation to the other models

  18. Model-supported selection of distribution coefficients for performance assessment

    International Nuclear Information System (INIS)

    Ochs, M.; Lothenbach, B.; Shibata, Hirokazu; Yui, Mikazu

    1999-01-01

    A thermodynamic speciation/sorption model is used to illustrate typical problems encountered in the extrapolation of batch-type K d values to repository conditions. For different bentonite-groundwater systems, the composition of the corresponding equilibrium solutions and the surface speciation of the bentonite is calculated by treating simultaneously solution equilibria of soluble components of the bentonite as well as ion exchange and acid/base reactions at the bentonite surface. K d values for Cs, Ra, and Ni are calculated by implementing the appropriate ion exchange and surface complexation equilibria in the bentonite model. Based on this approach, hypothetical batch experiments are contrasted with expected conditions in compacted backfill. For each of these scenarios, the variation of K d values as a function of groundwater composition is illustrated for Cs, Ra, and Ni. The applicability of measured, batch-type K d values to repository conditions is discussed. (author)

  19. Selected bibliography on the modeling and control of plant processes

    Science.gov (United States)

    Viswanathan, M. M.; Julich, P. M.

    1972-01-01

    A bibliography of information pertinent to the problem of simulating plants is presented. Detailed simulations of constituent pieces are necessary to justify simple models which may be used for analysis. Thus, this area of study is necessary to support the Earth Resources Program. The report sums up the present state of the problem of simulating vegetation. This area holds the hope of major benefits to mankind through understanding the ecology of a region and in improving agricultural yield.

  20. Fuzzy Multicriteria Model for Selection of Vibration Technology

    OpenAIRE

    María Carmen Carnero

    2016-01-01

    The benefits of applying the vibration analysis program are well known and have been so for decades. A large number of contributions have been produced discussing new diagnostic, signal treatment, technical parameter analysis, and prognosis techniques. However, to obtain the expected benefits from a vibration analysis program, it is necessary to choose the instrumentation which guarantees the best results. Despite its importance, in the literature, there are no models to assist in taking this...

  1. Organization And Financing Models Of Health Service In Selected Countries

    Directory of Open Access Journals (Sweden)

    Branimir Marković

    2009-07-01

    Full Text Available The introductory part of the work gives a short theoretical presentation regarding possible financing models of health services in the world. In the applicative part of the work we shall present the basic practical models of financing health services in the countries that are the leaders of classic methods of health services financing, e. g. the USA, Great Britain, Germany and Croatia. Working out the applicative part of the work we gave the greatest significance to analysis of some macroeconomic indicators in health services (tendency of total health consumption in relation to GDP, average consumption per insured person etc., to structure analysis of health insurance and just to the scheme of health service organization and financing. We presume that each model of health service financing contains certain limitations that can cause problem (weak organization, increase of expenses etc.. This is the reason why we, in the applicative part of the work, paid a special attention to analysis of financial difficulties in the health sector and pointed to the needs and possibilities of solving them through possible reform measures. The end part of the work aims to point out to advantages and disadvantages of individual financing sources through the comparison method (budgetary – taxes or social health insurance – contributions.

  2. A Model for Service Life Control of Selected Device Systems

    Directory of Open Access Journals (Sweden)

    Zieja Mariusz

    2014-04-01

    Full Text Available This paper presents a way of determining distribution of limit state exceedence time by a diagnostic parameter which determines accuracy of maintaining zero state. For calculations it was assumed that the diagnostic parameter is deviation from nominal value (zero state. Change of deviation value occurs as a result of destructive processes which occur during service. For estimation of deviation increasing rate in probabilistic sense, was used a difference equation from which, after transformation, Fokker-Planck differential equation was obtained [4, 11]. A particular solution of the equation is deviation increasing rate density function which was used for determining exceedance probability of limit state. The so-determined probability was then used to determine density function of limit state exceedance time, by increasing deviation. Having at disposal the density function of limit state exceedance time one determined service life of a system of maladjustment. In the end, a numerical example based on operational data of selected aircraft [weapon] sights was presented. The elaborated method can be also applied to determining residual life of shipboard devices whose technical state is determined on the basis of analysis of values of diagnostic parameters.

