Institute of Scientific and Technical Information of China (English)
无
2008-01-01
@@ China Council for the Promotion of International Trade, Machin-ery Sub-Council (referred to as CCPIT MSC) & China Chamber of International Commerce, Machinery Chamber of Commerce was founded in 1988 as one of the first group of industrial trade promotion agencies approved by the governing authorities of China.
Establishment of evaluation model for tobacco field machinery selection%烟草田间作业机械评价模型的构建
Institute of Scientific and Technical Information of China (English)
张卫鹏; 郑志安; 王刚; 高振江; 杨宝玲; 王继承
2015-01-01
In order to avoid the bias occurred in empirical method for agricultural machinery selection, such as testing operation effects by field demonstration or organoleptic evaluation, this essay aims to develop an applicability satisfaction based evaluation solution for the agricultural machinery selection test. By adopting tobacco planting and tobacco field machinery as study objects, a practical production management oriented model is proposed. The process of tobacco planting is divided into ten links of three stages (Preparation, Planting, and Management). An evaluation index system, involving forty-four indexes in all, is set at the same time. In which, ten evaluation models correspond to ten tobacco planting links respectively, while each of the evaluation models consists of four major operational indicators, namely cost, efficiency, effect, and effectiveness. The system samples data from on-site test based on quantitative evaluation indicators as benchmark under agronomic requirements, and builds up a comprehensive evaluation model by means of AHP. The ridging link is taken as an example to demonstrate the application of the model. In the experiment, seven sets of ridging machinery are involved (with serial numbers as I, II, …… , and VII). In which, IV and V are of Caterpillar type; VII is of Multifunctional, and all others are of Walk-Behind. From the process of the experiment, the following performances have been presented. First, the Walk-Behind sets work best from the perspective of operation effect and stability due to their small overall sizes that facilitate adjusting the working attitudes in time. Among all types, these sets show the best also in safety assessment that they have lest risk of rollover when operating between small pieces of fields in hilly area. However, the operation efficiency is hard to be largely increased restricted by the slow walking speed of operator. Second, due to the big size, the Caterpillars are slow in both operation and
Implications of material selection on the design of packaging machinery.
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.
African Journals Online (AJOL)
Data were collected using a portable data acquisition system: SKF Microlog. Data were collected' ... Modelling (HMM) ofvibrational signals. ... trifugal pump designed for a pressure increase of. 6.6 bars at ..... These frequency amplitudes are located at the vertical. axial and .... distribution centre the residual chlorine concen-.
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.
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.
Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks
Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay
2013-01-01
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 using such more
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
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
Genome-scale modeling of the protein secretory machinery in yeast.
Feizi, Amir; Österlund, Tobias; Petranovic, Dina; Bordel, Sergio; Nielsen, Jens
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 was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery.
Directory of Open Access Journals (Sweden)
P. Marinkovic
2011-03-01
Full Text Available The aim of this work is to analyse the possibility to increase the service life of working parts on construction machinery exposed to intensive wear, such as steel blades of the rotary device for roadside vegetation maintenance and grass cutting. A special attention is paid to characteristic working conditions and complex wear mechanisms. In order to select the most appropriate reparation technology, both model and real investigations were conducted. The aim of the model investigations was to select the most appropriate procedure, filler materials and hard facing technology. Worn cutting edges of the blades were hard faced and sharpened by grinding to the shape and dimensions of new blades. Then, both new and repaired blades were alternately mounted on the rotor of the machine. Their wear was monitored under the same working and weather conditions. The repaired blades have proven more resistant to wear than the new ones, which is due to better properties of the hard faced layers.
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...... to be coupled with multibody dynamics models. The focus of the current paper is an approach where the transient pressure field in hydrodynamic lubricated joint clearances are modelled by a set of control volumes and coupled with the fluid power machinery mechanics....... 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...
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
Development of dynamical model of wheel machinery allocated on a flat–car
Directory of Open Access Journals (Sweden)
Khabibulla TURANOV
2010-01-01
Full Text Available The paper gives the results of dynamical modeling of the mechanical system “flat car – elastic elements – wheel machinery body”, allocated on a railway flat car. There have been obtained the formulas of equivalent rigidity of fastening spatial flexible elements relative to a vertical line, which are equal to rigidity of bus and spring flexible elements being plugged in series and which are then equal to rigidity of all elastic elements of wheeled machinery as springs being plugged in parallel.
Institute of Scientific and Technical Information of China (English)
DOU Wei; LIU Zhan-sheng
2009-01-01
To overcome the limitations of traditional monitoring methods, based on vibration parameter image of rotating machinery, this paper presents an abnormality online monitoring method suitable for rotating machinery using the negative selection mechanism of biology immune system. This method uses techniques of biology clone and learning mechanism to improve the negative selection algorithm to generate detectors possessing different monitoring radius, covers the abnormality space effectively, and avoids such problems as the low efficiency of generating detectors, etc. The result of an example applying the presented monitoring method shows that this method can solve the difficulty of obtaining fault samples preferably and extract the turbine state character effec tively, it also can detect abnormality by causing various fault of the turbine and obtain the degree of abnormality accurately. The exact monitoring precision of abnormality indicates that this method is feasible and has better on-line quality, accuracy and robustness.
Institute of Scientific and Technical Information of China (English)
陶少雄; 毛敏华; 黎咏梅
2012-01-01
为了在众多的水利施工机械设备中选择经济、环保、安全以及实用的设备,提出一种基于层次分析法(AHP)和灰色关联分析法(GCA)相结合的设备选择模型.首先建立由经济性、技术性、绿色性以及职业健康与安全性组成的设备选择的多目标指标体系,并对指标量化方法进行了研究,然后将层次分析法与灰色关联分析法相结合对设备选型模型进行了求解.实例分析验证了模型的可行性和有效性.%To help decision maker select the economic, green, safe and suitable machinery equipment in water conservancy project construction, a model based on Analytic Hierarchy Process (AHP) and Grey Correlation Analysis (GCA) method was presented. A decision-making multi -criteria index system comprising economy, technology, environmental impact and the occupation healthy security was established at first and do research on the indexes' quantification method. Use the method combined AHP with GCA to solve the model. An illustrative example is provided to validate the model's feasibility and validity.
Institute of Scientific and Technical Information of China (English)
WANG Ting; YI Shuping; YANG Yuanzhao
2007-01-01
A set of indices for performance evaluation for business processes with multiple inputs and multiple outputs is proposed, which are found in machinery manufacturers. Based on the traditional methods of data envelopment analysis (DEA) and analytical hierarchical process (AHP), a hybrid model called DEA/AHP model is proposed to deal with the evaluation of business process performance. With the proposed method, the DEA is firstly used to develop a pairwise comparison matrix, and then the AHP is applied to evaluate the performance of business process using the pairwise comparison matrix. The significant advantage of this hybrid model is the use of objective data instead of subjective human judgment for performance evaluation. In the case study, a project of business process reengineering (BPR) with a hydraulic machinery manufacturer is used to demonstrate the effectiveness of the DEA/AHP model.
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...... parameters for marine WHR. Using this mentioned methodology, regression models are derived for the prediction of the maximum obtainable thermal efficiency of ORCs. A unique configuration of the Kalina cycle, the Split-cycle, is analysed to evaluate the fullest potential of the Kalina cycle for the purpose...
Purchase-oriented Classification Model of the Spare Parts of Agricultural Machinery
Institute of Scientific and Technical Information of China (English)
2011-01-01
Based on the classification of spare parts and the research results of the demand of spare parts,a three-dimensional classification model of spare parts of agricultural machinery is established,which includes the application axis sorted by technical characteristics,the cost axis classified by ABC method,and the demand axis classified by the demand of the spare parts of agricultural machinery.These dimension axes represent different factors,and the application of factors in purchase is analyzed.Guiding value of each dimension axis is summarized in the field of the spare parts purchase;and corresponding strategy instruction is put forward.Integrated application of these strategies by model makes the purchase have more realistic operational meaning.Application field of the three-dimensional model of spare parts is discussed;and the direction for further research is pointed out.
Institute of Scientific and Technical Information of China (English)
Xiaoling; HAO; Ruixia; SUO
2015-01-01
Agricultural machinery total power is an important index to reflect and evaluate the level of agricultural mechanization. Firstly,we respectively made use of exponential model,grey forecasting and BP neural network to construct models depending on historical data of agricultural machinery total power of Heilongjiang Province; secondly,we constructed the combined forecasting models that respectively based on divergence coefficient method,quadratic programming and weight distribution of Shapley value. Fitting results showed that the various combination forecasting model is superior to the single models. Finally,we applied the combination forecasting model which based on the weight distribution method of Shapley value to forecast Heilongjiang agricultural machinery total power,and it would provide some reference to the development and program for power of agriculture machinery.
First-principle and data-driven model- based approach in rotating machinery failure mode detection
Directory of Open Access Journals (Sweden)
G. Wszołek
2010-12-01
Full Text Available Purpose: A major concern of modern diagnostics is the use of vibration or acoustic signals generated by a machine to reveal its operating conditions. This paper presents a method which allows to periodically obtain estimates of model eigenvalues represented by complex numbers. The method is intended to diagnose rotating machinery under transient conditions.Design/methodology/approach: The method uses a parametric data-driven model, the parameters of which are estimated using operational data.Findings: Experimental results were obtained with the use of a laboratory single-disc rotor system equipped with both sliding and hydrodynamic bearings. The test rig used allows measurements of data under normal, or reference, and malfunctioning operation, including oil instabilities, rub, looseness and unbalance, to be collected.Research limitations/implications: Numerical and experimental studies performed in order to validate the method are presented in the paper. Moreover, literature and industrial case studies are analyzed to better understand vibration modes of the rotor under abnormal operating conditions. Practical implications: A model of the test rig has been developed to verify the method proposed herein and to understand the results of the experiments. Hardware realization of the proposed method was implemented as a standalone operating module developed using the Texas Instruments TMS3200LF2407 Starter Kit.Originality/value: The parametric approach was proposed instead of nonparametric one towards diagnosing of rotating machinery.
Binshtok, Alexander M
2011-01-01
Many surgical and dental procedures depend on use of local anesthetics to reversibly eliminate pain. By the blockade of voltage-gated sodium channels, local anesthetics prevent the transmission of nociceptive information. However, since all local anesthetics act non-selectively on all types of axons they also cause a loss of innocuous sensation, motor paralysis and autonomic block. Thus, approaches that produce only a selective blockade of pain fibers are of great potential clinical importance. In this chapter we will review the recent findings describing mechanisms of pain transduction and transmission and introduce novel therapeutic approaches to produce pain-selective analgesia.
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 lackin...
Directory of Open Access Journals (Sweden)
Misato Kaishima
Full Text Available Molecules that can control protein-protein interactions (PPIs have recently drawn attention as new drug pipeline compounds. Here, we report a technique to screen desirable affinity-altered (affinity-enhanced and affinity-attenuated protein variants. We previously constructed a screening system based on a target protein fused to a mutated G-protein γ subunit (Gγcyto lacking membrane localization ability. This ability, required for signal transmission, is restored by recruiting Gγcyto into the membrane only when the target protein interacts with an artificially membrane-anchored candidate protein, thereby allowing interacting partners (Gγ recruitment system to be searched and identified. In the present study, the Gγ recruitment system was altered by integrating the cytosolic expression of a third protein as a competitor to set a desirable affinity threshold. This enabled the reliable selection of both affinity-enhanced and affinity-attenuated protein variants. The presented approach may facilitate the development of therapeutic proteins that allow the control of PPIs.
Wind power research at Oregon State University. [for selecting windpowered machinery sites
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.
Model Selection for Geostatistical Models
Energy Technology Data Exchange (ETDEWEB)
Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.
2006-02-01
We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.
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.)
2006-05-01
interests include feature selection, statistical learning, multivariate statistics, market research, and classification. He may be contacted at...current youth market , and reducing barriers to Army enlistment. Part of the Army Recruiting Initiatives was the creation of a recruiter selection...Selection Model DevelPed by the Openuier Reseach Crate of E...lneSstm Erapseeeng Depce-teo, WViitd Ntt. siliec Academy, NW..t Point, 271 Weau/’itt 21M
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.
Tena-Suck, Martha Lilia; Hernández-Campos, Ma Elena; Ortiz-Plata, Alma; Salinas-Lara, Citlaltepetl; Colín-González, Ana Laura; Santamaría, Abel
2014-04-01
Craniopharyngiomas (CPs) are benign epithelial cystic tumors of the sellar and suprasellar region with a high survival rate and high recurrence in children. CPs contain dense oily fluid, but little is known yet about this content and its contribution to tissue damage and tumoral growth. In this study, we developed a simple experimental model produced by intracortical injection to rats of the cyst fluid content collected from human CPs to explore its possible contribution to brain tissue damage. The cyst fluid of the CPs ("oil machinery fluid") was collected during surgical removal, briefly preserved and further tested in rats through intracortical infusion. The group receiving "oil machinery fluid" presented increased reactive oxygen species formation, oxidative damage to lipids and reactive gliosis accompanied by augmented immunoreactivity to peroxiredoxin and thioredoxin reductase 1 at 15, 30 and 45 days post-injection. Other markers of inflammation and cell damage were stimulated at all post-lesion days tested. There was also a body weight gain. The persistence of tissue damage and oxidative stress suggests that "oil machinery fluid" exerts progressive alterations similar to those observed in patients with CPs, supporting the concept that some components of cyst fluid may contribute to brain tissue damage in these patients.
MODELING ENVIRONMENTAL IMPACT OF MACHINERY SECTORS TO PROMOTE SUSTAINABLE DEVELOPMENT OF THAILAND
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 ...
Vibration of hydraulic machinery
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...
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.
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Individual Influence on Model Selection
Sterba, Sonya K.; Pek, Jolynn
2012-01-01
Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…
Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery
Institute of Scientific and Technical Information of China (English)
Wang Hongjun; Xu Xiaoli; Rosen B G
2014-01-01
Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine (PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.
An application to model traffic intensity of agricultural machinery at field scale
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.
2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed.Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.
Zemenkova, M. Yu; Shipovalov, A. N.; Zemenkov, Yu D.
2016-04-01
The main technological equipment of pipeline transport of hydrocarbons are hydraulic machines. During transportation of oil mainly used of centrifugal pumps, designed to work in the “pumping station-pipeline” system. Composition of a standard pumping station consists of several pumps, complex hydraulic piping. The authors have developed a set of models and algorithms for calculating system reliability of pumps. It is based on the theory of reliability. As an example, considered one of the estimation methods with the application of graph theory.
Mavelli, Fabio; Stano, Pasquale
2015-01-01
Synthetic or semi-synthetic minimal cells are those cell-like artificial compartments that are based on the encapsulation of molecules inside lipid vesicles (liposomes). Synthetic cells are currently used as primitive cell models and are very promising tools for future biotechnology. Despite the recent experimental advancements and sophistication reached in this field, the complete elucidation of many fundamental physical aspects still poses experimental and theoretical challenges. The interplay between solute capture and vesicle formation is one of the most intriguing ones. In a series of studies, we have reported that when vesicles spontaneously form in a dilute solution of proteins, ribosomes, or ribo-peptidic complexes, then, contrary to statistical predictions, it is possible to find a small fraction of liposomes (<1%) that contain a very large number of solutes, so that their local (intravesicular) concentrations largely exceed the expected value. More recently, we have demonstrated that this effect (spontaneous crowding) operates also on multimolecular mixtures, and can drive the synthesis of proteins inside vesicles, whereas the same reaction does not proceed at a measurable rate in the external bulk phase. Here we firstly introduce and discuss these already published observations. Then, we present a computational investigation of the encapsulation of transcription-translation (TX-TL) machinery inside vesicles, based on a minimal protein synthesis model and on different solute partition functions. Results show that experimental data are compatible with an entrapment model that follows a power law rather than a Gaussian distribution. The results are discussed from the viewpoint of origin of life, highlighting open questions and possible future research directions.
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.
Improving machinery reliability
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.
Launch vehicle selection model
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
Model Selection Principles in Misspecified Models
Lv, Jinchi
2010-01-01
Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information criterion when models are correctly specified. Yet model misspecification is unavoidable when we have no knowledge of the true model or when we have the correct family of distributions but miss some true predictor. In this paper, we propose a family of semi-Bayesian principles for model selection in misspecified models, which combine the strengths of the two well-known principles. We derive asymptotic expansions of the semi-Bayesian principles in misspecified generalized linear models, which give the new semi-Bayesian information criteria (SIC). A specific form of SIC admits a natural decomposition into the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model miss...
Bayesian Model Selection and Statistical Modeling
Ando, Tomohiro
2010-01-01
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik
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.
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.
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.
Okutani, Satoshi; Iwai, Takayoshi; Iwatani, Shintaro; Matsuno, Kiyoshi; Takahashi, Yasuhiro; Hase, Toshiharu
2015-09-01
During amino-acid crystal fermentation, mechanical stress on bacterial cells caused by crystal collision often impacts negatively on bacterial growth and amino-acid production. When Escherichia coli cells were cultivated under mechanical stress of polyvinyl chloride particles as a model of the crystal fermentation, activities of iron-sulfur (Fe-S) cluster-containing enzymes were apparently decreased. Based on an assumption that function of Fe-S cluster assembly machinery would be elevated to recover the enzyme activities in such stressed cells, we analyzed levels of various components of Fe-S cluster assembly machinery by western blotting. It was found that the expression of HscA, a chaperon component of the machinery, was up-regulated and that shorter forms of HscA with the N-terminal region truncated were accumulated, suggesting an important role of HscA against the mechanical stress. An overexpression of HscA gene in E. coli cells gave a positive effect on rescue of the stress-induced decrease of the activity of Fe-S cluster-containing enzyme. These results may provide a new strategy to alleviate the mechanical stress during the amino-acid crystal fermentation.
Introduction. Modelling natural action selection.
Prescott, Tony J; Bryson, Joanna J; Seth, Anil K
2007-09-29
Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.
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...
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.
DEFF Research Database (Denmark)
Frimann Nielsen, Rasmus; Haglind, Fredrik; Larsen, Ulrik
2014-01-01
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...... 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...
Bayesian Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
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.
Model Selection for Pion Photoproduction
Landay, J; Fernández-Ramírez, C; Hu, B; Molina, R
2016-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the $S$-matrix are implemented to different degree in different approaches, but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the Least Absolute Shrinkage and Selection Operator (LASSO) in combination with criteria from information theory and $K$-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data, then, its feasibility for real data is demonstrated by analyzing the latest available measu...