  3. Probabilistic wind power forecasting with online model selection and warped gaussian process

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin

    2014-01-01

    Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms

  4. Models for MOX fuel behaviour. A selective review

    International Nuclear Information System (INIS)

    Massih, Ali R.

    2006-01-01

    This report reviews the basic physical properties of light water reactor mixed-oxide (MOX) fuel comprising nuclear characteristics, thermal properties such as melting temperature, thermal conductivity, thermal expansion, and heat capacity, and compares these with properties of conventional UO 2 fuel. These properties are generally well understood for MOX fuel and are well described by appropriate models developed for engineering analysis. Moreover, certain modelling approaches of MOX fuel in-reactor behaviour, regarding densification, swelling, fission product gas release, helium release, fuel creep and grain growth, are evaluated and compared with the models for UO 2 . In MOX fuel the presence of plutonium rich agglomerates adds to the complexity of fuel behaviour on the micro scale. In addition, we survey the recent fuel performance experience and post irradiation examinations on several types of MOX fuel types. We discuss the data from these examinations, regarding densification, swelling, fission product gas release and the evolution of the microstructure during irradiation. The results of our review indicate that in general MOX fuel has a higher fission gas release and helium release than UO 2 fuel. Part of this increase is due to the higher operating temperatures of MOX fuel relative to UO 2 fuel due to the lower thermal conductivity of MOX material. But this effect by itself seems to be insufficient to make for the difference in the observed fission gas release of UO 2 vs. MOX fuel. Furthermore, the irradiation induced creep rate of MOX fuel is higher than that of UO 2 . This effect can reduce the pellet-clad interaction intensity in fuel rods. Finally, we suggest that certain physical based approaches discussed in the report are implemented in the fuel performance code to account for the behaviour of MOX fuel during irradiation

  5. Models for MOX fuel behaviour. A selective review

    Energy Technology Data Exchange (ETDEWEB)

    Massih, Ali R. [Quantum Technologies AB, Uppsala Science Park (Sweden)

    2006-12-15

    This report reviews the basic physical properties of light water reactor mixed-oxide (MOX) fuel comprising nuclear characteristics, thermal properties such as melting temperature, thermal conductivity, thermal expansion, and heat capacity, and compares these with properties of conventional UO{sub 2} fuel. These properties are generally well understood for MOX fuel and are well described by appropriate models developed for engineering analysis. Moreover, certain modelling approaches of MOX fuel in-reactor behaviour, regarding densification, swelling, fission product gas release, helium release, fuel creep and grain growth, are evaluated and compared with the models for UO{sub 2}. In MOX fuel the presence of plutonium rich agglomerates adds to the complexity of fuel behaviour on the micro scale. In addition, we survey the recent fuel performance experience and post irradiation examinations on several types of MOX fuel types. We discuss the data from these examinations, regarding densification, swelling, fission product gas release and the evolution of the microstructure during irradiation. The results of our review indicate that in general MOX fuel has a higher fission gas release and helium release than UO{sub 2} fuel. Part of this increase is due to the higher operating temperatures of MOX fuel relative to UO{sub 2} fuel due to the lower thermal conductivity of MOX material. But this effect by itself seems to be insufficient to make for the difference in the observed fission gas release of UO{sub 2} vs. MOX fuel. Furthermore, the irradiation induced creep rate of MOX fuel is higher than that of UO{sub 2}. This effect can reduce the pellet-clad interaction intensity in fuel rods. Finally, we suggest that certain physical based approaches discussed in the report are implemented in the fuel performance code to account for the behaviour of MOX fuel during irradiation.

  6. Causal Inference and Model Selection in Complex Settings

    Science.gov (United States)

    Zhao, Shandong

    Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly

  7. On extended liability in a model of adverse selection

    OpenAIRE

    Dieter Balkenborg

    2004-01-01

    We consider a model where a judgment-proof firm needs finance to realize a project. This project might cause an environmental hazard with a probability that is the private knowledge of the firm. Thus there is asymmetric information with respect to the environmental riskiness of the project. We consider the implications of a simple joint and strict liability rule on the lender and the firm where, in case of a damage, the lender is responsible for that part of the liability which the judgment-p...