DEFF Research Database (Denmark)
Frimann Nielsen, Rasmus; Haglind, Fredrik; Larsen, Ulrik
2013-01-01
In order to reduce the formation of acid rain and its harmful effects, stricter legislations on emissions of sulphur oxides from ships applies as of 2015 in emission control areas and globally in 2020 by the international maritime organization (IMO). Consequently, prices on low sulphur fuels...... of the machinery system. The wet sulphuric acid process has shown to be an effective way of removing sulphur oxides from flue gas of land-based coal fired power plants. Moreover, organic Rankine cycles are suitable for heat to power conversion for low temperature heat sources. This paper is aimed at designing......-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...
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...
Entropic criterion for model selection
Tseng, Chih-Yuan
2006-10-01
Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.
A Selective Review of Group Selection in High Dimensional Models
Huang, Jian; Ma, Shuangge
2012-01-01
Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties, and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.
Selected soil thermal conductivity models
Directory of Open Access Journals (Sweden)
Rerak Monika
2017-01-01
Full Text Available The paper presents collected from the literature models of soil thermal conductivity. This is a very important parameter, which allows one to assess how much heat can be transferred from the underground power cables through the soil. The models are presented in table form, thus when the properties of the soil are given, it is possible to select the most accurate method of calculating its thermal conductivity. Precise determination of this parameter results in designing the cable line in such a way that it does not occur the process of cable overheating.
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.
Energy Technology Data Exchange (ETDEWEB)
Isermann, Rolf [Technische Univ. Darmstadt (DE). Inst. fuer Automatisierungstechnik (IAT)
2011-07-01
Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book ''Fault-Diagnosis Systems'' published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers. (orig.)
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.
Model selection for pion photoproduction
Landay, J.; Döring, M.; Fernández-Ramírez, C.; Hu, B.; Molina, R.
2017-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory and K -fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (d σ /d Ω ), photon-beam asymmetries (Σ ), and target asymmetry differential cross sections (d σT/d ≡T d σ /d Ω ) in the low-energy regime.
The RNA polymerase I transcription machinery.
Russell, Jackie; Zomerdijk, Joost C B M
2006-01-01
The rRNAs constitute the catalytic and structural components of the ribosome, the protein synthesis machinery of cells. The level of rRNA synthesis, mediated by Pol I (RNA polymerase I), therefore has a major impact on the life and destiny of a cell. In order to elucidate how cells achieve the stringent control of Pol I transcription, matching the supply of rRNA to demand under different cellular growth conditions, it is essential to understand the components and mechanics of the Pol I transcription machinery. In this review, we discuss: (i) the molecular composition and functions of the Pol I enzyme complex and the two main Pol I transcription factors, SL1 (selectivity factor 1) and UBF (upstream binding factor); (ii) the interplay between these factors during pre-initiation complex formation at the rDNA promoter in mammalian cells; and (iii) the cellular control of the Pol I transcription machinery.
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.
Oils degradation in agricultural machinery
Directory of Open Access Journals (Sweden)
Vojtěch Kumbár
2013-01-01
Full Text Available Evaluating of oils condition in agricultural machinery is very important. With monitoring and evaluating we can prevent technical and economic losses. In this paper there were monitored the liquid lubricants taken from mobile thresher New Holland CX 860. Chemical and viscosity degradation of the lubricants were evaluated. Temperature dependence dynamic viscosity was observed in the range of temperature from −10 °C to 80 °C (for all oils. Considerable temperature dependence dynamic viscosity was found and demonstrated in case of all samples, which is in accordance with theoretical assumptions and literature data. Mathematical models were developed and tested. Temperature dependence dynamic viscosity was modeled using a polynomial 6th degree. The proposed models can be used for prediction of flow behavior of oils.
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 Par......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....
Selective Maintenance Model Considering Time Uncertainty
Le Chen; Zhengping Shu; Yuan Li; Xuezhi Lv
2012-01-01
This study proposes a selective maintenance model for weapon system during mission interval. First, it gives relevant definitions and operational process of material support system. Then, it introduces current research on selective maintenance modeling. Finally, it establishes numerical model for selecting corrective and preventive maintenance tasks, considering time uncertainty brought by unpredictability of maintenance procedure, indetermination of downtime for spares and difference of skil...
Institute of Scientific and Technical Information of China (English)
袁吉
2016-01-01
Taking the shield machine as an example, this paper uses composite life method and the annual summation method to calculate depreciation charges. Through calculation and comparison, we found total amount of depreciation charge and tax unchanged, the time of depreciation and taxes different, lead to the final cost of depreciation and tax amount appeared the 8.1% difference. Through comparative analysis, we found that the annual summation method is very beneficial for enterprises of construction machinery update because the final cost of depreciation value increased. In the case of constant total tax , tax time was pushed back,and the burden of the enterprise is reduced.%以盾构机为例，分别采用平均年限法和年数总和法计提折旧费，通过计算对比，发现分别采用两种折旧方法时，折旧费计提总额和税金总额不变，但因折旧费和税金发生的时点不同，导致折旧费和税金的终值出现了8.1%的差值。通过对比分析，发现采用加速折旧法时折旧费的终值增加对于企业工程机械的更新非常有利，在不减少企业纳税总额同时，也推迟了企业纳税的时间，减轻了企业的负担。
Matrix analysis of electrical machinery
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
Pumping machinery theory and practice
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
Institute of Scientific and Technical Information of China (English)
吴彪; 戴彤焱; 何挺继
2011-01-01
以经济性、技术性、协调性、适应性为主要元素,构成表征养护机械配置方案的属性集合;利用三角模糊数的相似度和可能度理论,构建高速公路养护机械合理配置方案的多属性决策模型,模型中通过单目标优化模型确定属性的最优权重向量.以某高速公路机械化养护中心的养护机械配置为例,证明该方法切实可行.%The attributes' sets characterizing maintenance machinery allocation schemes are first constituted using the economy, technicality, coordination and adaptability as the major elements. Then the multi-attribute decision-making model for freeway maintenance machinery allocation schemes is built based on triangular fuzzy number and combined with the concept of similarity degree and possibility degree. The optimal weights are derived by solving an optimization model of simple object. The application of the maintenance machinery allocation of a certain freeway mechanization conservation center is presented for demonstration. Case study shows that the method of multi-object decision-making is feasible and effective. The model provides a new method for decision-making for reasonable allocation of freeway maintenance machinery.
Bayesian Constrained-Model Selection for Factor Analytic Modeling
Peeters, Carel F.W.
2016-01-01
My dissertation revolves around Bayesian approaches towards constrained statistical inference in the factor analysis (FA) model. Two interconnected types of restricted-model selection are considered. These types have a natural connection to selection problems in the exploratory FA (EFA) and confirmatory FA (CFA) model and are termed Type I and Type II model selection. Type I constrained-model selection is taken to mean the determination of the appropriate dimensionality of a model. This type ...
A model-based approach to selection of tag SNPs
Directory of Open Access Journals (Sweden)
Sun Fengzhu
2006-06-01
Full Text Available Abstract Background Single Nucleotide Polymorphisms (SNPs are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD, a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets. Results Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of entropy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNP selection. Conclusion Our study provides strong evidence that the tag sets selected by our best method, based on Li and Stephens model, outperform those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the correct prediction rate of tagged SNPs. Besides, we show that haplotype phase uncertainty has an almost negligible impact on the ability of good tag sets to predict tagged SNPs. This justifies the selection of tag SNPs on the basis of haplotype
Skill Sheets for Agricultural Machinery.
Iowa State Univ. of Science and Technology, Ames. Dept. of Agricultural Education.
This set of 21 skill sheets for agricultural machinery was developed for use in high school and vocational school agricultural mechanics programs. Each sheet covers a single operational procedure for a piece of agricultural machinery, and includes: (1) a diagram, (2) a step-by-step operational procedure, (3) abilities or understandings taught, (4)…
Model selection bias and Freedman's paradox
Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.
2010-01-01
In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.
Selected Logistics Models and Techniques.
1984-09-01
ACCESS PROCEDURE: On-Line System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease...System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease arrangement. • SPONSOR: ASD/ACCC
Volkov Andrey Anatolevich; Rakhmonov Emomali Karimovich
2012-01-01
The authors consider the problems that accompany development of construction conflict management techniques using infographic modeling. The authors analyze comprehensive safety and comfort assurance requirements applicable to an intelligent building. The authors provide a brief overview of systems that comprise an intelligent building. The authors argue that there is a pressing need for the study of the fundamentals of construction conflict management as a new theoretical and a...
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
Energy Technology Data Exchange (ETDEWEB)
Asensio Ramos, A.; Manso Sainz, R.; Martinez Gonzalez, M. J.; Socas-Navarro, H. [Instituto de Astrofisica de Canarias, E-38205, La Laguna, Tenerife (Spain); Viticchie, B. [ESA/ESTEC RSSD, Keplerlaan 1, 2200 AG Noordwijk (Netherlands); Orozco Suarez, D., E-mail: aasensio@iac.es [National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588 (Japan)
2012-04-01
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.
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...
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
Model selection for amplitude analysis
Guegan, Baptiste; Stevens, Justin; Williams, Mike
2015-01-01
Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a data-driven method for limiting model complexity through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. A method is also proposed for obtaining the significance of a resonance in a multivariate amplitude analysis.
The Ouroboros Model, selected facets.
Thomsen, Knud
2011-01-01
The Ouroboros Model features a biologically inspired cognitive architecture. At its core lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. The activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed 'consumption analysis' is then checking how well such expectations fit with successive activations. Mismatches between anticipations based on previous experience and actual current data are highlighted and used for controlling the allocation of attention. A measure for the goodness of fit provides feedback as (self-) monitoring signal. The basic algorithm works for goal directed movements and memory search as well as during abstract reasoning. It is sketched how the Ouroboros Model can shed light on characteristics of human behavior including attention, emotions, priming, masking, learning, sleep and consciousness.
Random Effect and Latent Variable Model Selection
Dunson, David B
2008-01-01
Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Machinery. 169.241 Section 169.241 Shipping COAST GUARD... Certification Inspections § 169.241 Machinery. (a) At each inspection for certification and periodic inspection.... Mechanisms are operationally tested and visually examined. (3) Auxiliary machinery. All machinery...
Stochastic noise in splicing machinery.
Melamud, Eugene; Moult, John
2009-08-01
The number of known alternative human isoforms has been increasing steadily with the amount of available transcription data. To date, over 100 000 isoforms have been detected in EST libraries, and at least 75% of human genes have at least one alternative isoform. In this paper, we propose that most alternative splicing events are the result of noise in the splicing process. We show that the number of isoforms and their abundance can be predicted by a simple stochastic noise model that takes into account two factors: the number of introns in a gene and the expression level of a gene. The results strongly support the hypothesis that most alternative splicing is a consequence of stochastic noise in the splicing machinery, and has no functional significance. The results are also consistent with error rates tuned to ensure that an adequate level of functional product is produced and to reduce the toxic effect of accumulation of misfolding proteins. Based on simulation of sampling of virtual cDNA libraries, we estimate that error rates range from 1 to 10% depending on the number of introns and the expression level of a gene.
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.
Property Analysis of the Agricultural Machinery Lubricants
Directory of Open Access Journals (Sweden)
Tone Ploj
2000-03-01
Full Text Available We need to produce enough healthy and cheap food as well as to preserve the ecologic equilibrium. This can be achived by using modern machinery and up- to-date knowledge and technology. Agricultural machinery, in which 40-60% of all funds are invested, is poorly maintained and underused. The main causes for this are poor knowledge and extensive farm land fragmentation. The fact that over 140,000 tractors in Slovenia are on average 9.6 years old, i.e. that more than 80% of overall agricultural machinery is obsolete, should be a matter of serious concern. In the paper we follow tribological conditions in particular tractor assemblies. In the first part of the paper we have treated the required conditions of tractor manufacturers in Europe and primarily in Slovenia, what has served us in the final phase of the research for elaboration of the model. In this way we have got data about the presence of particular tractor types. We have separately elaborated the necessary specifications of engine lubricants, transmission, gears, hydraulics and wet breaks. We have carried out chemical and mechanical analyses of all accessible lubricants in agricultural mechanisation. The results of the new oils were coordinated with the required specifications of tractor manufacturers and so we have got such a model, that certainly meet all lubricating requirements of our tractors.
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...
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...
Melody Track Selection Using Discriminative Language Model
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.
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.
Textile Machinery: Imports Rebound Again
Institute of Scientific and Technical Information of China (English)
Zheng Yan
2007-01-01
@@ In the year of 2006, the general situation of China's textile machinery equipment imports had shown a clear sign of revival from the downward trend of two years ago, with a total annual import of 4.1 billion USD, an increase of 19.05% against the same period of 2005.
Tractor & Machinery Safety. 1984 Revision.
Montana State Office of Public Instruction, Helena. Dept. of Vocational Education Services.
This curriculum guide is intended for use in teaching an instructional unit in tractor and machinery safety that is geared toward college freshmen. Addressed in the individual lessons of the unit are the following topics: understanding the importance of safe and efficient tractor operation, understanding the characteristics of tractors, preparing…
Bayesian variable selection for latent class models.
Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria
2011-09-01
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.
Factors associated with small-scale agricultural machinery adoption in Bangladesh: Census findings.
Mottaleb, Khondoker Abdul; Krupnik, Timothy J; Erenstein, Olaf
2016-08-01
There is strong advocacy for agricultural machinery appropriate for smallholder farmers in South Asia. Such 'scale-appropriate' machinery can increase returns to land and labour, although the still substantial capital investment required can preclude smallholder ownership. Increasing machinery demand has resulted in relatively well-developed markets for rental services for tillage, irrigation, and post-harvest operations. Many smallholders thereby access agricultural machinery that may have otherwise been cost prohibitive to purchase through fee-for-service arrangements, though opportunity for expansion remains. To more effectively facilitate the development and investment in scale-appropriate machinery, there is a need to better understand the factors associated with agricultural machinery purchases and service provision. This paper first reviews Bangladesh's historical policy environment that facilitated the development of agricultural machinery markets. It then uses recent Bangladesh census data from 814,058 farm households to identify variables associated with the adoption of the most common smallholder agricultural machinery - irrigation pumps, threshers, and power tillers (mainly driven by two-wheel tractors). Multinomial probit model results indicate that machinery ownership is positively associated with household assets, credit availability, electrification, and road density. These findings suggest that donors and policy makers should focus not only on short-term projects to boost machinery adoption. Rather, sustained emphasis on improving physical and civil infrastructure and services, as well as assuring credit availability, is also necessary to create an enabling environment in which the adoption of scale-appropriate farm machinery is most likely.
Dresig, Hans
2010-01-01
Dynamic loads and disturbing oscillations increase with higher speed of the machines and more lightweight constructions. Industrial safety standards require better oscillation reduction and noise control. The book by Dresig/Holzweissig deals with these topics. It presents the classical areas of modeling, dynamics of rigid bodies, balancing, torsional and bending vibrations, problems of vibration isolation and the dynamic behavior of complex vibrating systems. Typical dynamic effects, i.e., the gyroscopic effect, the damping of oscillations, resonances of k-th order, subharmonic and nonlinear f
MODEL SELECTION FOR LOG-LINEAR MODELS OF CONTINGENCY TABLES
Institute of Scientific and Technical Information of China (English)
ZHAO Lincheng; ZHANG Hong
2003-01-01
In this paper, we propose an information-theoretic-criterion-based model selection procedure for log-linear model of contingency tables under multinomial sampling, and establish the strong consistency of the method under some mild conditions. An exponential bound of miss detection probability is also obtained. The selection procedure is modified so that it can be used in practice. Simulation shows that the modified method is valid. To avoid selecting the penalty coefficient in the information criteria, an alternative selection procedure is given.
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.
Adaptive Covariance Estimation with model selection
Biscay, Rolando; Loubes, Jean-Michel
2012-01-01
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.
Vibrations of rotating machinery
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...
A Theoretical Model for Selective Exposure Research.
Roloff, Michael E.; Noland, Mark
This study tests the basic assumptions underlying Fishbein's Model of Attitudes by correlating an individual's selective exposure to types of television programs (situation comedies, family drama, and action/adventure) with the attitudinal similarity between individual attitudes and attitudes characterized on the programs. Twenty-three college…
Energy Savings Thanks to French Textile Machinery
Institute of Scientific and Technical Information of China (English)
2010-01-01
@@ The French Textile Machinery Manufacturers' Association (UCMTF) has presented, during a seminar it organized for textile professionals and students, the spectacular energy savings achieved thanks to state of the art machinery.
Italian Textile Machinery Seminar in Bangladesh
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The Association of Italian Textile Machinery Manufacturers (ACIMIT) and the Italian Trade Commission will hold a technological seminar on "Italian textile machinery: the way to improve Bangladesh textile competitiveness"
Model selection for radiochromic film dosimetry
Méndez, Ignasi
2015-01-01
The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to...
30 CFR 56.14204 - Machinery lubrication.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machinery lubrication. 56.14204 Section 56.14204 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Equipment Safety Practices and Operational Procedures § 56.14204 Machinery lubrication. Machinery...
30 CFR 57.14204 - Machinery lubrication.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machinery lubrication. 57.14204 Section 57.14204 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR METAL AND NONMETAL... Equipment Safety Practices and Operational Procedures § 57.14204 Machinery lubrication. Machinery...
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Machinery. 176.804 Section 176.804 Shipping COAST GUARD... CERTIFICATION Material Inspections § 176.804 Machinery. At each initial and subsequent inspection for... ready for inspections of machinery, fuel, and piping systems, including the following: (a) Operation...
46 CFR 130.450 - Machinery alarms.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Machinery alarms. 130.450 Section 130.450 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) OFFSHORE SUPPLY VESSELS VESSEL CONTROL, AND MISCELLANEOUS EQUIPMENT AND SYSTEMS Automation of Unattended Machinery Spaces § 130.450 Machinery alarms....
Portfolio Selection Model with Derivative Securities
Institute of Scientific and Technical Information of China (English)
王春峰; 杨建林; 蒋祥林
2003-01-01
Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed-integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
Directory of Open Access Journals (Sweden)
M. Laine
2008-12-01
Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.
The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.
We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
On Model Selection Criteria in Multimodel Analysis
Energy Technology Data Exchange (ETDEWEB)
Ye, Ming; Meyer, Philip D.; Neuman, Shlomo P.
2008-03-21
Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions. There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (a) rank these models, (b) eliminate some of them and/or (c) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as AIC [Akaike, 1974], AICc [Hurvich and Tsai, 1989], BIC [Schwartz, 1978] and KIC [Kashyap, 1982] and some lack of clarity about the proper interpretation and mathematical representation of each criterion. In particular, whereas we [Neuman, 2003; Ye et al., 2004, 2005; Meyer et al., 2007] have based our approach to multimodel hydrologic ranking and inference on the Bayesian criterion KIC (which reduces asymptotically to BIC), Poeter and Anderson [2005] and Poeter and Hill [2007] have voiced a preference for the information-theoretic criterion AICc (which reduces asymptotically to AIC). Their preference stems in part from a perception that KIC and BIC require a "true" or "quasi-true" model to be in the set of alternatives while AIC and AICc are free of such an unreasonable requirement. We examine the model selection literature to find that (a) all published rigorous derivations of AIC and AICc require that the (true) model having generated the observational data be in the set of candidate models; (b) though BIC and KIC were originally derived by assuming that such a model is in the set, BIC has been rederived by Cavanaugh and Neath [1999] without the need for such an assumption; (c) KIC reduces to BIC as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives; (d) if a true
A Neurodynamical Model for Selective Visual Attention
Institute of Scientific and Technical Information of China (English)
QU Jing-Yi; WANG Ru-Bin; ZHANG Yuan; DU Ying
2011-01-01
A neurodynamical model for selective visual attention considering orientation preference is proposed. Since orientation preference is one of the most important properties of neurons in the primary visual cortex, it should be fully considered besides external stimuli intensity. By tuning the parameter of orientation preference, the regimes of synchronous dynamics associated with the development of the attention focus are studied. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics correspond to the partial synchronization mode. Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another, which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.%A neurodynamical model for selective visual attention considering orientation preference is proposed.Since orientation preference is one of the most important properties of neurons in the primary visual cortex,it should be fully considered besides external stimuli intensity.By tuning the parameter of orientation preference,the regimes of synchronous dynamics associated with the development of the attention focus are studied.The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed.Such dynamics correspond to the partial synchronization mode.Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another,which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.Selective visual
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.
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; 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 parsimoneous 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 parsimoneous...... 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....
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....
Skewed factor models using selection mechanisms
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.
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.
Multi-dimensional model order selection
Directory of Open Access Journals (Sweden)
Roemer Florian
2011-01-01
Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.
Model selection and comparison for independents sinusoids
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online....
Tracking Models for Optioned Portfolio Selection
Liang, Jianfeng
In this paper we study a target tracking problem for the portfolio selection involving options. In particular, the portfolio in question contains a stock index and some European style options on the index. A refined tracking-error-variance methodology is adopted to formulate this problem as a multi-stage optimization model. We derive the optimal solutions based on stochastic programming and optimality conditions. Attention is paid to the structure of the optimal payoff function, which is shown to possess rich properties.
New insights in portfolio selection modeling
Zareei, Abalfazl
2016-01-01
Recent advancements in the field of network theory commence a new line of developments in portfolio selection techniques that stands on the ground of perceiving financial market as a network with assets as nodes and links accounting for various types of relationships among financial assets. In the first chapter, we model the shock propagation mechanism among assets via network theory and provide an approach to construct well-diversified portfolios that are resilient to shock propagation and c...
Robust inference in sample selection models
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.
Bayesian model selection in Gaussian regression
Abramovich, Felix
2009-01-01
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.
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...... 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...
Strategies for Improving Enterprise Standardization Management of Tropical Crop Machinery
Institute of Scientific and Technical Information of China (English)
无
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.
Inflation model selection meets dark radiation
Tram, Thomas; Vallance, Robert; Vennin, Vincent
2017-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species Neff. Using a minimal dataset (Planck low-l polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when Neff is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H0 makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H0 tension would have deep consequences for inflation model selection.
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing...... 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......Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent...
The Markowitz model for portfolio selection
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MARIAN ZUBIA ZUBIAURRE
2002-06-01
Full Text Available Since its first appearance, The Markowitz model for portfolio selection has been a basic theoretical reference, opening several new development options. However, practically it has not been used among portfolio managers and investment analysts in spite of its success in the theoretical field. With our paper we would like to show how The Markowitz model may be of great help in real stock markets. Through an empirical study we want to verify the capability of Markowitz’s model to present portfolios with higher profitability and lower risk than the portfolio represented by IBEX-35 and IGBM indexes. Furthermore, we want to test suggested efficiency of these indexes as representatives of market theoretical-portfolio.
Model selection for Poisson processes with covariates
Sart, Mathieu
2011-01-01
We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. To handle this problem, we assume that the intensity of each Poisson process is of the form $s (\\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function. We propose a model selection approach where the models are used to approximate the multivariate function $s$. We show that our estimator satisfies an oracle-type inequality under very weak assumptions both on the intensities and the models. By using an Hellinger-type loss, we establish non-asymptotic risk bounds and specify them under various kind of assumptions on the target function $s$ such as being smooth or composite. Besides, we show that our estimation procedure is robust with respect to these assumptions.
Information criteria for astrophysical model selection
Liddle, A R
2007-01-01
Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.
Entropic Priors and Bayesian Model Selection
Brewer, Brendon J
2009-01-01
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual Bayesian "Occam's Razor". This is illustrated with a simple example involving what Jaynes called a "sure thing" hypothesis. Jaynes' resolution of the situation involved introducing a large number of alternative "sure thing" hypotheses that were possible before we observed the data. However, in more complex situations, it may not be possible to explicitly enumerate large numbers of alternatives. The entropic priors formalism produces the desired result without modifying the hypothesis space or requiring explicit enumeration of alternatives; all that is required is a good model for the prior predictive distribution for the data. This idea is illustrated with a simple rigged-lottery example, and we outline how this idea may help to resolve a recent debate amongst ...
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.
Appropriate model selection methods for nonstationary generalized extreme value models
Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng
2017-04-01
Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.
STUDY OF THE PARAMETERS OF EFFICIENCY IN CENTRES FOR REPAIR OF AGRICULTURAL MACHINERY
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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.
Ancestral process and diffusion model with selection
Mano, Shuhei
2008-01-01
The ancestral selection graph in population genetics introduced by Krone and Neuhauser (1997) is an analogue to the coalescent genealogy. The number of ancestral particles, backward in time, of a sample of genes is an ancestral process, which is a birth and death process with quadratic death and linear birth rate. In this paper an explicit form of the number of ancestral particle is obtained, by using the density of the allele frequency in the corresponding diffusion model obtained by Kimura (1955). It is shown that fixation is convergence of the ancestral process to the stationary measure. The time to fixation of an allele is studied in terms of the ancestral process.
Improving randomness characterization through Bayesian model selection
R., Rafael Díaz-H; Martínez, Alí M Angulo; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Castillo, Isaac Pérez
2016-01-01
Nowadays random number generation plays an essential role in technology with important applications in areas ranging from cryptography, which lies at the core of current communication protocols, to Monte Carlo methods, and other probabilistic algorithms. In this context, a crucial scientific endeavour is to develop effective methods that allow the characterization of random number generators. However, commonly employed methods either lack formality (e.g. the NIST test suite), or are inapplicable in principle (e.g. the characterization derived from the Algorithmic Theory of Information (ATI)). In this letter we present a novel method based on Bayesian model selection, which is both rigorous and effective, for characterizing randomness in a bit sequence. We derive analytic expressions for a model's likelihood which is then used to compute its posterior probability distribution. Our method proves to be more rigorous than NIST's suite and the Borel-Normality criterion and its implementation is straightforward. We...
Developing Process of Tropical Crop Machinery Standardization
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
@@ General Situation Tropical crop machinery is a new special mechanical profession, which began to develop from 1950s to 1960s in China. Because the weather, soil and farm crops varieties in tropical region are greatly different from those in the other regions, most of the traditional farm machinery can't be directly used in tropical region or on the tropical crops. Tropical crop machinery needs a special design and manufacture. So some professional research institutes and education units were set up and some enterprises were built at that time, and the profession of tropical crop machinery was formed.
Membrane manipulations by the ESCRT machinery.
Odorizzi, Greg
2015-01-01
The endosomal sorting complexes required for transport (ESCRTs) collectively comprise a machinery that was first known for its function in the degradation of transmembrane proteins in the endocytic pathway of eukaryotic cells. Since their discovery, however, ESCRTs have been recognized as playing important roles at the plasma membrane, which appears to be the original site of function for the ESCRT machinery. This article reviews some of the major research findings that have shaped our current understanding of how the ESCRT machinery controls membrane dynamics and considers new roles for the ESCRT machinery that might be driven by these mechanisms.
Inflation Model Selection meets Dark Radiation
Tram, Thomas; Vennin, Vincent
2016-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard $\\Lambda\\mathrm{CDM}$ model and an extension including dark radiation parametrised by its effective number of relativistic species $N_\\mathrm{eff}$. We find that the observational status of most inflationary models is unchanged, with the exception of potentials such as power-law inflation that predict a value for the scalar spectral index that is too large in $\\Lambda\\mathrm{CDM}$ but which can be accommodated when $N_\\mathrm{eff}$ is allowed to vary. In this case, cosmic microwave background data indicate that power-law inflation is one of the best models together with plateau potentials. However, contrary to plateau p...
Analysis of noise control measures on outdoor machinery using EQUIP+
Dittrich, M.G.
2006-01-01
Noise control of different types of outdoor machinery covered by EU Directive 2000/14/EC such as construction machines, generators and other equipment powered by internal combustion engines requires knowledge of the noise path model and the potential noise control measures. As there is often a
FOREWORD: 26th IAHR Symposium on Hydraulic Machinery and Systems
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
High-dimensional model estimation and model selection
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.
Fuzzy modelling for selecting headgear types.
Akçam, M Okan; Takada, Kenji
2002-02-01
The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.
SLAM: A Connectionist Model for Attention in Visual Selection Tasks.
Phaf, R. Hans; And Others
1990-01-01
The SeLective Attention Model (SLAM) performs visual selective attention tasks and demonstrates that object selection and attribute selection are both necessary and sufficient for visual selection. The SLAM is described, particularly with regard to its ability to represent an individual subject performing filtering tasks. (TJH)
Multi-state targeting machinery govern the fidelity and efficiency of protein localization.
Yang, Mingjun; Pang, Xueqin; Han, Keli
2014-01-01
Proper localization of newly synthesized proteins is essential to cellular function. Among different protein localization modes, the signal recognition particle (SRP) and SRP receptor (SR) constitute the conserved targeting machinery in all three life kingdoms and mediate about one third of the protein targeting reactions. Based on experimental and computational studies, a detailed molecular model is proposed to explain how this molecular machinery governs the efficiency and fidelity of protein localizations. In this targeting machinery, two distinct SRP GTPases are contained into the SRP and SR that are responsible to the interactions between SRP and SR. These two GTPases can interact with one another through a series of sequential and discrete interaction states that are the early intermediate formation, stable complex association, and GTPase activation. In contrast to canonical GTPases, a floppy and open conformation adopted in free SRP GTPases can facilitate efficient GTP/GDP exchange without the aid of any external factors. As the apo-form free SRP GTPases can adopt the conformational states of GDP- or GTP-bound form, the binding of GTP/GDP follows a mechanism of conformational selection. In the first step of complex formation, the two SRP GTPases can rapidly assemble into an unstable early intermediate by selecting and stabilizing one another's primed states from the equilibrium conformational ensemble. Subsequently, extensive inter- and intra-domain changes rearrange the early complex into a tight and closed state of stable complex through induced fit mechanism. Upon stable complex association, further tune of several important interaction networks activates the SRP GTPase for GTP hydrolysis. These different conformational states are coupled to corresponding protein targeting events, in which the complex formation deliveries the translating ribosome to the target membrane and the GTPase activation couples to the cargo release from SRP-SR machinery to the
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.
The Olympic of Textile Machinery closed
Institute of Scientific and Technical Information of China (English)
无
2012-01-01
The five days 2012 China International Textile Machinery Exhibition ITMA Asia Exhibition, which attracted much attention from the industry, was closed at the Shanghai New International Expo Center on June 16, more than 1300 textile enterprises from nearly 30 countries around the world gathered on the exhibition, the world’s latest textile machinery technologies, crafts and equipments were also presented one by one.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Machinery. 115.804 Section 115.804 Shipping COAST GUARD....804 Machinery. At each initial and subsequent inspection for certification of a vessel, the owner or managing operator shall be prepared to conduct tests and have the vessel ready for inspections of...
China Textile Machinery Expresses Self-Surpassing
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
It’s difficult to imagine that any sector of the textile industry has benefited more from innovations in the past 10 years than textile machinery. Advanced textile machinery has brought new life to the production segment of the business and fulfills the essential preconditions for economically efficient textile production.
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.
46 CFR 58.01-40 - Machinery, angles of inclination.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery, angles of inclination. 58.01-40 Section 58.01... AUXILIARY MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-40 Machinery, angles of inclination. (a) Propulsion machinery and all auxiliary machinery essential to the propulsion and safety of the vessel must...
46 CFR 58.01-45 - Machinery space, ventilation.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery space, ventilation. 58.01-45 Section 58.01-45... MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-45 Machinery space, ventilation. Each machinery space must be ventilated to ensure that, when machinery or boilers are operating at full power in...
B2B Marketing in the Flexographic Machinery Industry
Verner, Jan
2012-01-01
Soma Engineering, Czech engineering company, designed its new marketing strategy in order to succeed amongst its competitors in the flexographic machinery market. Goal of the thesis is to verify if the new marketing strategy of SOMA engineering leads to desired positioning of the company and its products in selected markets. In case, the research indicates that the strategy does not work, identify the areas/attributes where the company lacks behind the desired positioning. Desired positioning...
Selection in spatial stochastic models of cancer: Migration as a key modulator of fitness
Directory of Open Access Journals (Sweden)
Stupack Dwayne
2010-04-01
Full Text Available Abstract Background We study the selection dynamics in a heterogeneous spatial colony of cells. We use two spatial generalizations of the Moran process, which include cell divisions, death and migration. In the first model, migration is included explicitly as movement to a proximal location. In the second, migration is implicit, through the varied ability of cell types to place their offspring a distance away, in response to another cell's death. Results In both models, we find that migration has a direct positive impact on the ability of a single mutant cell to invade a pre-existing colony. Thus, a decrease in the growth potential can be compensated by an increase in cell migration. We further find that the neutral ridges (the set of all types with the invasion probability equal to that of the host cells remain invariant under the increase of system size (for large system sizes, thus making the invasion probability a universal characteristic of the cells selection status. We find that repeated instances of large scale cell-death, such as might arise during therapeutic intervention or host response, strongly select for the migratory phenotype. Conclusions These models can help explain the many examples in the biological literature, where genes involved in cell's migratory and invasive machinery are also associated with increased cellular fitness, even though there is no known direct effect of these genes on the cellular reproduction. The models can also help to explain how chemotherapy may provide a selection mechanism for highly invasive phenotypes. Reviewers This article was reviewed by Marek Kimmel and Glenn Webb.
The Management of Park Equipment and Machinery Used in the Construction Industry and Housing Sector
Khayrullin Rustam; Marichev Pavel
2016-01-01
The mathematical model was created, applicable to elaboration of options of development of a park of equipment and machinery used in construction industry, housing and utility management. The model based on solving the linear programming problem by means of simplex method. The mathematical model allows develop the rational plans and quasi optimal plans. The model is based on the created in the article the financial management algorithms of procurement and of repairs of equipment and machinery...
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.
The Management of Park Equipment and Machinery Used in the Construction Industry and Housing Sector
Directory of Open Access Journals (Sweden)
Khayrullin Rustam
2016-01-01
Full Text Available The mathematical model was created, applicable to elaboration of options of development of a park of equipment and machinery used in construction industry, housing and utility management. The model based on solving the linear programming problem by means of simplex method. The mathematical model allows develop the rational plans and quasi optimal plans. The model is based on the created in the article the financial management algorithms of procurement and of repairs of equipment and machinery. The algorithms allow to take into account joint consideration of indicators of modernity and operability of equipment and machinery. The basic model is developed. The model is included into the automated planning system. It is used for planning and preparation of proposals of draft policy documents for development of park devices, equipment and machinery.
The detection of observations possibly influential for model selection
Ph.H.B.F. Franses (Philip Hans)
1991-01-01
textabstractModel selection can involve several variables and selection criteria. A simple method to detect observations possibly influential for model selection is proposed. The potentials of this method are illustrated with three examples, each of which is taken from related studies.
Institute of Scientific and Technical Information of China (English)
刘超; 谭冬梅
2012-01-01
Total dynamic structure of agricultural machinery is an embodiment of development in agricultural machinery items, so dispatching the total dynamics of agricultural machinery rationally can drive its development. On the basis of dividing dynamic structure of agricultural machinery from power source and agricultural machinery items, according to the theory of econometrics, taken the hilly area as the background, the interaction relationship between the total dynamics of agricultural machinery and its structure configuration was constructed, and a comprehensive prediction model for analysis of dynamic structure with multiple test feedback mechanism was established, which has been applied in practice and satisfying results were obtained.%农业机械总动力结构是农业机械项目发展水平高低的具体体现,合理配置农业机械总动力,可有序地推动农业机械化的发展.从动力源和农业机械项目两方面划分农业机械总动力结构的基础上,依据经济计量学理论,以丘陵地区为背景,构造了农业机械总动力及其结构配置影响关系,建立了具有多重检验反馈机制的动力结构综合分析预测数学模型,并在实际中加以应用,可得到满意结果.
Selective experimental review of the Standard Model
Energy Technology Data Exchange (ETDEWEB)
Bloom, E.D.
1985-02-01
Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are ..cap alpha../sub s/, ..cap alpha../sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, M..mu.., M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta/sub 1/, theta/sub 2/, theta/sub 3/, and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant ..cap alpha../sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring ..cap alpha../sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures.
An integrated model for supplier selection process
Institute of Scientific and Technical Information of China (English)
无
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.
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...... 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...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models....