  8. Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

    OpenAIRE

    Saleem Riaz; Hassan Elahi; Kashif Javaid; Tufail Shahzad

    2017-01-01

    Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, deve...

  9. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang

    2017-02-16

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  10. Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

    KAUST Repository

    Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

    2017-01-01

    Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

  11. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    International Nuclear Information System (INIS)

    Zhou, Z; Folkert, M; Wang, J

    2016-01-01

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  12. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  13. Selective Cooperation in Early Childhood - How to Choose Models and Partners.

    Directory of Open Access Journals (Sweden)

    Jonas Hermes

    Full Text Available Cooperation is essential for human society, and children engage in cooperation from early on. It is unclear, however, how children select their partners for cooperation. We know that children choose selectively whom to learn from (e.g. preferring reliable over unreliable models on a rational basis. The present study investigated whether children (and adults also choose their cooperative partners selectively and what model characteristics they regard as important for cooperative partners and for informants about novel words. Three- and four-year-old children (N = 64 and adults (N = 14 saw contrasting pairs of models differing either in physical strength or in accuracy (in labeling known objects. Participants then performed different tasks (cooperative problem solving and word learning requiring the choice of a partner or informant. Both children and adults chose their cooperative partners selectively. Moreover they showed the same pattern of selective model choice, regarding a wide range of model characteristics as important for cooperation (preferring both the strong and the accurate model for a strength-requiring cooperation tasks, but only prior knowledge as important for word learning (preferring the knowledgeable but not the strong model for word learning tasks. Young children's selective model choice thus reveals an early rational competence: They infer characteristics from past behavior and flexibly consider what characteristics are relevant for certain tasks.

  14. Selected topics in phenomenology of the standard model

    International Nuclear Information System (INIS)

    Roberts, R.G.

    1991-01-01

    These lectures cover some aspects of phenomenology of topics in high energy physics which advertise the success of the standard model in dealing with a wide variety of experimental data. First we begin with a look at deep inelastic scattering. This tells us about the structure of the nucleon, which is understood in terms of the SU(3) gauge theory of QCD, which then allows the information on quark and gluon distributions to be carried over to other 'hard' processes such as hadronic production of jets. Recent data on electroweak processes can estimate the value of Sin 2 θw to a precision where the inclusion of radiative corrections allow bounds to be made on the mass of the top quark. Electroweak effects arise in e + e - collisions, but we first present a review of the recent history of this topic within the context of QCD. We bring the subject up to date with a look at the physics at (or near) the Z pole where the measurement of asymmetries can give more information. We look at the conventional description of quark mixing by the CKM matrix and see how the mixing parameters are systematically being extracted from a variety of reactions and decays. In turn, the values can be used to set bounds on the top quark mass. The matter of CP violation in weak interactions is addressed within the context of the standard model, recent data on ε'/ε being the source of current excitement. Finally, we at the theoretical description and experimental efforts to search for the top quark. (author)

  15. Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.

    Science.gov (United States)

    Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H

    2014-12-30

    For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons

  16. Detecting consistent patterns of directional adaptation using differential selection codon models.

    Science.gov (United States)

    Parto, Sahar; Lartillot, Nicolas

    2017-06-23

    Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

  17. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

    Science.gov (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

    2017-07-01

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  18. Integrated logistics management system for operation of machinery and equipment

    Directory of Open Access Journals (Sweden)