Institute of Scientific and Technical Information of China (English)
孟庆瑞; 田兆锋; 张书明; 阎楚良
2009-01-01
Against the background of implementing load spectrum data management system of the typical agricultural machinery, the load spectrum data management system was designed and implemented using the technology of . NET in the mixed model of C/S and B/S. Then overall plan and structure model of the system were worked out according to actual situation and detailed requirements of engineering application. Finally, each system module was coded and debugged. Thus, it can be implemented for the modernization management of load spectrum data, adding communication channels and improving efficiency of structural fatigue research.%以拖拉机、联合收获机等典型农业机械载荷谱数据库建设工作为背景,基于B/S与C/S混合模式,利用.NET技术体系设计与实现载荷谱数据库信息管理系统.针对实际情况和工程应用的具体需求制定了系统的总体方案,设计了系统的结构模型,最后完成系统具体模块的开发调试.系统可实现载荷谱数据现代化管理、增加信息沟通渠道、提高结构疲劳寿命研究工作的效率.
Textile Machinery:Imports Rebound Again
Institute of Scientific and Technical Information of China (English)
2007-01-01
In the year of 2006,the general situation of China’s textile machinery equipment imports had shown a clear sign of revival from the downward trend of two years ago,with a total annual import of 4.1 billion USD,an increase of 19.05% against the same period of 2005. Continuingly,the year of 2007 has witnessed a sustainable growth trend of textile machinery equipment imports in the first quarter and the trend definitely will be maintained through the whole year.According to statistics released from China Customs,the imports of textile machinery reached 1.098 billion USD in the first three months of 2007, up by 35.26% year-on-year. Then,why China’s textile machinery imports warm up again after two years’ cool down?
A comprehensive overview of hybrid construction machinery
Directory of Open Access Journals (Sweden)
Jixin Wang
2016-03-01
Full Text Available With the increasing attention of energy saving and emission reduction technology, the recent application of hybrid powertrain technology affects the development of construction machinery industry. This article reviews these publications and provides comprehensive references. This article reviews the state-of-art for the hybrid wheel loader and excavator, which focuses on powertrain configuration, energy storage devices, and energy management strategies. The basis of classification and characteristic of each powertrain configuration are described. Advantages and disadvantages of batteries, supercapacitors, hydraulic accumulators, and flywheel used in hybrid construction machinery are summarized. The existing energy management strategies for hybrid construction machinery are also elaborated. The technological challenges and developing trends in the near future for hybrid construction machinery are discussed.
Energy Savings Thanks to French Textile Machinery
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The French Textile Machinery Manufacturers’ Association (UCMTF) has presented,during a seminar it organized for textile professionals and students,the spectacular energy savings achieved thanks to state of the art
Model for personal computer system selection.
Blide, L
1987-12-01
Successful computer software and hardware selection is best accomplished by following an organized approach such as the one described in this article. The first step is to decide what you want to be able to do with the computer. Secondly, select software that is user friendly, well documented, bug free, and that does what you want done. Next, you select the computer, printer and other needed equipment from the group of machines on which the software will run. Key factors here are reliability and compatibility with other microcomputers in your facility. Lastly, you select a reliable vendor who will provide good, dependable service in a reasonable time. The ability to correctly select computer software and hardware is a key skill needed by medical record professionals today and in the future. Professionals can make quality computer decisions by selecting software and systems that are compatible with other computers in their facility, allow for future net-working, ease of use, and adaptability for expansion as new applications are identified. The key to success is to not only provide for your present needs, but to be prepared for future rapid expansion and change in your computer usage as technology and your skills grow.
Textile Machinery Import and Export in 2011
Institute of Scientific and Technical Information of China (English)
2012-01-01
Along with the rebounded international market, in the year of 2011, the foreign trade of textile machinery industry preserved a stable development： the import amount saw a slightly decrease, while the total import and export value kept an increasing trend. According to the Customs, the export and import of textile machinery totalized 2.245 billion USD and 5.364 billion USD, increasing by 27.81% and 24.70%, respectively, comparing with the same period of time in 2010.
Function complex for automated system of coke machinery remote control
Energy Technology Data Exchange (ETDEWEB)
Simonov, N.F.; Pankrat' ev, O.N.; Bannikov, L.S.; Slatin, E.I.; Parfenov, G.I.
1979-05-01
this paper discusses a functional control system for remote control of coking plants introduced at the KBAiM of the Giprokoks. The control block allows for three modes of operation: fully automatic, by predesignated program according to oven design and technology; semi-automatic, in which individual programs perform automatically, checked and initiated by the operator; and remote, in which the operator controls each operation from the control console. The functions of selecting the location for the coke machinery, signal transmission and control selection have been incorporated as three autonomous but interfacing systems. (In Russian)
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; van den Berg, Stéphanie Martine
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2016-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Institute of Scientific and Technical Information of China (English)
黄晨; 孙晓强
2015-01-01
In order to improve vibration isolation performance of cab suspension system and ride comfort of construction machinery, a new type cab suspension system with inerter is presented, which mainly consists of main spring, auxiliary spring, damper and a new vibration isolation compo-nent named as inerter. In order to determine the optimal parameters of the new type suspension sys-tem, a three-DOF full floating cab dynamics mathematical model is established based on Newton's laws of motion and the suspension parameters optimal objective and constraints are defined. The par-ticle swarm optimization ( PSO) algorithm is applied for optimization of the cab suspension parame-ters and the simulation comparison results indicate that the weighted root mean square value of the cab floor vertical acceleration is reduced from 0. 42 to 0. 33 , which has a decline by 21%, thus the vehicle ride comfort of construction machinery is effectively improved. The research of this paper can provide theoretical foundation and technical support for analyzing and improving performance of new type cab suspension system for construction machinery based on inerter.%为进一步提高工程机械驾驶室悬置系统的隔振性能,改善工程机械的乘坐舒适性,提出一种包含惯容器的新型工程机械驾驶室悬置系统,该系统由主弹簧、副弹簧、减振器以及新型隔振元件———惯容器等四个基本元件构成。为确定该新型悬置的最优结构参数,基于牛顿运动定律建立了工程机械全浮式驾驶室悬置三自由度动力学数学模型,确定了驾驶室悬置参数的优化目标及约束条件,并采用粒子群优化算法对工程机械驾驶室悬置参数进行了优化。仿真对比结果表明,基于最优参数的新型悬置系统可使驾驶室地板垂向振动加权加速度均方根值由0.42降低到0.33,降幅达21%,工程机械的乘坐舒适性得到了明显提高,研究将为基于惯容器的新型工程机械驾
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....
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....
Institute of Scientific and Technical Information of China (English)
廖一专; 肖田元; 韩丹
2014-01-01
介绍了制造执行系统（ MES ）领域的企业-控制系统集成标准ANSI/ISA-95与其应用现状。利用制造运行管理活动模型分析，提取出纺机企业实际生产流程中关键性制造活动及功能、硬件需求，以辅助MES方案设计。通过纺机制造行业MES案例，展示了该标准在行业应用的实践指导作用。%It introduces current international standards of enterprise -control system integration of manufacturing execution system ( MES) ANSI/ISA-95 and its application in industry .By using manufacturing operation man-agement activity model analysis , it defines the critical function needs and hardware requirements of production processes , and assists MES solution design .It presents a MES case of textile machinery manufacturing and illus-tratese the ANSI/ISA-95 applications in industrial field .
Cardinality constrained portfolio selection via factor models
Monge, Juan Francisco
2017-01-01
In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Markowitz model. The addition of the cardinality constraints limits the number of securities in the portfolio. Restricting the number of securities in the portfolio allows us to o...
Schmitt, C K; Ikeda, J S; Darnell, S C; Watson, P R; Bispham, J; Wallis, T S; Weinstein, D L; Metcalf, E S; O'Brien, A D
2001-09-01
In this study, we constructed an flhD (the master flagellar regulator gene) mutant of Salmonella enterica serovar Typhimurium and compared the virulence of the strain to that of the wild-type strain in a series of assays that included the mouse model of typhoid fever, the mouse macrophage survival assay, an intestinal epithelial cell adherence and invasion assay, and the calf model of enterocolitis. We found that the flhD mutant was more virulent than its parent in the mouse and displayed slightly faster net growth between 4 and 24 h of infection in mouse macrophages. Conversely, the flhD mutant exhibited diminished invasiveness for human and mouse intestinal epithelial cells, as well as a reduced capacity to induce fluid secretion and evoke a polymorphonuclear leukocyte response in the calf ligated-loop assay. These findings, taken with the results from virulence assessment assays done on an fljB fliC mutant of serovar Typhimurium that does not produce flagellin but does synthesize the flagellar secretory apparatus, indicate that neither the presence of flagella (as previously reported) nor the synthesis of the flagellar export machinery are necessary for pathogenicity of the organism in the mouse. Conversely, the presence of flagella is required for the full invasive potential of the bacterium in tissue culture and for the influx of polymorphonuclear leukocytes in the calf intestine, while the flagellar secretory components are also necessary for the induction of maximum fluid secretion in that enterocolitis model. A corollary to this conclusion is that, as has previously been surmised but not demonstrated in a comparative investigation of the same mutant strains, the mouse systemic infection and macrophage assays measure aspects of virulence different from those of the tissue culture invasion assay, and the latter is more predictive of findings in the calf enterocolitis model.
Evidence accumulation as a model for lexical selection
Anders, R.; Riès, S.; van Maanen, L.; Alario, F.-X.
2015-01-01
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
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
46 CFR 58.20-15 - Installation of refrigerating machinery.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Installation of refrigerating machinery. 58.20-15... AND AUXILIARY MACHINERY AND RELATED SYSTEMS Refrigeration Machinery § 58.20-15 Installation of refrigerating machinery. (a) Where refrigerating machines are installed in which anhydrous ammonia is used as...
46 CFR 169.625 - Compartments containing diesel machinery.
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Compartments containing diesel machinery. 169.625... SCHOOL VESSELS Machinery and Electrical Ventilation § 169.625 Compartments containing diesel machinery. (a) Spaces containing machinery must be fitted with adequate dripproof ventilators, trunks,...
46 CFR 174.195 - Bulkheads in machinery spaces.
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Bulkheads in machinery spaces. 174.195 Section 174.195... in machinery spaces. (a) The bulkhead in each machinery space of each OSV must be watertight to the bulkhead deck. (b) Each penetration of, and each opening in, a bulkhead in a machinery space must— (1)...
46 CFR 58.01-35 - Main propulsion auxiliary machinery.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Main propulsion auxiliary machinery. 58.01-35 Section 58... AUXILIARY MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-35 Main propulsion auxiliary machinery. Auxiliary machinery vital to the main propulsion system must be provided in duplicate unless the...
46 CFR 58.01-25 - Means of stopping machinery.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Means of stopping machinery. 58.01-25 Section 58.01-25 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) MARINE ENGINEERING MAIN AND AUXILIARY MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-25 Means of stopping machinery. Machinery...
46 CFR 42.15-35 - Machinery space openings.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery space openings. 42.15-35 Section 42.15-35... BY SEA Conditions of Assignment of Freeboard § 42.15-35 Machinery space openings. (a) Machinery space..., funnel, or machinery space ventilators in an exposed position on the freeboard or superstructure...
46 CFR 45.149 - Machinery space openings.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery space openings. 45.149 Section 45.149 Shipping... Assignment § 45.149 Machinery space openings. (a) Machinery space openings in position 1 or 2 must be framed... funnel or machinery space ventilator that must be kept open for the essential operations of the ship...
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.
Selection of Temporal Lags When Modeling Economic and Financial Processes.
Matilla-Garcia, Mariano; Ojeda, Rina B; Marin, Manuel Ruiz
2016-10-01
This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.
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...
Ohio State Univ., Columbus. Center for Vocational and Technical Education.
ONE OF A SERIES DESIGNED FOR HELPING TEACHERS PREPARE POSTSECONDARY-LEVEL STUDENTS FOR AGRICULTURAL MACHINERY SERVICE OCCUPATIONS AS PARTS MEN, MECHANICS, MECHANIC'S HELPERS, AND SERVICE SUPERVISORS, THIS GUIDE AIMS TO DEVELOP STUDENT COMPETENCY IN ADJUSTING, REPAIRING, AND MAINTAINING CROP HARVESTING MACHINERY. SUGGESTIONS FOR INTRODUCTION OF THE…
Astrophysical Model Selection in Gravitational Wave Astronomy
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%.
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.
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Using multilevel models to quantify heterogeneity in resource selection
Wagner, T.; Diefenbach, D.R.; Christensen, S.A.; Norton, A.S.
2011-01-01
Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection. ?? The Wildlife Society, 2011.
Python Program to Select HII Region Models
Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.
2016-01-01
HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.
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
Bayesian Model Selection for LISA Pathfinder
Karnesis, Nikolaos; Sopuerta, Carlos F; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; Plagnol, Eric; Vitale, and Stefano
2013-01-01
The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the LISA/eLISA concept. The Data Analysis (DA) team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the DA team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching to this problem is to recover the essential parameters of the LTP which describe the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate...
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.
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...
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...
Development of SPAWM: selection program for available watershed models.
Cho, Yongdeok; Roesner, Larry A
2014-01-01
A selection program for available watershed models (also known as SPAWM) was developed. Thirty-three commonly used watershed models were analyzed in depth and classified in accordance to their attributes. These attributes consist of: (1) land use; (2) event or continuous; (3) time steps; (4) water quality; (5) distributed or lumped; (6) subsurface; (7) overland sediment; and (8) best management practices. Each of these attributes was further classified into sub-attributes. Based on user selected sub-attributes, the most appropriate watershed model is selected from the library of watershed models. SPAWM is implemented using Excel Visual Basic and is designed for use by novices as well as by experts on watershed modeling. It ensures that the necessary sub-attributes required by the user are captured and made available in the selected watershed model.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Mitochondrial Machineries for Protein Import and Assembly.
Wiedemann, Nils; Pfanner, Nikolaus
2017-03-15
Mitochondria are essential organelles with numerous functions in cellular metabolism and homeostasis. Most of the >1,000 different mitochondrial proteins are synthesized as precursors in the cytosol and are imported into mitochondria by five transport pathways. The protein import machineries of the mitochondrial membranes and aqueous compartments reveal a remarkable variability of mechanisms for protein recognition, translocation, and sorting. The protein translocases do not operate as separate entities but are connected to each other and to machineries with functions in energetics, membrane organization, and quality control. Here, we discuss the versatility and dynamic organization of the mitochondrial protein import machineries. Elucidating the molecular mechanisms of mitochondrial protein translocation is crucial for understanding the integration of protein translocases into a large network that controls organelle biogenesis, function, and dynamics. Expected final online publication date for the Annual Review of Biochemistry Volume 86 is June 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Gerretzen, Jan; Szymańska, Ewa; Bart, Jacob; Davies, Antony N; van Manen, Henk-Jan; van den Heuvel, Edwin R; Jansen, Jeroen J; Buydens, Lutgarde M C
2016-09-28
The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of
Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications
Pande, S.
2013-01-01
Quantile hydrologic model selection and structure deficiency assessment is applied in three case studies. The performance of quantile model selection problem is rigorously evaluated using a model structure on the French Broad river basin data set. The case study shows that quantile model selection
STUDY OF THE PARAMETERS OF EFFICIENCY IN CENTRES FOR REPAIR OF AGRICULTURAL MACHINERY
2015-01-01
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 cu...
The genealogy of samples in models with selection.
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.
Adapting AIC to conditional model selection
M. van Ommen (Matthijs)
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 $x_1, \\ldots, x_n$.
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...
A Decision Model for Selecting Participants in Supply Chain
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to satisfy the rapid changing requirements of customers, enterprises must cooperate with each other to form supply chain. The first and the most important stage in the forming of supply chain is the selection of participants. The article proposes a two-staged decision model to select partners. The first stage is the inter company comparison in each business process to select highefficiency candidate based on inside variables. The next stage is to analyse the combination of different candidates in order to select the most perfect partners according to a goal-programming model.
Machinery condition monitoring principles and practices
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
Model selection in systems biology depends on experimental design.
Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H
2014-06-01
Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.
Assembly of the bacterial type III secretion machinery.
Diepold, Andreas; Wagner, Samuel
2014-07-01
Many bacteria that live in contact with eukaryotic hosts, whether as symbionts or as pathogens, have evolved mechanisms that manipulate host cell behaviour to their benefit. One such mechanism, the type III secretion system, is employed by Gram-negative bacterial species to inject effector proteins into host cells. This function is reflected by the overall shape of the machinery, which resembles a molecular syringe. Despite the simplicity of the concept, the type III secretion system is one of the most complex known bacterial nanomachines, incorporating one to more than hundred copies of up to twenty different proteins into a multi-MDa transmembrane complex. The structural core of the system is the so-called needle complex that spans the bacterial cell envelope as a tripartite ring system and culminates in a needle protruding from the bacterial cell surface. Substrate targeting and translocation are accomplished by an export machinery consisting of various inner membrane embedded and cytoplasmic components. The formation of such a multimembrane-spanning machinery is an intricate task that requires precise orchestration. This review gives an overview of recent findings on the assembly of type III secretion machines, discusses quality control and recycling of the system and proposes an integrated assembly model.
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.
Asset pricing model selection: Indonesian Stock Exchange
Pasaribu, Rowland Bismark Fernando
2010-01-01
The Capital Asset Pricing Model (CAPM) has dominated finance theory for over thirty years; it suggests that the market beta alone is sufficient to explain stock returns. However evidence shows that the cross-section of stock returns cannot be described solely by the one-factor CAPM. Therefore, the idea is to add other factors in order to complete the beta in explaining the price movements in the stock exchange. The Arbitrage Pricing Theory (APT) has been proposed as the first multifactor succ...
A mixed model reduction method for preserving selected physical information
Zhang, Jing; Zheng, Gangtie
2017-03-01
A new model reduction method in the frequency domain is presented. By mixedly using the model reduction techniques from both the time domain and the frequency domain, the dynamic model is condensed to selected physical coordinates, and the contribution of slave degrees of freedom is taken as a modification to the model in the form of effective modal mass of virtually constrained modes. The reduced model can preserve the physical information related to the selected physical coordinates such as physical parameters and physical space positions of corresponding structure components. For the cases of non-classical damping, the method is extended to the model reduction in the state space but still only contains the selected physical coordinates. Numerical results are presented to validate the method and show the effectiveness of the model reduction.
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.
Factors Influencing Farmers’ Willingness to Participate in Plant Protection Machinery Subsidies
Institute of Scientific and Technical Information of China (English)
Linping; WANG; Liangmei; CAI
2013-01-01
In order to have an overview of implementation of the subsidy policy for purchase of plant protection machinery in Fujian Province, based on the questionnaire data on Fujian Province, we use Logit model to conduct empirical analysis of factors influencing farmers’ willingness to participate in the subsidy policy for purchase of plant protection machinery. Research results show that there are 69.4% of farmers willing to participate in the subsidy policy for purchase of plant protection machinery; farmers’ growing area has a significant impact on the willingness to participate in the subsidy policy for purchase of plant protection machinery, and there is negative correlation; educational level, experience in planting, family farming pure income all have a significant positive impact on the willingness to participate in the subsidy policy.
Selection of probability based weighting models for Boolean retrieval system
Energy Technology Data Exchange (ETDEWEB)
Ebinuma, Y. (Japan Atomic Energy Research Inst., Tokai, Ibaraki. Tokai Research Establishment)
1981-09-01
Automatic weighting models based on probability theory were studied if they can be applied to boolean search logics including logical sum. The INIS detabase was used for searching of one particular search formula. Among sixteen models three with good ranking performance were selected. These three models were further applied to searching of nine search formulas in the same database. It was found that two models among them show slightly better average ranking performance while the other model, the simplest one, seems also practical.
Model Selection Through Sparse Maximum Likelihood Estimation
Banerjee, Onureena; D'Aspremont, Alexandre
2007-01-01
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for...
Sensitivity of resource selection and connectivity models to landscape definition
Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce
2017-01-01
Context: The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention. Objectives: We evaluated the sensitivity of resource selection and connectivity models to four landscape...
A Working Model of Natural Selection Illustrated by Table Tennis
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…
Elementary Teachers' Selection and Use of Visual Models
Lee, Tammy D.; Gail Jones, M.
2017-07-01
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.
Fluctuating selection models and McDonald-Kreitman type analyses.
Directory of Open Access Journals (Sweden)
Toni I Gossmann
Full Text Available It is likely that the strength of selection acting upon a mutation varies through time due to changes in the environment. However, most population genetic theory assumes that the strength of selection remains constant. Here we investigate the consequences of fluctuating selection pressures on the quantification of adaptive evolution using McDonald-Kreitman (MK style approaches. In agreement with previous work, we show that fluctuating selection can generate evidence of adaptive evolution even when the expected strength of selection on a mutation is zero. However, we also find that the mutations, which contribute to both polymorphism and divergence tend, on average, to be positively selected during their lifetime, under fluctuating selection models. This is because mutations that fluctuate, by chance, to positive selected values, tend to reach higher frequencies in the population than those that fluctuate towards negative values. Hence the evidence of positive adaptive evolution detected under a fluctuating selection model by MK type approaches is genuine since fixed mutations tend to be advantageous on average during their lifetime. Never-the-less we show that methods tend to underestimate the rate of adaptive evolution when selection fluctuates.
Italian Textile Machinery Industry Focuses on Sustainability
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
As for fashion industry,Italian brands always play as one of the leaders influencing the global trends.This time,in Shanghai,the Italian textile machinery manufacturers highlight the latest proposals on sustainability and eco-friendly technology.
Italian Textite Machinery Industry Focuses on Sustainability
Institute of Scientific and Technical Information of China (English)
Wang Ting
2010-01-01
@@ At the ITMA Asia+CITME 2010,which was held on June 22at the Shanghai New International Expo Centre in Shanghai,Italian textile machinery manufacturers represented one of the largest foreign delegations: 115exhibitors occupying a total surface area of about 4,000 sq.meters.
Active Vibration Dampers For Rotating Machinery
Kascack, Albert F.; Ropchock, John J.; Lakatos, Tomas F.; Montague, Gerald T.; Palazzolo, Alan; Lin, Reng Rong
1994-01-01
Active dampers developed to suppress vibrations in rotating machinery. Essentially feedback control systems and reciprocating piezoelectric actuators. Similar active damper containing different actuators described in LEW-14488. Concept also applicable to suppression of vibrations in stationary structures subject to winds and earthquakes. Active damper offers adjustable suppression of vibrations. Small and lightweight and responds faster to transients.
CCPIT Machinery Exhibition Succeeded in Kuala Lumpur
Institute of Scientific and Technical Information of China (English)
无
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.……
CCPIT Machinery Exhibition Succeeded in Kuala Lumpur
Institute of Scientific and Technical Information of China (English)
无
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.
Brownian machinery in physics and biology
Hänggi, Peter
2001-01-01
Brownian machinery in physics and biology. - In: Noise in physical systems and 1/f fluctuations : proceedings of the 16th internat. conference, Gainesville, Fl., 22-25 Oct. 2001 / Ed.: Gijs Bosman. - New Jersey u.a. : World Scientific, 2001. - S. 397-399
Computational design of hydrogen fluid machinery
Energy Technology Data Exchange (ETDEWEB)
Kaupert, K.A. [Ebara Cryodynamics Div., Sparks, NV (United States)
2001-06-01
This paper presented computational design methods for liquid hydrogen fluid machinery for which the aerospace industry holds a particular interest. The main design points are reliability, efficiency and predictability. The methods described here were based on experience gained in the liquefied natural gas sector. The main design validation tools presented in this paper were computational fluid dynamics, computational rotordynamics, and computational stress analysis. Recent advances have made it possible to incorporate the influence of unsteady phenomena. These new design techniques make it possible to increase the reliability, efficiency and predictability of fluid machinery. A design example for an Ebara Cryodynamics multi-stage impeller/diffuser-return vane combination operating in liquefied natural gas was presented. The fluid machinery characteristics were compared for liquid hydrogen and liquefied natural gas to demonstrate the technical feasibility of industrial liquid hydrogen fluid machinery. Currently, no significant demand exists for liquid hydrogen turbomachinery except for the rocket engine industry. But the emergence of the hydrogen economy will promote the growth in liquefied natural gas and or liquid hydrogen cryogenic turbomachinery. 11 refs., 3 tabs., 6 figs.
Elements of active vibration control for rotating machinery
Ulbrich, Heinz
1990-01-01
The success or failure of active vibration control is determined by the availability of suitable actuators, modeling of the entire system including all active elements, positioning of the actuators and sensors, and implementation of problem-adapted control concepts. All of these topics are outlined and their special problems are discussed in detail. Special attention is given to efficient modeling of systems, especially for considering the active elements. Finally, design methods for and the application of active vibration control on rotating machinery are demonstrated by several real applications.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
Robust Decision-making Applied to Model Selection
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M. [Los Alamos National Laboratory
2012-08-06
The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.
Information-theoretic model selection applied to supernovae data
Biesiada, M
2007-01-01
There are several different theoretical ideas invoked to explain the dark energy with relatively little guidance of which one of them might be right. Therefore the emphasis of ongoing and forthcoming research in this field shifts from estimating specific parameters of cosmological model to the model selection. In this paper we apply information-theoretic model selection approach based on Akaike criterion as an estimator of Kullback-Leibler entropy. In particular, we present the proper way of ranking the competing models based on Akaike weights (in Bayesian language - posterior probabilities of the models). Out of many particular models of dark energy we focus on four: quintessence, quintessence with time varying equation of state, brane-world and generalized Chaplygin gas model and test them on Riess' Gold sample. As a result we obtain that the best model - in terms of Akaike Criterion - is the quintessence model. The odds suggest that although there exist differences in the support given to specific scenario...
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
SELECTION MOMENTS AND GENERALIZED METHOD OF MOMENTS FOR HETEROSKEDASTIC MODELS
Directory of Open Access Journals (Sweden)
Constantin ANGHELACHE
2016-06-01
Full Text Available In this paper, the authors describe the selection methods for moments and the application of the generalized moments method for the heteroskedastic models. The utility of GMM estimators is found in the study of the financial market models. The selection criteria for moments are applied for the efficient estimation of GMM for univariate time series with martingale difference errors, similar to those studied so far by Kuersteiner.
Modeling Suspicious Email Detection using Enhanced Feature Selection
2013-01-01
The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Na\\"ive Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algo...
RUC at TREC 2014: Select Resources Using Topic Models
2014-11-01
them being observed (i.e. sampled). To infer the topic Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the...Selection. In CIKM 2009, pages 1277-1286. [10] M. Baillie, M. Carmen, and F. Crestani. A Multiple- Collection Latent Topic Model for Federated...RUC at TREC 2014: Select Resources Using Topic Models Qiuyue Wang, Shaochen Shi, Wei Cao School of Information Renmin University of China Beijing
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.
A guide to Bayesian model selection for ecologists
Hooten, Mevin B.; Hobbs, N.T.
2015-01-01
The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
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.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Directory of Open Access Journals (Sweden)
Feipeng Guo
2013-10-01
Full Text Available 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 method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
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.
Stochastic noise in splicing machinery
Melamud, Eugene; Moult, John
2009-01-01
The number of known alternative human isoforms has been increasing steadily with the amount of available transcription data. To date, over 100 000 isoforms have been detected in EST libraries, and at least 75% of human genes have at least one alternative isoform. In this paper, we propose that most alternative splicing events are the result of noise in the splicing process. We show that the number of isoforms and their abundance can be predicted by a simple stochastic noise model that takes i...
Bayesian model evidence for order selection and correlation testing.
Johnston, Leigh A; Mareels, Iven M Y; Egan, Gary F
2011-01-01
Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data. Secondly, an evidence-based method for testing change in linear correlation between datasets is proposed, which is demonstrated to outperform both the traditional statistical test of the null hypothesis of no correlation change and the likelihood ratio test.
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.
Selection Bias in Educational Transition Models: Theory and Empirical Evidence
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads
Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...... 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...
Multicriteria framework for selecting a process modelling language
Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel
2016-01-01
The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.
Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romanach, 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 (models had high performance metrics (>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
Operational safety practices as determinants of machinery-related injury on Saskatchewan farms.
Narasimhan, Gopinath R; Peng, Yingwei; Crowe, Trever G; Hagel, Louise; Dosman, James; Pickett, William
2010-07-01
Agricultural machinery is a major source of injury on farms. The importance of machinery safety practices as potential determinants of injury remains incompletely understood. We examined two such safety practices as risk factors for injury: (1) the presence of safety devices on machinery and (2) low levels of routine machinery maintenance. Our data source was the Saskatchewan Farm Injury Cohort baseline survey (n=2390 farms). Factor analysis was used to create measures of the two operational safety practices. The farm was the unit for all analyses and associations were evaluated using multiple Poisson regression models. Limited presence of safety devices on machinery during farm operations was associated with higher risks for injury (RR 1.94; 95% CI 1.13-3.33; p(trend)=0.02). Lower routine maintenance scores were associated with significantly reduced risks for injury (RR 0.54; 95% CI 0.29-0.98; p(trend)=0.05). The first finding implies that injury prevention programs require continued focus on the use of safety devices on machinery. The second finding could indicate that maintenance itself is a risk factor or that more modern equipment that requires less maintenance places the operator at lower risk. These findings provide etiological data that confirms the practical importance of operational safety practices as components of injury control strategies on farms. Copyright 2010 Elsevier Ltd. All rights reserved.
Models of microbiome evolution incorporating host and microbial selection.
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
Testing exclusion restrictions and additive separability in sample selection models
DEFF Research Database (Denmark)
Huber, Martin; Mellace, Giovanni
2014-01-01
Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these......Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction...... of these assumptions by applying the approach of Huber and Mellace (Testing instrument validity for LATE identification based on inequality moment constraints, 2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive...... separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non...
Periodic Integration: Further Results on Model Selection and Forecasting
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1996-01-01
textabstractThis paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other
Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies
Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies structur
AN EXPERT SYSTEM MODEL FOR THE SELECTION OF TECHNICAL PERSONNEL
Directory of Open Access Journals (Sweden)
Emine COŞGUN
2005-03-01
Full Text Available In this study, a model has been developed for the selection of the technical personnel. In the model Visual Basic has been used as user interface, Microsoft Access has been utilized as database system and CLIPS program has been used as expert system program. The proposed model has been developed by utilizing expert system technology. In the personnel selection process, only the pre-evaluation of the applicants has been taken into consideration. Instead of replacing the expert himself, a decision support program has been developed to analyze the data gathered from the job application forms. The attached study will assist the expert to make faster and more accurate decisions.
Novel web service selection model based on discrete group search.
Zhai, Jie; Shao, Zhiqing; Guo, Yi; Zhang, Haiteng
2014-01-01
In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases.
Operation Analysis for Electrical Machinery Based on Reluctance Network
Nakamura, Kenji; Ichinokura, Osamu
In this paper, we describe the basis of a magnetic circuit method and introduce a reluctance network analysis (RNA) proposed by authors. The RNA, which is based on the magnetic circuit method, has some suitable merits for simulating electrical machineries such as a simple analytical model, ease of coupled analysis with electrical circuits, motion, and thermal fields. In addition to these merits, a general-purpose circuit simulator like “SPICE" can be utilized as a solver. We present some applications of the RNA to operation analysis for an orthogonal-core, a switched reluctance motor, and a permanent magnet generator.
Present Situation of Petroleum Machinery Manufacturers in China
Institute of Scientific and Technical Information of China (English)
Li Xilu
2008-01-01
Since China joined the WTO, the environment for the development of petroleum machinery industry in the country has changed a lot. As international petroleum machinery manufacturers enter into Chinese market, petroleum machinery manufacturers of the country are facing fierce competition both at home and abroad.
30 CFR 56.14205 - Machinery, equipment, and tools.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machinery, equipment, and tools. 56.14205... NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-SURFACE METAL AND NONMETAL MINES Machinery and Equipment Safety Practices and Operational Procedures § 56.14205 Machinery, equipment, and tools....
46 CFR 58.01-20 - Machinery guards.
2010-10-01
... 46 Shipping 2 2010-10-01 2010-10-01 false Machinery guards. 58.01-20 Section 58.01-20 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) MARINE ENGINEERING MAIN AND AUXILIARY MACHINERY AND RELATED SYSTEMS General Requirements § 58.01-20 Machinery guards. Gears, couplings, flywheels...
46 CFR 109.205 - Inspection of boilers and machinery.
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...
29 CFR 1915.165 - Ship's deck machinery.
2010-07-01
... 29 Labor 7 2010-07-01 2010-07-01 false Ship's deck machinery. 1915.165 Section 1915.165 Labor... (CONTINUED) OCCUPATIONAL SAFETY AND HEALTH STANDARDS FOR SHIPYARD EMPLOYMENT Ship's Machinery and Piping Systems § 1915.165 Ship's deck machinery. (a) Before work is performed on the anchor windlass or any...
46 CFR 122.208 - Accidents to machinery.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Accidents to machinery. 122.208 Section 122.208 Shipping... Voyage Records § 122.208 Accidents to machinery. The owner, managing operator, or master shall report damage to a boiler, unfired pressure vessel, or machinery that renders further use of the item...
29 CFR 1915.164 - Ship's propulsion machinery.
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... Machinery and Piping Systems § 1915.164 Ship's propulsion machinery. (a) Before work is performed on...
46 CFR 109.419 - Report of unsafe machinery.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Report of unsafe machinery. 109.419 Section 109.419... OPERATIONS Reports, Notifications, and Records Reports and Notifications § 109.419 Report of unsafe machinery. If a boiler, unfired pressure vessel, or other machinery on a unit is unsafe to operate, the...
30 CFR 57.14205 - Machinery, equipment, and tools.
2010-07-01
... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Machinery, equipment, and tools. 57.14205... NONMETAL MINE SAFETY AND HEALTH SAFETY AND HEALTH STANDARDS-UNDERGROUND METAL AND NONMETAL MINES Machinery and Equipment Safety Practices and Operational Procedures § 57.14205 Machinery, equipment, and...
46 CFR 252.33 - Hull and machinery insurance.
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...
29 CFR 1910.214 - Cooperage machinery. [Reserved
2010-07-01
... 29 Labor 5 2010-07-01 2010-07-01 false Cooperage machinery. 1910.214 Section 1910.214 Labor Regulations Relating to Labor (Continued) OCCUPATIONAL SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR OCCUPATIONAL SAFETY AND HEALTH STANDARDS Machinery and Machine Guarding § 1910.214 Cooperage machinery....
46 CFR 196.30-5 - Accidents to machinery.
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...
46 CFR 97.30-5 - Accidents to machinery.
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...
46 CFR 185.208 - Accidents to machinery.
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Accidents to machinery. 185.208 Section 185.208 Shipping...) OPERATIONS Marine Casualties and Voyage Records § 185.208 Accidents to machinery. The owner, managing operator, or master shall report damage to a boiler, unfired pressure vessel, or machinery that...
46 CFR 78.33-5 - Accidents to machinery.
2010-10-01
... 46 Shipping 3 2010-10-01 2010-10-01 false Accidents to machinery. 78.33-5 Section 78.33-5 Shipping... Accidents, Repairs, and Unsafe Equipment § 78.33-5 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 the...
46 CFR 185.352 - Ventilation of gasoline machinery spaces.
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Ventilation of gasoline machinery spaces. 185.352 Section 185.352 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) SMALL PASSENGER VESSELS... machinery spaces. The mechanical exhaust for the ventilation of a gasoline machinery space, required...
46 CFR 130.460 - Placement of machinery alarms.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Placement of machinery alarms. 130.460 Section 130.460..., AND MISCELLANEOUS EQUIPMENT AND SYSTEMS Automation of Unattended Machinery Spaces § 130.460 Placement of machinery alarms. (a) Visible and audible alarms must be installed at the pilothouse to...
46 CFR 171.095 - Machinery space bulkhead.
2010-10-01
... 46 Shipping 7 2010-10-01 2010-10-01 false Machinery space bulkhead. 171.095 Section 171.095... PERTAINING TO VESSELS CARRYING PASSENGERS Additional Subdivision Requirements § 171.095 Machinery space... transverse watertight bulkheads to separate the machinery space from the remainder of the vessel....
46 CFR 111.103-3 - Machinery space ventilation.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Machinery space ventilation. 111.103-3 Section 111.103-3...-GENERAL REQUIREMENTS Remote Stopping Systems § 111.103-3 Machinery space ventilation. (a) Each machinery space ventilation system must have two controls to stop the ventilation, one of which may be the...
33 CFR 157.39 - Machinery space bilges.
2010-07-01
... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false Machinery space bilges. 157.39... Vessel Operation § 157.39 Machinery space bilges. (a) A tank vessel may discharge an oily mixture from a machinery space bilge that is combined with an oil cargo residue if the vessel discharges in compliance...
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...
OPTIMAL MAINTENANCE AND REPLACEMENT OF EXTRACTION MACHINERY
Institute of Scientific and Technical Information of China (English)
Suresh P.SETHI; Hong-Mo YEH; Rong ZHANG; Andrew K.S.JARDINE
2008-01-01
This paper considers a problem of optimal preventive maintenance and replacement schedule ofequipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.