    Józef Frąś

    2014-09-01

    Full Text Available Background: The main issue in the operations of machinery and equipment, which is the subject of theoretical and empirical research is to provide high reliability and durability with qualitative post-trade services of machinery and equipment. Quality of service can be achieved through planned maintenance activities supported by computer technology. The article presents the concept of an integrated system of logistics management operation of machinery and equipment, especially special one for stationary transport equipment. At the outset, it emphasized the importance and essence of technological transport and storage systems storage in modern manufacturing enterprise. Then the objective and the method of research have been set. An essential part of deliberations in the article is the concept of integrated logistics management system operation for stationary transport equipment. Authors of this article have presented the results the implementation and operation of the system. The results are presented in a descriptive and graphic form. Methods: The purpose of this article is to present the concept of implementing an integrated logistics management system for operation of stationary transport equipment. It goes through combination of planning, event logging service, warehouse management in the field of spare parts, account and records of the cost of service activities. The paper presents an analysis and evaluation method of brainstorming a new approach to logistics management operation stationary transport equipment. Authors takes into account the specific conditions of use of transport equipment and conduct the service, which have a significant impact on the time and place of cost and service as well. It should be noted that the developed system has been implemented. It was also carried out an assessment of its functionality and efficiency as the new IT tool for logistics management operation. Results and conclusions: The paper presents a new

  19. gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

    OpenAIRE

    Hofner, Benjamin; Mayr, Andreas; Schmid, Matthias

    2014-01-01

    Generalized additive models for location, scale and shape are a flexible class of regression models that allow to model multiple parameters of a distribution function, such as the mean and the standard deviation, simultaneously. With the R package gamboostLSS, we provide a boosting method to fit these models. Variable selection and model choice are naturally available within this regularized regression framework. To introduce and illustrate the R package gamboostLSS and its infrastructure, we...

  20. Transverse tripolar stimulation of peripheral nerve: a modelling study of spatial selectivity

    NARCIS (Netherlands)

    Deurloo, K.E.I.; Holsheimer, J.; Boom, H.B.K.

    1998-01-01

    Various anode-cathode configurations in a nerve cuff are modelled to predict their spatial selectivity characteristics for functional nerve stimulation. A 3D volume conductor model of a monofascicular nerve is used for the computation of stimulation-induced field potentials, whereas a cable model of

  1. Experiment selection for the discrimination of semi-quantitative models of dynamical systems

    NARCIS (Netherlands)

    Vatcheva, [No Value; de Jong, H; Bernard, O; Mars, NJI

    Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. To discriminate among these models, additional experiments are needed. Existing methods for the selection of discriminatory experiments in statistics and in

  2. Model selection in Bayesian segmentation of multiple DNA alignments.

    Science.gov (United States)

    Oldmeadow, Christopher; Keith, Jonathan M

    2011-03-01

    The analysis of multiple sequence alignments is allowing researchers to glean valuable insights into evolution, as well as identify genomic regions that may be functional, or discover novel classes of functional elements. Understanding the distribution of conservation levels that constitutes the evolutionary landscape is crucial to distinguishing functional regions from non-functional. Recent evidence suggests that a binary classification of evolutionary rates is inappropriate for this purpose and finds only highly conserved functional elements. Given that the distribution of evolutionary rates is multi-modal, determining the number of modes is of paramount concern. Through simulation, we evaluate the performance of a number of information criterion approaches derived from MCMC simulations in determining the dimension of a model. We utilize a deviance information criterion (DIC) approximation that is more robust than the approximations from other information criteria, and show our information criteria approximations do not produce superfluous modes when estimating conservation distributions under a variety of circumstances. We analyse the distribution of conservation for a multiple alignment comprising four primate species and mouse, and repeat this on two additional multiple alignments of similar species. We find evidence of six distinct classes of evolutionary rates that appear to be robust to the species used. Source code and data are available at http://dl.dropbox.com/u/477240/changept.zip.

  3. An Evaluation Model To Select an Integrated Learning System in a Large, Suburban School District.

    Science.gov (United States)

    Curlette, William L.; And Others

    The systematic evaluation process used in Georgia's DeKalb County School System to purchase comprehensive instructional software--an integrated learning system (ILS)--is described, and the decision-making model for selection is presented. Selection and implementation of an ILS were part of an instructional technology plan for the DeKalb schools…

  4. Augmented Self-Modeling as a Treatment for Children with Selective Mutism.

    Science.gov (United States)

    Kehle, Thomas J.; Madaus, Melissa R.; Baratta, Victoria S.; Bray, Melissa A.