The Autophagic Machinery in Enterovirus Infection
Directory of Open Access Journals (Sweden)
Jeffrey K. F. Lai
2016-01-01
Full Text Available 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.
Fuzzy MCDM Model for Risk Factor Selection in Construction Projects
Directory of Open Access Journals (Sweden)
Pejman Rezakhani
2012-11-01
Full Text Available Risk factor selection is an important step in a successful risk management plan. There are many risk factors in a construction project and by an effective and systematic risk selection process the most critical risks can be distinguished to have more attention. In this paper through a comprehensive literature survey, most significant risk factors in a construction project are classified in a hierarchical structure. For an effective risk factor selection, a modified rational multi criteria decision making model (MCDM is developed. This model is a consensus rule based model and has the optimization property of rational models. By applying fuzzy logic to this model, uncertainty factors in group decision making such as experts` influence weights, their preference and judgment for risk selection criteria will be assessed. Also an intelligent checking process to check the logical consistency of experts` preferences will be implemented during the decision making process. The solution inferred from this method is in the highest degree of acceptance of group members. Also consistency of individual preferences is checked by some inference rules. This is an efficient and effective approach to prioritize and select risks based on decisions made by group of experts in construction projects. The applicability of presented method is assessed through a case study.
A Hybrid Program Projects Selection Model for Nonprofit TV Stations
Directory of Open Access Journals (Sweden)
Kuei-Lun Chang
2015-01-01
Full Text Available This study develops a hybrid multiple criteria decision making (MCDM model to select program projects for nonprofit TV stations on the basis of managers’ perceptions. By the concept of balanced scorecard (BSC and corporate social responsibility (CSR, we collect criteria for selecting the best program project. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Next, considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. A case study is presented to demonstrate the applicability of the proposed model.
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.
Machinery prognostics and prognosis oriented maintenance management
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.
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.
Bayesian model selection for constrained multivariate normal linear models
Mulder, J.
2010-01-01
The expectations that researchers have about the structure in the data can often be formulated in terms of equality constraints and/or inequality constraints on the parameters in the model that is used. In a (M)AN(C)OVA model, researchers have expectations about the differences between the
The exportomer: the peroxisomal receptor export machinery.
Platta, Harald W; Hagen, Stefanie; Erdmann, Ralf
2013-04-01
Peroxisomes constitute a dynamic compartment of almost all eukaryotic cells. Depending on environmental changes and cellular demands peroxisomes can acquire diverse metabolic roles. The compartmentalization of peroxisomal matrix enzymes is a prerequisite to carry out their physiologic function. The matrix proteins are synthesized on free ribosomes in the cytosol and are ferried to the peroxisomal membrane by specific soluble receptors. Subsequent to cargo release into the peroxisomal matrix, the receptors are exported back to the cytosol to facilitate further rounds of matrix protein import. This dislocation step is accomplished by a remarkable machinery, which comprises enzymes required for the ubiquitination as well as the ATP-dependent extraction of the receptor from the membrane. Interestingly, receptor ubiquitination and dislocation are the only known energy-dependent steps in the peroxisomal matrix protein import process. The current view is that the export machinery of the receptors might function as molecular motor not only in the dislocation of the receptors but also in the import step of peroxisomal matrix protein by coupling ATP-dependent removal of the peroxisomal import receptor with cargo translocation into the organelle. In this review we will focus on the architecture and function of the peroxisomal receptor export machinery, the peroxisomal exportomer.
Genetic signatures of natural selection in a model invasive ascidian
Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin
2017-01-01
Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta. PMID:28266616
IT vendor selection model by using structural equation model & analytical hierarchy process
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.
Robust model selection and the statistical classification of languages
García, J. E.; González-López, V. A.; Viola, M. L. L.
2012-10-01
In this paper we address the problem of model selection for the set of finite memory stochastic processes with finite alphabet, when the data is contaminated. We consider m independent samples, with more than half of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We devise a model selection procedure such that for a sample size large enough, the selected process is the one with law Q. Our model selection strategy is based on estimating relative entropies to select a subset of samples that are realizations of the same law. Although the procedure is valid for any family of finite order Markov models, we will focus on the family of variable length Markov chain models, which include the fixed order Markov chain model family. We define the asymptotic breakdown point (ABDP) for a model selection procedure, and we show the ABDP for our procedure. This means that if the proportion of contaminated samples is smaller than the ABDP, then, as the sample size grows our procedure selects a model for the process with law Q. We also use our procedure in a setting where we have one sample conformed by the concatenation of sub-samples of two or more stochastic processes, with most of the subsamples having law Q. We conducted a simulation study. In the application section we address the question of the statistical classification of languages according to their rhythmic features using speech samples. This is an important open problem in phonology. A persistent difficulty on this problem is that the speech samples correspond to several sentences produced by diverse speakers, corresponding to a mixture of distributions. The usual procedure to deal with this problem has been to choose a subset of the original sample which seems to best represent each language. The selection is made by listening to the samples. In our application we use the full dataset without any preselection of samples. We apply our robust methodology estimating
Selecting Optimal Subset of Features for Student Performance Model
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Hany M. Harb
2012-09-01
Full Text Available Educational data mining (EDM is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.
Short-Run Asset Selection using a Logistic Model
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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.
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
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...... 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...... 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...
Viral interactions with components of the splicing machinery.
Meyer, F
2016-01-01
Eukaryotic genes are often interrupted by stretches of sequence with no protein coding potential or obvious function. After transcription, these interrupting sequences must be removed to give rise to the mature messenger RNA. This fundamental process is called RNA splicing and is achieved by complicated machinery made of protein and RNA that assembles around the RNA to be edited. Viruses also use RNA splicing to maximize their coding potential and economize on genetic space, and use clever strategies to manipulate the splicing machinery to their advantage. This article gives an overview of the splicing process and provides examples of viral strategies that make use of various components of the splicing system to promote their replicative cycle. Representative virus families have been selected to illustrate the interaction with various regulatory proteins and ribonucleoproteins. The unifying theme is fine regulation through protein-protein and protein-RNA interactions with the spliceosome components and associated factors to promote or prevent spliceosome assembly on given splice sites, in addition to a strong influence from cis-regulatory sequences on viral transcripts. Because there is an intimate coupling of splicing with the processes that direct mRNA biogenesis, a description of how these viruses couple the regulation of splicing with the retention or stability of mRNAs is also included. It seems that a unique balance of suppression and activation of splicing and nuclear export works optimally for each family of viruses.
Financial applications of a Tabu search variable selection model
Directory of Open Access Journals (Sweden)
Zvi Drezner
2001-01-01
Full Text Available We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1 illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2 procedure, and (2 show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2 by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968 Z-Score model.
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....
Österlind, Tomas; Kari, Leif; Nicolescu, Cornel Mihai
2017-02-01
Rotor vibration and stationary displacement patterns observed in rotating machineries subject to local harmonic excitation are analysed for improved understanding and dynamic characterization. The analysis stresses the importance of coordinate transformation between rotating and stationary frame of reference for accurate results and estimation of dynamic properties. A generic method which can be used for various rotor applications such as machine tool spindle and turbo machinery vibration is presented. The phenomenon shares similarities with stationary waves in rotating disks though focuses on vibration in shafts. The paper further proposes a graphical tool, the displacement map, which can be used for selection of stable rotational speed for rotating machinery. The results are validated through simulation of dynamic response of a milling cutter, which is a typical example of a variable speed rotor operating under different load conditions.
Caffaro, Federica; Mirisola, Alberto; Cavallo, Eugenio
2017-01-01
This study investigated the extent to which a sample of Italian users comprehended safety pictorials used on agricultural machinery. A questionnaire with 12 safety pictorials was administered to 248 users of agricultural machinery. For each of the pictorials, the participants were asked to select the most appropriate description of four written choices. The investigated safety pictorials were, in general, not well comprehended. Two different classes of participants were identified, each with a different level of comprehension. The participants with better comprehension were characterized by the regular use of agricultural machinery and frequent previous exposure to pictorials. The need for training courses focusing on safety pictorials and their meanings, as well as the need for improvement to the pictorials themselves to make them more easily comprehended, is discussed.
The plastid-dividing machinery: formation, constriction and fission.
Yoshida, Yamato; Miyagishima, Shin-ya; Kuroiwa, Haruko; Kuroiwa, Tsuneyoshi
2012-12-01
Plastids divide by constriction of the plastid-dividing (PD) machinery, which encircles the division site. The PD machinery consists of the stromal inner machinery which includes the inner PD and filamenting temperature-sensitive mutant Z (FtsZ) rings and the cytosolic outer machinery which includes the outer PD and dynamin rings. The major constituent of the PD machinery is the outer PD ring, which consists of a bundle of polyglucan filaments. In addition, recent proteomic studies suggest that the PD machinery contains additional proteins that have not been characterized. The PD machinery forms from the inside to the outside of the plastid. The constriction seems to occur by sliding of the polyglucan filaments of the outer PD ring, aided by dynamin. The final fission of the plastid is probably promoted by the 'pinchase' activity of dynamin.
SUBOPTIMAL NONLINEAR CONTROL OF PACKAGING MACHINERY DRIVE
Kudin, V. F.; Toropov, A.V.
2013-01-01
This paper deals with the procedure of synthesis of a nonlinear position controller for the «feeder» of packaging mechanism. The mathematical model of «feeder» drive with regard to the restriction on the control output of external PLC. Linearization of nonlinear characteristic by the «secants» method is implemented and selected functional quality that defines the minimal time of transients is selected. Quality functional in the form of a quadratic functional with a variable weighting factor i...
A Decision-Analytic Feasibility Study of Upgrading Machinery at a Tools Workshop
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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.
TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING?
Institute of Scientific and Technical Information of China (English)
YU Lean; WANG Shouyang; K. K. Lai; Y.Nakamori
2005-01-01
Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.
Bayesian selection of nucleotide substitution models and their site assignments.
Wu, Chieh-Hsi; Suchard, Marc A; Drummond, Alexei J
2013-03-01
Probabilistic inference of a phylogenetic tree from molecular sequence data is predicated on a substitution model describing the relative rates of change between character states along the tree for each site in the multiple sequence alignment. Commonly, one assumes that the substitution model is homogeneous across sites within large partitions of the alignment, assigns these partitions a priori, and then fixes their underlying substitution model to the best-fitting model from a hierarchy of named models. Here, we introduce an automatic model selection and model averaging approach within a Bayesian framework that simultaneously estimates the number of partitions, the assignment of sites to partitions, the substitution model for each partition, and the uncertainty in these selections. This new approach is implemented as an add-on to the BEAST 2 software platform. We find that this approach dramatically improves the fit of the nucleotide substitution model compared with existing approaches, and we show, using a number of example data sets, that as many as nine partitions are required to explain the heterogeneity in nucleotide substitution process across sites in a single gene analysis. In some instances, this improved modeling of the substitution process can have a measurable effect on downstream inference, including the estimated phylogeny, relative divergence times, and effective population size histories.
An Integrated Model For Online shopping, Using Selective Models
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Fereshteh Rabiei Dastjerdi
Full Text Available As in traditional shopping, customer acquisition and retention are critical issues in the success of an online store. Many factors impact how, and if, customers accept online shopping. Models presented in recent years, only focus on behavioral or technolo ...
Selecting global climate models for regional climate change studies
Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.
2009-01-01
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simula...
Spatial Fleming-Viot models with selection and mutation
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.
Model selection and inference a practical information-theoretic approach
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 ...
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.
A topic evolution model with sentiment and selective attention
Si, Xia-Meng; Wang, Wen-Dong; Zhai, Chun-Qing; Ma, Yan
2017-04-01
Topic evolution is a hybrid dynamics of information propagation and opinion interaction. The dynamics of opinion interaction is inherently interwoven with the dynamics of information propagation in the network, owing to the bidirectional influences between interaction and diffusion. The degree of sentiment determines if the topic can continue to spread from this node, and the selective attention determines the information flow direction and communicatee selection. For this end, we put forward a sentiment-based mixed dynamics model with selective attention, and applied the Bayesian updating rules on it. Our model can indirectly describe the isolated users who seem isolated from a topic due to some reasons even everybody around them has heard about it. Numerical simulations show that, more insiders initially and fewer simultaneous spreaders can lessen the extremism. To promote the topic diffusion or restrain the prevailing of extremism, fewer agents with constructive motivation and more agents with no involving motivation are encouraged.
Evidence accumulation as a model for lexical selection.
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.
Second-order model selection in mixture experiments
Energy Technology Data Exchange (ETDEWEB)
Redgate, P.E.; Piepel, G.F.; Hrma, P.R.
1992-07-01
Full second-order models for q-component mixture experiments contain q(q+l)/2 terms, which increases rapidly as q increases. Fitting full second-order models for larger q may involve problems with ill-conditioning and overfitting. These problems can be remedied by transforming the mixture components and/or fitting reduced forms of the full second-order mixture model. Various component transformation and model reduction approaches are discussed. Data from a 10-component nuclear waste glass study are used to illustrate ill-conditioning and overfitting problems that can be encountered when fitting a full second-order mixture model. Component transformation, model term selection, and model evaluation/validation techniques are discussed and illustrated for the waste glass example.
Measuring balance and model selection in propensity score methods
Belitser, S.; Martens, Edwin P.; Pestman, Wiebe R.; Groenwold, Rolf H.H.; De Boer, Anthonius; Klungel, Olaf H.
2011-01-01
Background: Propensity score (PS) methods focus on balancing confounders between groups to estimate an unbiased treatment or exposure effect. However, there is lack of attention in actually measuring, reporting and using the information on balance, for instance for model selection. Objectives: To de
Selecting crop models for decision making in wheat insurance
Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.
2015-01-01
In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a c
Cross-validation criteria for SETAR model selection
de Gooijer, J.G.
2001-01-01
Three cross-validation criteria, denoted C, C_c, and C_u are proposed for selecting the orders of a self-exciting threshold autoregressive SETAR) model when both the delay and the threshold value are unknown. The derivatioon of C is within a natural cross-validation framework. The crietion C_c is si
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2011-01-01
’s optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all...
Selecting crop models for decision making in wheat insurance
Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.
2015-01-01
In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a
Accurate model selection of relaxed molecular clocks in bayesian phylogenetics.
Baele, Guy; Li, Wai Lok Sibon; Drummond, Alexei J; Suchard, Marc A; Lemey, Philippe
2013-02-01
Recent implementations of path sampling (PS) and stepping-stone sampling (SS) have been shown to outperform the harmonic mean estimator (HME) and a posterior simulation-based analog of Akaike's information criterion through Markov chain Monte Carlo (AICM), in bayesian model selection of demographic and molecular clock models. Almost simultaneously, a bayesian model averaging approach was developed that avoids conditioning on a single model but averages over a set of relaxed clock models. This approach returns estimates of the posterior probability of each clock model through which one can estimate the Bayes factor in favor of the maximum a posteriori (MAP) clock model; however, this Bayes factor estimate may suffer when the posterior probability of the MAP model approaches 1. Here, we compare these two recent developments with the HME, stabilized/smoothed HME (sHME), and AICM, using both synthetic and empirical data. Our comparison shows reassuringly that MAP identification and its Bayes factor provide similar performance to PS and SS and that these approaches considerably outperform HME, sHME, and AICM in selecting the correct underlying clock model. We also illustrate the importance of using proper priors on a large set of empirical data sets.
Rank-based model selection for multiple ions quantum tomography
Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian
2012-10-01
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 χ2 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.
46 CFR 30.10-42 - Machinery space-TB/ALL.
2010-10-01
... 46 Shipping 1 2010-10-01 2010-10-01 false Machinery space-TB/ALL. 30.10-42 Section 30.10-42...-42 Machinery space—TB/ALL. The term machinery space means any space that contains machinery and related equipment including Category A machinery spaces, propelling machinery, boilers, oil fuel...
Selective refinement and selection of near-native models in protein structure prediction.
Zhang, Jiong; Barz, Bogdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan
2015-10-01
In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.
A model selection approach to analysis of variance and covariance.
Alber, Susan A; Weiss, Robert E
2009-06-15
An alternative to analysis of variance is a model selection approach where every partition of the treatment means into clusters with equal value is treated as a separate model. The null hypothesis that all treatments are equal corresponds to the partition with all means in a single cluster. The alternative hypothesis correspond to the set of all other partitions of treatment means. A model selection approach can also be used for a treatment by covariate interaction, where the null hypothesis and each alternative correspond to a partition of treatments into clusters with equal covariate effects. We extend the partition-as-model approach to simultaneous inference for both treatment main effect and treatment interaction with a continuous covariate with separate partitions for the intercepts and treatment-specific slopes. The model space is the Cartesian product of the intercept partition and the slope partition, and we develop five joint priors for this model space. In four of these priors the intercept and slope partition are dependent. We advise on setting priors over models, and we use the model to analyze an orthodontic data set that compares the frictional resistance created by orthodontic fixtures. Copyright (c) 2009 John Wiley & Sons, Ltd.
Model selection for the extraction of movement primitives.
Endres, Dominik M; Chiovetto, Enrico; Giese, Martin A
2013-01-01
A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA), independent component analysis (ICA), anechoic demixing, and the time-varying synergy model (d'Avella and Tresch, 2002). However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model. We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria [Bayesian information criterion, BIC (Schwarz, 1978) and the Akaike Information Criterion (AIC) (Akaike, 1974)]. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
Model selection for the extraction of movement primitives
Directory of Open Access Journals (Sweden)
Dominik M Endres
2013-12-01
Full Text Available A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA,independent component analysis (ICA, anechoic demixing, and the time-varying synergy model. However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model.We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
Analysis of electric machinery and drive systems
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
The RNA polymerase I transcription machinery
Russell, Jackie; Zomerdijk, Joost C. B. M.
2006-01-01
The rRNAs constitute the catalytic and structural components of the ribosome, the protein synthesis machinery of cells. The level of rRNA synthesis, mediated by Pol I (RNA polymerase I), therefore has a major impact on the life and destiny of a cell. In order to elucidate how cells achieve the stringent control of Pol I transcription, matching the supply of rRNA to demand under different cellular growth conditions, it is essential to understand the components and mechanics of the Pol I transc...
How many separable sources? Model selection in independent components analysis.
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.
Statistical modelling in biostatistics and bioinformatics selected papers
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...
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
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 Analysi...... 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.......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...
PROPOSAL OF AN EMPIRICAL MODEL FOR SUPPLIERS SELECTION
Directory of Open Access Journals (Sweden)
Paulo Ávila
2015-03-01
Full Text Available The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP method or Simple Multi-Attribute Rating Technique (SMART. The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.
Supplier Selection in Virtual Enterprise Model of Manufacturing Supply Network
Kaihara, Toshiya; Opadiji, Jayeola F.
The market-based approach to manufacturing supply network planning focuses on the competitive attitudes of various enterprises in the network to generate plans that seek to maximize the throughput of the network. It is this competitive behaviour of the member units that we explore in proposing a solution model for a supplier selection problem in convergent manufacturing supply networks. We present a formulation of autonomous units of the network as trading agents in a virtual enterprise network interacting to deliver value to market consumers and discuss the effect of internal and external trading parameters on the selection of suppliers by enterprise units.
Efficiency of model selection criteria in flood frequency analysis
Calenda, G.; Volpi, E.
2009-04-01
The estimation of high flood quantiles requires the extrapolation of the probability distributions far beyond the usual sample length, involving high estimation uncertainties. The choice of the probability law, traditionally based on the hypothesis testing, is critical to this point. In this study the efficiency of different model selection criteria, seldom applied in flood frequency analysis, is investigated. The efficiency of each criterion in identifying the probability distribution of the hydrological extremes is evaluated by numerical simulations for different parent distributions, coefficients of variation and skewness, and sample sizes. The compared model selection procedures are the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Anderson Darling Criterion (ADC) recently discussed by Di Baldassarre et al. (2008) and Sample Quantile Criterion (SQC), recently proposed by the authors (Calenda et al., 2009). The SQC is based on the principle of maximising the probability density of the elements of the sample that are considered relevant to the problem, and takes into account both the accuracy and the uncertainty of the estimate. Since the stress is mainly on extreme events, the SQC involves upper-tail probabilities, where the effect of the model assumption is more critical. The proposed index is equal to the sum of logarithms of the inverse of the sample probability density of the observed quantiles. The definition of this index is based on the principle that the more centred is the sample value in respect to its density distribution (accuracy of the estimate) and the less spread is this distribution (uncertainty of the estimate), the greater is the probability density of the sample quantile. Thus, lower values of the index indicate a better performance of the distribution law. This criterion can operate the selection of the optimum distribution among competing probability models that are estimated using different samples. The
An integrated method for matching forest machinery and a weight-value adjustment
Institute of Scientific and Technical Information of China (English)
Dan Li
2014-01-01
Proper matching of forestry machinery is important when raising mechanization levels for forestry production. In the matching process, forestry machinery needs not only expertise, but also improved methods for solving problems. I propose combination of case-based reasoning (CBR) and rule-based reasoning (RBR) by calculating the similarity of quantitative parameters of various forestry machines in an analytical and hierarchical process. I calculated the similarity of machin-ery used in forest industries to enable better selection and matching of equipment. I propose a weight-value adjusting method based on sums of squares of deviations in which the individual parameter weights were modified in the process of application. During the process of system design, I put forward a design method knowledge base and generated a dynamic web reasoning framework to integrate the processes of forest industry machinery selection and weight-value adjustment. This enables expansion of the scope of the complete system and enhancement of the reasoning efficiency. I demonstrate the validity and practicability of this method using a practical example.
Models of cultural niche construction with selection and assortative mating.
Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W
2012-01-01
Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Models of cultural niche construction with selection and assortative mating.
Directory of Open Access Journals (Sweden)
Nicole Creanza
Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
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++.
Evolution of the chloroplast division machinery
Institute of Scientific and Technical Information of China (English)
Hongbo GAO; Fuli GAO
2011-01-01
Chloroplasts are photosynthetic organelles derived from endosymbiotic cyanobacteria during evolution.Dramatic changes occurred during the process of the formation and evolution of chloroplasts,including the large-scale gene transfer from chloroplast to nucleus.However,there are still many essential characters remaining.For the chloroplast division machinery,FtsZ proteins,Ftn2,SulA and part of the division site positioning system- MinD and MinE are still conserved.New or at least partially new proteins,such as FtsZ family proteins FtsZl and ARC3,ARC6H,ARC5,PDV1,PDV2 and MCD1,were introduced for the division of chloroplasts during evolution.Some bacterial cell division proteins,such as FtsA,MreB,Ftn6,FtsW and Ftsl,probably lost their function or were gradually lost.Thus,the chloroplast division machinery is a dynamically evolving structure with both conservation and innovation.
QOS Aware Formalized Model for Semantic Web Service Selection
Directory of Open Access Journals (Sweden)
Divya Sachan
2014-10-01
Full Text Available Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS based Ranking of semantic web services.
Modelling autophagy selectivity by receptor clustering on peroxisomes
Brown, Aidan I
2016-01-01
When subcellular organelles are degraded by autophagy, typically some, but not all, of each targeted organelle type are degraded. Autophagy selectivity must not only select the correct type of organelle, but must discriminate between individual organelles of the same kind. In the context of peroxisomes, we use computational models to explore the hypothesis that physical clustering of autophagy receptor proteins on the surface of each organelle provides an appropriate all-or-none signal for degradation. The pexophagy receptor proteins NBR1 and p62 are well characterized, though only NBR1 is essential for pexophagy (Deosaran {\\em et al.}, 2013). Extending earlier work by addressing the initial nucleation of NBR1 clusters on individual peroxisomes, we find that larger peroxisomes nucleate NBR1 clusters first and lose them due to competitive coarsening last, resulting in significant size-selectivity favouring large peroxisomes. This effect can explain the increased catalase signal that results from experimental s...
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....
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.
Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.
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.
Fault Diagnosis for Rotating Machinery: A Method based on Image Processing
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. PMID
Exploratory Bayesian model selection for serial genetics data.
Zhao, Jing X; Foulkes, Andrea S; George, Edward I
2005-06-01
Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.
Stationary solutions for metapopulation Moran models with mutation and selection
Constable, George W. A.; McKane, Alan J.
2015-03-01
We construct an individual-based metapopulation model of population genetics featuring migration, mutation, selection, and genetic drift. In the case of a single "island," the model reduces to the Moran model. Using the diffusion approximation and time-scale separation arguments, an effective one-variable description of the model is developed. The effective description bears similarities to the well-mixed Moran model with effective parameters that depend on the network structure and island sizes, and it is amenable to analysis. Predictions from the reduced theory match the results from stochastic simulations across a range of parameters. The nature of the fast-variable elimination technique we adopt is further studied by applying it to a linear system, where it provides a precise description of the slow dynamics in the limit of large time-scale separation.
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...
Model Selection Framework for Graph-based data
Caceres, Rajmonda S; Schmidt, Matthew C; Miller, Benjamin A; Campbell, William M
2016-01-01
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the generative mechanism of a graph. Yet given the complexity and inherent noise in real datasets, it is still very challenging to identify the best model for a given observed graph. We discuss a framework for graph model selection that leverages a long list of graph topological properties and a random forest classifier to learn and classify different graph instances. We fully characterize the discriminative power of our approach as we sweep through the parameter space of two generative models, the Erdos-Renyi and the stochastic block model. We show that our approach gets very close to known theoretical bounds and we provide insight on which topological features play a critical discriminating role.
Feature selection and survival modeling in The Cancer Genome Atlas
Directory of Open Access Journals (Sweden)
Kim H
2013-09-01
Full Text Available Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. Patients and methods: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1 1-nearest neighbor (1-NN survival prediction method; (2 random patient selection method and a Cox-based regression method with nested cross-validation; (3 least absolute shrinkage and selection operator (LASSO optimization using whole-genome gene expression profiles; or (4 gene expression profiles of cancer pathway genes. Results: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone. Conclusion: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology. Keywords: brain, feature selection
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.
The Impacts of Agricultural Machinery Purchase Subsidies on Mechanized Crop Residue Recycling
Institute of Scientific and Technical Information of China (English)
2011-01-01
Crop residue recycling can improve the quality of the cropland,and it has multiple economic and ecological benefits.However,such practice is with low adoption due to different constraints.In this paper,we use the survey data from Baoding,Hebei province,and use the probit model to explore how the agricultural machinery purchase subsidies affect the mechanized crop residue recycling.The results showed that several factors that affect farmers in adopting the practice of mechanized crop residue crop recycling.Among these factors,the cost of adopting such practice is significant.The agricultural machinery purchase subsidies can effectively reduce the cost of such practice,as well as promote mechanized crop residue recycling.The paper also proposed several actions in the future.They include increasing the subsidies on agricultural machinery purchase and increasing farmers’ awareness on crop residue recycling.
Ensemble feature selection integrating elitist roles and quantum game model
Institute of Scientific and Technical Information of China (English)
Weiping Ding; Jiandong Wang; Zhijin Guan; Quan Shi
2015-01-01
To accelerate the selection process of feature subsets in the rough set theory (RST), an ensemble elitist roles based quantum game (EERQG) algorithm is proposed for feature selec-tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles’ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec-tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Final y, the en-semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which wil greatly improve the fea-sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.
Transitions in a genotype selection model driven by coloured noises
Institute of Scientific and Technical Information of China (English)
Wang Can-Jun; Mei Dong-Cheng
2008-01-01
This paper investigates a genotype selection model subjected to both a multiplicative coloured noise and an additive coloured noise with different correlation time T1 and T2 by means of the numerical technique.By directly simulating the Langevin Equation,the following results are obtained.(1) The multiplicative coloured noise dominates,however,the effect of the additive coloured noise is not neglected in the practical gene selection process.The selection rate μ decides that the selection is propitious to gene A haploid or gene B haploid.(2) The additive coloured noise intensity α and the correlation time T2 play opposite roles.It is noted that α and T2 can not separate the single peak,while αcan make the peak disappear and T2 can make the peak be sharp.(3) The multiplicative coloured noise intensity D and the correlation time T1 can induce phase transition,at the same time they play opposite roles and the reentrance phenomenon appears.In this case,it is easy to select one type haploid from the group with increasing D and decreasing T1.
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...
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Selection between Linear Factor Models and Latent Profile Models Using Conditional Covariances
Halpin, Peter F.; Maraun, Michael D.
2010-01-01
A method for selecting between K-dimensional linear factor models and (K + 1)-class latent profile models is proposed. In particular, it is shown that the conditional covariances of observed variables are constant under factor models but nonlinear functions of the conditioning variable under latent profile models. The performance of a convenient…
Modeling selective attention using a neuromorphic analog VLSI device.
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.
Model Order Selection Rules for Covariance Structure Classification in Radar
Carotenuto, Vincenzo; De Maio, Antonio; Orlando, Danilo; Stoica, Petre
2017-10-01
The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.
Autoregressive model selection with simultaneous sparse coefficient estimation
Sang, Hailin
2011-01-01
In this paper we propose a sparse coefficient estimation procedure for autoregressive (AR) models based on penalized conditional maximum likelihood. The penalized conditional maximum likelihood estimator (PCMLE) thus developed has the advantage of performing simultaneous coefficient estimation and model selection. Mild conditions are given on the penalty function and the innovation process, under which the PCMLE satisfies a strong consistency, local $N^{-1/2}$ consistency, and oracle property, respectively, where N is sample size. Two penalty functions, least absolute shrinkage and selection operator (LASSO) and smoothly clipped average deviation (SCAD), are considered as examples, and SCAD is shown to have better performances than LASSO. A simulation study confirms our theoretical results. At the end, we provide an application of our method to a historical price data of the US Industrial Production Index for consumer goods, and the result is very promising.
Energy Technology Data Exchange (ETDEWEB)
Stoehr, Michael [Bundesdeutscher Arbeitskreis fuer Umweltbewusstes Management e.V., B.A.U.M., Muenchen (Germany); Pickel, Peter [John Deere European Technology Innovation Center, Kaiserslautern (Germany)
2012-07-01
The use of biofuels in agricultural machinery is an option to respond to climate requirements. This option is being imposed from the European Commission to manufacturers of mobile machines. The contribution under consideration formulates a mathematical model that implements the regulations of the EU Fuel Quality Directive for complex manufacturing processes in the calculation rules. Initially, this model was tested and verified by the example of the standard manufacturing process of pure rapeseed oil. Then, possibilities of optimization for the production of rapeseed oil are explored. Finally, the mathematical model was applied to the calculation of greenhouse gas emissions from camelina oil from mixed cultivation with wheat.
ABOUT ETHICS IN PLANT, MACHINERY AND EQUIPMENT VALUATION DOMAIN
Directory of Open Access Journals (Sweden)
Sorin Adrian Achim
2015-09-01
Full Text Available In this paper we put in discussion some ethical aspects of plant, machinery and equipment valuation. After a presentation of the general issues of ethics in valuation domain, we analyze the application of the fundamental ethical principles in plant, machinery and equipment valuation domain. To support our conclusions we use some findings of a study that we conducted to identify the particularities of the plant, machinery and equipment valuation activity in Romania.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Structure and selection in an autocatalytic binary polymer model
DEFF Research Database (Denmark)
Tanaka, Shinpei; Fellermann, Harold; Rasmussen, Steen
2014-01-01
An autocatalytic binary polymer system is studied as an abstract model for a chemical reaction network capable to evolve. Due to autocatalysis, long polymers appear spontaneously and their concentration is shown to be maintained at the same level as that of monomers. When the reaction starts from....... Stability, fluctuations, and dynamic selection mechanisms are investigated for the involved self-organizing processes. Copyright (C) EPLA, 2014......An autocatalytic binary polymer system is studied as an abstract model for a chemical reaction network capable to evolve. Due to autocatalysis, long polymers appear spontaneously and their concentration is shown to be maintained at the same level as that of monomers. When the reaction starts from...
Velocity selection in the symmetric model of dendritic crystal growth
Barbieri, Angelo; Hong, Daniel C.; Langer, J. S.
1987-01-01
An analytic solution of the problem of velocity selection in a fully nonlocal model of dendritic crystal growth is presented. The analysis uses a WKB technique to derive and evaluate a solvability condition for the existence of steady-state needle-like solidification fronts in the limit of small under-cooling Delta. For the two-dimensional symmetric model with a capillary anisotropy of strength alpha, it is found that the velocity is proportional to (Delta to the 4th) times (alpha exp 7/4). The application of the method in three dimensions is also described.
A simple application of FIC to model selection
Wiggins, Paul A
2015-01-01
We have recently proposed a new information-based approach to model selection, the Frequentist Information Criterion (FIC), that reconciles information-based and frequentist inference. The purpose of this current paper is to provide a simple example of the application of this criterion and a demonstration of the natural emergence of model complexities with both AIC-like ($N^0$) and BIC-like ($\\log N$) scaling with observation number $N$. The application developed is deliberately simplified to make the analysis analytically tractable.
Small populations corrections for selection-mutation models
Jabin, Pierre-Emmanuel
2012-01-01
We consider integro-differential models describing the evolution of a population structured by a quantitative trait. Individuals interact competitively, creating a strong selection pressure on the population. On the other hand, mutations are assumed to be small. Following the formalism of Diekmann, Jabin, Mischler, and Perthame, this creates concentration phenomena, typically consisting in a sum of Dirac masses slowly evolving in time. We propose a modification to those classical models that takes the effect of small populations into accounts and corrects some abnormal behaviours.
Process chain modeling and selection in an additive manufacturing context
DEFF Research Database (Denmark)
Thompson, Mary Kathryn; Stolfi, Alessandro; Mischkot, Michael
2016-01-01
can compete with traditional process chains for small production runs. Combining both types of technology added cost but no benefit in this case. The new process chain model can be used to explain the results and support process selection, but process chain prototyping is still important for rapidly......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...
General Course of Failure Distributions at Complex Machineries
Directory of Open Access Journals (Sweden)
Naqib Daneshjo
2017-02-01
Full Text Available In the process of maintenance management of machinery and devices there is necessity to keep in the mind "construction technologicity” (ability of construction technology. This term is used in evaluating of machinery construction, their groups and components in terms of production. The aim of management and planning maintenance of machinery is the failure-free operation in the application process. The range of machinery maintenance machines from routine one and inspection to general repairs is important to organize in a way to prevent unplanned idle time and failures or very likely to accidents.
Selecting, weeding, and weighting biased climate model ensembles
Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.
2012-12-01
In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.
Bayesian Model Selection with Network Based Diffusion Analysis.
Whalen, Andrew; Hoppitt, William J E
2016-01-01
A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
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.
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 Poppers 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, Akaikes 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.
Selection of key terrain attributes for SOC model
DEFF Research Database (Denmark)
Greve, Mogens Humlekrog; Adhikari, Kabindra; Chellasamy, Menaka
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...... (standh) are the first three key terrain attributes in 5-attributes-model in all resolutions, the rest 2 of 5 attributes are Normal High (NormalH) and Valley Depth (Vall_depth) at the resolution finer than 40m, and Elevation and Channel Base (Chnl_base) coarser than 40m. The models at pixels size at 88m......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...
Unifying models for X-ray selected and Radio selected BL Lac Objects
Fossati, G; Ghisellini, G; Maraschi, L; Brera-Merate, O A
1997-01-01
We discuss alternative interpretations of the differences in the Spectral Energy Distributions (SEDs) of BL Lacs found in complete Radio or X-ray surveys. A large body of observations in different bands suggests that the SEDs of BL Lac objects appearing in X-ray surveys differ from those appearing in radio surveys mainly in having a (synchrotron) spectral cut-off (or break) at much higher frequency. In order to explain the different properties of radio and X-ray selected BL Lacs Giommi and Padovani proposed a model based on a common radio luminosity function. At each radio luminosity, objects with high frequency spectral cut-offs are assumed to be a minority. Nevertheless they dominate the X-ray selected population due to the larger X-ray-to-radio-flux ratio. An alternative model explored here (reminiscent of the orientation models previously proposed) is that the X-ray luminosity function is "primary" and that at each X-ray luminosity a minority of objects has larger radio-to-X-ray flux ratio. The prediction...