    1998-01-01

    Describes the treatment of three children experiencing selective mutism. The procedure utilized incorporated self-modeling, mystery motivators, self-reinforcement, stimulus fading, spacing, and antidepressant medication. All three children evidenced a complete cessation of selective mutism and maintained their treatment gains at follow-up.…

  5. Selecting a Response in Task Switching: Testing a Model of Compound Cue Retrieval

    Science.gov (United States)

    Schneider, Darryl W.; Logan, Gordon D.

    2009-01-01

    How can a task-appropriate response be selected for an ambiguous target stimulus in task-switching situations? One answer is to use compound cue retrieval, whereby stimuli serve as joint retrieval cues to select a response from long-term memory. In the present study, the authors tested how well a model of compound cue retrieval could account for a…

  6. Multi-scale habitat selection modeling: A review and outlook

    Science.gov (United States)

    Kevin McGarigal; Ho Yi Wan; Kathy A. Zeller; Brad C. Timm; Samuel A. Cushman

    2016-01-01

    Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.

  7. The use of vector bootstrapping to improve variable selection precision in Lasso models

    NARCIS (Netherlands)

    Laurin, C.; Boomsma, D.I.; Lubke, G.H.

    2016-01-01

    The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections.

  8. Towards a pro-health food-selection model for gatekeepers in ...

    African Journals Online (AJOL)

    The purpose of this study was to develop a pro-health food selection model for gatekeepers of Bulawayo high-density suburbs in Zimbabwe. Gatekeepers in five suburbs constituted the study population from which a sample of 250 subjects was randomly selected. Of the total respondents (N= 182), 167 had their own ...

  9. Vibration analysis for trending ageing in rotating machinery

    International Nuclear Information System (INIS)

    Sinha, S.K.; Rama Rao, A.

    2006-01-01

    The need for condition monitoring system for important equipment and machinery is a growing requirement in every industry and more so in the nuclear power plants because of stringent safety requirements. This is largely because of the inherent benefit of being able to promote predictive maintenance practice rather than uneconomical preventive maintenance practice in the plant. Forerunner among the condition monitoring parameter is vibration signatures measured on a rotating machine. It is known that every moving element in a rotating machine generates vibration signal that is uniquely its own. Detection of such signals and monitoring the changing conditions in a machine through vibration analysis is a technique involving the knowledge of engineering art and the mathematical theory. This blend of sound engineering judgement and vibration data interpretation skill is in fact the basis of vibration diagnostic techniques. (author)

  10. Effect of machinery wheel load on grass yield

    DEFF Research Database (Denmark)

    Green, Ole; Jørgensen, Rasmus Nyholm; Kristensen, Kristian

    2010-01-01

    Effect of machinery wheel load on grass   Ole Green1, Rasmus N. Jørgensen2, Kristian Kristensen3, René Gislum3, Dionysis Bochtis1, & Claus G. Sørensen1   1University of Aarhus, Dept. of Agricultural Engineering 2University of Southern Denmark, Inst. of Chemical Eng., Biotechnology and Environmental...... 3University of Aarhus, Dept. of Genetics and Biotechnology   Corresponding author: Ole Green Address & e-mail: Research Centre Foulum, Blichers Allé 20, 8830 Tjele. Ole.Green@agrsci.dk     Abstract   Different traffic intensities have been shown to have a negative influence on the yield of grass...... and clover. A full scale grass-clover field trial was established to estimate the effect on clover-grass yields as a function of different wheel loads and tire pressures. The trial comprised 16 different traffic intensities with 35 replicates and 1 traffic free treatment with 245 replicates, totalling 17...

  11. Skoda Concern's cooperation with State Machinery Design Research Institute

    International Nuclear Information System (INIS)

    Valchar, J.; Kuhn, L.

    1988-01-01

    The main areas are presented of cooperation between the Skoda Plzen Concern and the State Machinery Design Research Institute in Prague-Bechovice. This is mainly the development of steam turbines, from 50 MW turbines to the present 1000 MW saturated steam turbines designed for nuclear power plants. Main attention is centred on conditions of the boiling crisis in the steam turbine circuit, and its consequences. This study is served by the experimental equipment of the institute and its computer. The cooperation of the two institutions in the field of testing and diagnostic equipment is centred on measuring natural oscillations of turbine blades, the diagnostics of vibrations of steam turbines, the measurement of the humidity of saturated steam, optical measurements of the parameters of saturated steam, ultrasound diagnostics and the measurement of turbine blade deformation caused by hydraulic effects. (Z.M.). 8 figs

  12. Mechatronics Applied to Fluid Film Bearings: Towards More Efficient Machinery

    DEFF Research Database (Denmark)

    Cerda Varela, Alejandro Javier

    the machine is defined as a mechatronic system. This integrated approach comprises the usage of machine elements capable of modifying their characteristics, by using in a combined way mechanical elements, sensors, processing units and actuators. The research project entitled "Mechatronics Applied to Fluid......The current trends regarding turbomachinery design and operation demand for an expansion of the operational boundaries of these mechanical systems, regarding production rate, reliability and adaptability. In order to face the new requirements, it is necessary to migrate towards a new concept, where...... Film Bearings: Towards more Efficient Machinery" was aimed at improving the state of the art regarding the usage of fluid film bearings as "smart" machine elements. Specifically, this project dealt with a tilting pad journal bearing design that features a controllable lubrication system, capable...

  13. SKODA Nuclear Machinery - tradition and expertise in nuclear power industry

    International Nuclear Information System (INIS)

    Svitak, F.

    1997-01-01

    The SKODA Nuclear Machinery company is a major manufacturer of nuclear reactor assemblies and supplier of WWER type primary coolant circuits. In the past, the company was nearly a monopolistic manufacturer of WWER reactor assemblies supplied to the Central and East European countries (except the USSR) grouped in the former Council of Mutual Economic Assistance. Over the 1980-1993 period, 21 units of the WWER-440 type and 3 units of the WWER-1000 type were manufactured. The company keeps abreast of technological progress and has been switching to new manufacturing areas, such as compact storage racks for spent fuel pools, hermetic cable bushings, spent fuel storage and transport casks, and cooperation in the manufacture of neutron flux measuring channels. Technological services provided to nuclear power plants constitute another important field of the company's business. The company's combined expertise in Soviet and Western designed PWRs is a considerable asset. (P.A.)

  14. Stepwise Diagnosis for Rotating Machinery Using Force Identification Approach

    Directory of Open Access Journals (Sweden)

    Shozo Kawamura

    2012-01-01

    Full Text Available Machine condition monitoring and diagnosis have become increasingly important, and the application of these processes has been widely investigated. The authors previously proposed a stepwise diagnosis method for a beam structure. In that method, the location of the abnormality is first estimated using the force identification approach, and then the cause of the abnormality is identified. In this study, the stepwise diagnosis method was improved specifically for rotating machinery. The applicability of the proposed method was checked by using the experimental data. In the case of a rotor system with unbalance, it was shown that the location of the abnormality and its severity could be identified, and, in the case of a rotor system with stationary rubbing, the location of the abnormality could be accurately identified. Therefore, it was confirmed that the proposed diagnostic method is feasible for actual application.

  15. Autophagic machinery activated by dengue virus enhances virus replication

    International Nuclear Information System (INIS)

    Lee, Y.-R.; Lei, H.-Y.; Liu, M.-T.; Wang, J.-R.; Chen, S.-H.; Jiang-Shieh, Y.-F.; Lin, Y.-S.; Yeh, T.-M.; Liu, C.-C.; Liu, H.-S.

    2008-01-01

    Autophagy is a cellular response against stresses which include the infection of viruses and bacteria. We unravel that Dengue virus-2 (DV2) can trigger autophagic process in various infected cell lines demonstrated by GFP-LC3 dot formation and increased LC3-II formation. Autophagosome formation was also observed under the transmission electron microscope. DV2-induced autophagy further enhances the titers of extracellular and intracellular viruses indicating that autophagy can promote viral replication in the infected cells. Moreover, our data show that ATG5 protein is required to execute DV2-induced autophagy. All together, we are the first to demonstrate that DV can activate autophagic machinery that is favorable for viral replication

  16. Monitoring of vibrating machinery using artificial neural networks

    International Nuclear Information System (INIS)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.

    1991-01-01

    The primary source of vibration in complex engineering systems is rotating machinery. Vibration signatures collected from these components render valuable information about the operational state of the system and may be used to perform diagnostics. For example, the low frequency domain contains information about unbalance, misalignment, instability in journal bearing and mechanical looseness; analysis of the medium frequency range can render information about faults in meshing gear teeth; while the high frequency domain will contain information about incipient faults in rolling-element bearings. Trend analysis may be performed by comparing the vibration spectrum for each machine with a reference spectrum and evaluating the vibration magnitude changes at different frequencies. This form of analysis for diagnostics is often performed by maintenance personnel monitoring and recording transducer signals and analyzing the signals to identify the operating condition of the machine. With the advent of portable fast Fourier transform (FFT) analyzers and ''laptop'' computers, it is possible to collect and analyze vibration data an site and detect incipient failures several weeks or months before repair is necessary. It is often possible to estimate the remaining life of certain systems once a fault has been detected. RMS velocity, acceleration, displacements, peak value, and crest factor readings can be collected from vibration sensors. To exploit all the information embedded in these signals, a robust and advanced analysis technique is required. Our goal is to design a diagnostic system using neural network technology, a system such as this would automate the interpretation of vibration data coming from plant-wide machinery and permit efficient on-line monitoring of these components

  17. Cholinergic Machinery as Relevant Target in Acute Lymphoblastic T Leukemia

    Directory of Open Access Journals (Sweden)

    Oxana Dobrovinskaya

    2016-08-01

    Full Text Available Various types of non-neuronal cells, including tumors, are able to produce acetylcholine (ACh, which acts as an autocrine/paracrine growth factor. T lymphocytes represent a key component of the non-neuronal cholinergic system. T cells-derived ACh is involved in a stimulation of their activation and proliferation, and acts as a regulator of immune response. The aim of the present work was to summarize the data about components of cholinergic machinery in T lymphocytes, with an emphasis on the comparison of healthy and leukemic T cells. Cell lines derived from acute lymphoblastic leukemias of T lineage (T-ALL were found to produce a considerably higher amount of ACh than healthy T lumphocytes. Additionally, ACh produced by T-ALL is not efficiently hydrolyzed, because acetylcholinesterase (AChE activity is drastically decreased in these cells. Up-regulation of muscarinic ACh receptors was also demonstrated at expression and functional level, whereas nicotinic ACh receptors seem to play a less important role and not form functional channels in cells derived from T-ALL. We hypothesized that ACh over-produced in T-ALL may act as an autocrine growth factor and play an important role in leukemic clonal expansion through shaping of intracellular Ca2+ signals. We suggest that cholinergic machinery may be attractive targets for new drugs against T-ALL. Specifically, testing of high affinity antagonists of muscarinic ACh receptors as well as antagomiRs, which interfere with miRNAs involved in the suppression of AChE expression, may be the first choice options.

  18. Decision support model for selecting and evaluating suppliers in the construction industry

    Directory of Open Access Journals (Sweden)

    Fernando Schramm

    2012-12-01

    Full Text Available A structured evaluation of the construction industry's suppliers, considering aspects which make their quality and credibility evident, can be a strategic tool to manage this specific supply chain. This study proposes a multi-criteria decision model for suppliers' selection from the construction industry, as well as an efficient evaluation procedure for the selected suppliers. The model is based on SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranking method and its main contribution is a new approach to structure the process of suppliers' selection, establishing explicit strategic policies on which the company management system relied to make the suppliers selection. This model was applied to a Civil Construction Company in Brazil and the main results demonstrate the efficiency of the proposed model. This study allowed the development of an approach to Construction Industry which was able to provide a better relationship among its managers, suppliers and partners.

  19. Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

    Science.gov (United States)

    Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

    2015-11-01

    Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

  20. Which risk models perform best in selecting ever-smokers for lung cancer screening?

    Science.gov (United States)

    A new analysis by scientists at NCI evaluates nine different individualized lung cancer risk prediction models based on their selections of ever-smokers for computed tomography (CT) lung cancer screening.