Performance Characteristics of Agricultural Field Machineries In South- East Nigeria
Directory of Open Access Journals (Sweden)
Oduma O
2017-06-01
Full Text Available The field performances of agricultural field machineries in South -East agricultural zone of Nigeria were assessed, to enable farmers and agriculturists select suitable farm machines/implements based on soil conditions/characteristics for their agricultural activities. The various implements studied include; disc plough, 2-gang tandem disc harrow, ridger, rotovator and 6-row combine seed planter. Three different makes and models of tractors namely: New Holland (model-NH5610SE and capacity-55.9kw, Massey Ferguson (model-MF430E and capacity-55.2kw and Mahindra (model-NH7570E and capacity-55.9kw with 3- point hitch systems and average age of 1.3 years were used to study the field performances of each of the implements, in five different states that made up the study area. The field performances studied include; implements working speed, operation time, depth of cut, effective and theoretical field capacities, field efficiency, fuel consumption rate, implement power requirements, and wheel slippage, under different soil conditions. Results obtained revealed that the disc plough had field efficiency range of 85.74% to 88.55%, effective and theoretical field capacities range of 0.846 to 1.164ha/hr and 0.961 to 1.319ha/hr respectively; and the highest field (ploughing efficiency was obtained in loamy-sandy soil when the plough was operated with the Massey Ferguson tractor. Harrow recorded field efficiency range of 80.17 to 91.38%, effective and theoretical field capacities range of 0.931 to 1.458ha/hr and 1.151 to 1.667 ha/hr respectively; and the highest field (harrowing efficiency was obtained on sandy-clay soil by New Holland tractor. Ridger recorded 83.65 to 88.82% field efficiency, 0.932 to 1.322ha/hr effective capacity and 1.073 to 1.504ha/hr theoretical field capacity; and sandy-clay gave the highest field (ridging efficiency when operated with a New Holland tractor. The rotovator had field efficiency range of 81.10 to 89.81%, effective and
Bayesian model selection applied to artificial neural networks used for water resources modeling
Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
2008-04-01
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.
The Impact of Varied Discrimination Parameters on Mixed-Format Item Response Theory Model Selection
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…
The Hierarchical Sparse Selection Model of Visual Crowding
Directory of Open Access Journals (Sweden)
Wesley eChaney
2014-09-01
Full Text Available Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable – destroyed due to over-integration in early-stage visual processing – recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the gist of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g. specific critical spacing, spatial anisotropies, and temporal tuning, no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding— the hierarchical sparse selection (HSS model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.
The hierarchical sparse selection model of visual crowding.
Chaney, Wesley; Fischer, Jason; Whitney, David
2014-01-01
Because the environment is cluttered, objects rarely appear in isolation. The visual system must therefore attentionally select behaviorally relevant objects from among many irrelevant ones. A limit on our ability to select individual objects is revealed by the phenomenon of visual crowding: an object seen in the periphery, easily recognized in isolation, can become impossible to identify when surrounded by other, similar objects. The neural basis of crowding is hotly debated: while prevailing theories hold that crowded information is irrecoverable - destroyed due to over-integration in early stage visual processing - recent evidence demonstrates otherwise. Crowding can occur between high-level, configural object representations, and crowded objects can contribute with high precision to judgments about the "gist" of a group of objects, even when they are individually unrecognizable. While existing models can account for the basic diagnostic criteria of crowding (e.g., specific critical spacing, spatial anisotropies, and temporal tuning), no present model explains how crowding can operate simultaneously at multiple levels in the visual processing hierarchy, including at the level of whole objects. Here, we present a new model of visual crowding-the hierarchical sparse selection (HSS) model, which accounts for object-level crowding, as well as a number of puzzling findings in the recent literature. Counter to existing theories, we posit that crowding occurs not due to degraded visual representations in the brain, but due to impoverished sampling of visual representations for the sake of perception. The HSS model unifies findings from a disparate array of visual crowding studies and makes testable predictions about how information in crowded scenes can be accessed.
Finite element model selection using Particle Swarm Optimization
Mthembu, Linda; Friswell, Michael I; Adhikari, Sondipon
2009-01-01
This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has t...
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.)
Study on Fuzzy Decision for Purchasing Spare Parts for Machinery Equipment
Institute of Scientific and Technical Information of China (English)
YANG Cheng-xian; ZHANG Qi
2003-01-01
For purchasing spare parts for machinery products, the general principle, procedure and method are put forward in the paper. Fuzzy theory and method are applied to set up a decisive model for purchasing spare parts. By working out the kinds and quantities of spare parts, the purchase of parts is provided with scientific decision.
Whelan, Simon; Allen, James E; Blackburne, Benjamin P; Talavera, David
2015-01-01
Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons. A major limitation of existing model selection approaches is that they can only compare models acting upon a single type of data. Here, we extend model selection to allow comparisons between models describing different types of data by introducing the idea of adapter functions, which project aggregated models onto the originally observed sequence data. These projections are implemented in the program ModelOMatic and used to perform model selection on 3722 families from the PANDIT database, 68 genes from an arthropod phylogenomic data set, and 248 genes from a vertebrate phylogenomic data set. For the PANDIT and arthropod data, we find that amino acid models are selected for the overwhelming majority of alignments; with progressively smaller numbers of alignments selecting codon and nucleotide models, and no families selecting RY-based models. In contrast, nearly all alignments from the vertebrate data set select codon-based models. The sequence divergence, the number of sequences, and the degree of selection acting upon the protein sequences may contribute to explaining this variation in model selection. Our ModelOMatic program is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines. ModelOMatic is available at https://code.google.com/p/modelomatic/.
Accessorizing the human mitochondrial transcription machinery.
Bestwick, Megan L; Shadel, Gerald S
2013-06-01
The human genome comprises large chromosomes in the nucleus and mitochondrial DNA (mtDNA) housed in the dynamic mitochondrial network. Human cells contain up to thousands of copies of the double-stranded, circular mtDNA molecule that encodes essential subunits of the oxidative phosphorylation complexes and the rRNAs and tRNAs needed to translate these in the organelle matrix. Transcription of human mtDNA is directed by a single-subunit RNA polymerase, POLRMT, which requires two primary transcription factors, TFB2M (transcription factor B2, mitochondrial) and TFAM (transcription factor A, mitochondrial), to achieve basal regulation of the system. Here, we review recent advances in understanding the structure and function of the primary human transcription machinery and the other factors that facilitate steps in transcription beyond initiation and provide more intricate control over the system. Copyright © 2013 Elsevier Ltd. All rights reserved.
On the mechanochemical machinery underlying chromatin remodeling
Yusufaly, Tahir I.
This dissertation discuss two recent efforts, via a unique combination of structural bioinformatics and density functional theory, to unravel some of the details concerning how molecular machinery within the eukaryotic cell nucleus controls chromatin architecture. The first, a study of the 5-methylation of cytosine in 5'-CG-3' : 5'-CG-3' base-pair steps, reveals that the methyl groups roughen the local elastic energy landscape of the DNA. This enhances the probability of the canonical B-DNA structure transitioning into the undertwisted A-like and overtwisted C-like forms seen in nucleosomes, or looped segments of DNA bound to histones. The second part focuses on the formation of salt bridges between arginine residues in histones and phosphate groups on the DNA backbone. The arginine residues are ob- served to apply a tunable mechanical load to the backbone, enabling precision-controlled activation of DNA deformations.
The SNARE machinery in mast cell secretion
Directory of Open Access Journals (Sweden)
Axel eLorentz
2012-06-01
Full Text Available Mast cells are known as inflammatory cells which exert their functions in allergic and anaphylactic reactions by secretion of numerous inflammatory mediators. During an allergic response, the high-affinity IgE receptor, FcεRI, becomes cross-linked by receptor-bound IgE and antigen resulting in immediate release of pre-synthesized mediators – stored in granules – as well as in de novo synthesis of various mediators like cytokines and chemokines. Soluble N-ethylmaleimide-Sensitive Factor (NSF Attachment Protein (SNAP Receptors (SNARE proteins were found to play a central role in regulating membrane fusion events during exocytosis. In addition, several accessory regulators like Munc13, Munc18, Rab GTPases, SCAMPs, complexins or synaptotagmins were found to be involved in membrane fusion. In this review we summarize our current knowledge about the SNARE machinery and its mechanism of action in mast cell secretion.
Numerical Noise Prediction in Fluid Machinery
Institute of Scientific and Technical Information of China (English)
Iris PANTLE; Franco MAGAGNATO; Martin GABI
2005-01-01
Numerical methods successively became important in the design and optimization of fluid machinery. However,as noise emission is considered, one can hardly find standardized prediction methods combining flow and acoustical optimization. Several numerical field methods for sound calculations have been developed. Due to the complexity of the considered flow, approaches must be chosen to avoid exhaustive computing. In this contribution the noise of a simple propeller is investigated. The configurations of the calculations comply with an existing experimental setup chosen for evaluation. The used in-house CFD solver SPARC contains an acoustic module based on Ffowcs Williams-Hawkings Acoustic Analogy. From the flow results of the time dependent Large Eddy Simulation the time dependent acoustic sources are extracted and given to the acoustic module where relevant sound pressure levels are calculated. The difficulties, which arise while proceeding from open to closed rotors and from gas to liquid are discussed.
Selection of Representative Models for Decision Analysis Under Uncertainty
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.
Directory of Open Access Journals (Sweden)
Ana Pilipović
2014-03-01
Full Text Available Additive manufacturing (AM is increasingly applied in the development projects from the initial idea to the finished product. The reasons are multiple, but what should be emphasised is the possibility of relatively rapid manufacturing of the products of complicated geometry based on the computer 3D model of the product. There are numerous limitations primarily in the number of available materials and their properties, which may be quite different from the properties of the material of the finished product. Therefore, it is necessary to know the properties of the product materials. In AM procedures the mechanical properties of materials are affected by the manufacturing procedure and the production parameters. During SLS procedures it is possible to adjust various manufacturing parameters which are used to influence the improvement of various mechanical and other properties of the products. The paper sets a new mathematical model to determine the influence of individual manufacturing parameters on the polymer product made by selective laser sintering. Old mathematical model is checked by statistical method with central composite plan and it is established that old mathematical model must be expanded with new parameter beam overlay ratio. Verification of new mathematical model and optimization of the processing parameters are made on SLS machine.
Selecting global climate models for regional climate change studies.
Pierce, David W; Barnett, Tim P; Santer, Benjamin D; Gleckler, Peter J
2009-05-26
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.
Selecting global climate models for regional climate change studies
Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.
2009-01-01
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652
Multilevel selection in a resource-based model
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.
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.
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.
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.
Refined homology model of monoacylglycerol lipase: toward a selective inhibitor
Bowman, Anna L.; Makriyannis, Alexandros
2009-11-01
Monoacylglycerol lipase (MGL) is primarily responsible for the hydrolysis of 2-arachidonoylglycerol (2-AG), an endocannabinoid with full agonist activity at both cannabinoid receptors. Increased tissue 2-AG levels consequent to MGL inhibition are considered therapeutic against pain, inflammation, and neurodegenerative disorders. However, the lack of MGL structural information has hindered the development of MGL-selective inhibitors. Here, we detail a fully refined homology model of MGL which preferentially identifies MGL inhibitors over druglike noninhibitors. We include for the first time insight into the active-site geometry and potential hydrogen-bonding interactions along with molecular dynamics simulations describing the opening and closing of the MGL helical-domain lid. Docked poses of both the natural substrate and known inhibitors are detailed. A comparison of the MGL active-site to that of the other principal endocannabinoid metabolizing enzyme, fatty acid amide hydrolase, demonstrates key differences which provide crucial insight toward the design of selective MGL inhibitors as potential drugs.
Auditory-model based robust feature selection for speech recognition.
Koniaris, Christos; Kuropatwinski, Marcin; Kleijn, W Bastiaan
2010-02-01
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
POSSIBILISTIC SHARPE RATIO BASED NOVICE PORTFOLIO SELECTION MODELS
Directory of Open Access Journals (Sweden)
Rupak Bhattacharyya
2013-02-01
Full Text Available This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean, variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. The Sharpe ratio is a measure of the performance of the portfolio compared to the risk taken. The higher the Sharpe ratio, the better the performance of the portfolio is and the greater the profits of taking risk. New models of fuzzy portfolio selection considering the possibilistic Sharpe ratio, return and skewness of the portfolio are considered. The feasibility and effectiveness of the proposed method is illustrated by numerical example extracted from Bombay Stock Exchange (BSE, India and is solved by multiple objective genetic algorithm (MOGA.
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.
Automation of Endmember Pixel Selection in SEBAL/METRIC Model
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.
A Dual-Stage Two-Phase Model of Selective Attention
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…
Machinery failure analysis and troubleshooting practical machinery management for process plants
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
glmulti: An R Package for Easy Automated Model Selection with (Generalized Linear Models
Directory of Open Access Journals (Sweden)
Vincent Calcagno
2010-10-01
Full Text Available We introduce glmulti, an R package for automated model selection and multi-model inference with glm and related functions. From a list of explanatory variables, the provided function glmulti builds all possible unique models involving these variables and, optionally, their pairwise interactions. Restrictions can be specified for candidate models, by excluding specific terms, enforcing marginality, or controlling model complexity. Models are fitted with standard R functions like glm. The n best models and their support (e.g., (QAIC, (QAICc, or BIC are returned, allowing model selection and multi-model inference through standard R functions. The package is optimized for large candidate sets by avoiding memory limitation, facilitating parallelization and providing, in addition to exhaustive screening, a compiled genetic algorithm method. This article briefly presents the statistical framework and introduces the package, with applications to simulated and real data.
Selection between foreground models for global 21-cm experiments
Harker, Geraint
2015-01-01
The precise form of the foregrounds for sky-averaged measurements of the 21-cm line during and before the epoch of reionization is unknown. We suggest that the level of complexity in the foreground models used to fit global 21-cm data should be driven by the data, under a Bayesian model selection methodology. A first test of this approach is carried out by applying nested sampling to simplified models of global 21-cm data to compute the Bayesian evidence for the models. If the foregrounds are assumed to be polynomials of order n in log-log space, we can infer the necessity to use n=4 rather than n=3 with <2h of integration with limited frequency coverage, for reasonable values of the n=4 coefficient. Using a higher-order polynomial does not necessarily prevent a significant detection of the 21-cm signal. Even for n=8, we can obtain very strong evidence distinguishing a reasonable model for the signal from a null model with 128h of integration. More subtle features of the signal may, however, be lost if the...
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.
Development of solar drying model for selected Cambodian fish species.
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.
Selection Strategies for Social Influence in the Threshold Model
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.
Selection of models to calculate the LLW source term
Energy Technology Data Exchange (ETDEWEB)
Sullivan, T.M. (Brookhaven National Lab., Upton, NY (United States))
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.
Quantum Model for the Selectivity Filter in K$^{+}$ Ion Channel
Cifuentes, A A
2013-01-01
In this work, we present a quantum transport model for the selectivity filter in the KcsA potassium ion channel. This model is fully consistent with the fact that two conduction pathways are involved in the translocation of ions thorough the filter, and we show that the presence of a second path may actually bring advantages for the filter as a result of quantum interference. To highlight interferences and resonances in the model, we consider the selectivity filter to be driven by a controlled time-dependent external field which changes the free energy scenario and consequently the conduction of the ions. In particular, we demonstrate that the two-pathway conduction mechanism is more advantageous for the filter when dephasing in the transient configurations is lower than in the main configurations. As a matter of fact, K$^+$ ions in the main configurations are highly coordinated by oxygen atoms of the filter backbone and this increases noise. Moreover, we also show that, for a wide range of driving frequencie...
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.
A Successive Selection Method for finite element model updating
Gou, Baiyong; Zhang, Weijie; Lu, Qiuhai; Wang, Bo
2016-03-01
Finite Element (FE) model can be updated effectively and efficiently by using the Response Surface Method (RSM). However, it often involves performance trade-offs such as high computational cost for better accuracy or loss of efficiency for lots of design parameter updates. This paper proposes a Successive Selection Method (SSM), which is based on the linear Response Surface (RS) function and orthogonal design. SSM rewrites the linear RS function into a number of linear equations to adjust the Design of Experiment (DOE) after every FE calculation. SSM aims to interpret the implicit information provided by the FE analysis, to locate the Design of Experiment (DOE) points more quickly and accurately, and thereby to alleviate the computational burden. This paper introduces the SSM and its application, describes the solution steps of point selection for DOE in detail, and analyzes SSM's high efficiency and accuracy in the FE model updating. A numerical example of a simply supported beam and a practical example of a vehicle brake disc show that the SSM can provide higher speed and precision in FE model updating for engineering problems than traditional RSM.
Selection Experiments in the Penna Model for Biological Aging
Medeiros, G.; Idiart, M. A.; de Almeida, R. M. C.
We consider the Penna model for biological aging to investigate correlations between early fertility and late life survival rates in populations at equilibrium. We consider inherited initial reproduction ages together with a reproduction cost translated in a probability that mother and offspring die at birth, depending on the mother age. For convenient sets of parameters, the equilibrated populations present genetic variability in what regards both genetically programmed death age and initial reproduction age. In the asexual Penna model, a negative correlation between early life fertility and late life survival rates naturally emerges in the stationary solutions. In the sexual Penna model, selection experiments are performed where individuals are sorted by initial reproduction age from the equilibrated populations and the separated populations are evolved independently. After a transient, a negative correlation between early fertility and late age survival rates also emerges in the sense that populations that start reproducing earlier present smaller average genetically programmed death age. These effects appear due to the age structure of populations in the steady state solution of the evolution equations. We claim that the same demographic effects may be playing an important role in selection experiments in the laboratory.
Industrial Machinery Maintenance and Repair. Florida Vocational Program Guide.
University of South Florida, Tampa. Dept. of Adult and Vocational Education.
This vocational program guide is intended to assist in the organization, operation, and evaluation of a program in industrial machinery maintenance and repair in school districts, area vocational centers, and community colleges. The following topics are covered: job duties of millwrights, maintenance mechanics, and machinery erectors; program…
Stepwise evolution of the Sec machinery in Proteobacteria
van der Sluis, EO; Driessen, AJM; Sluis, Eli O. van der
2006-01-01
The Sec machinery facilitates the translocation of proteins across and into biological membranes. In several of the Proteobacteria, this machinery contains accessory features that are not present in any other bacterial division. The genomic distribution of these features in the context of bacterial
Recession Hits China’s Textile Machinery Markets
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
China’s textile machinery industry saw a continued decline intotaI profit and a hefty slump in imports and exports in the first twomonths this year.Analysts anticipated a continued weakening ofmomentum for China’s textile machinery markets owing to weakerconsumer spending and easing export growth.
ITALIAN TEXTILE MACHINERY WORKSHOP IN SUZHOU(CHINA)
Institute of Scientific and Technical Information of China (English)
2007-01-01
The Association of Italian Textile Machinery Manufacturers (ACIMIT)and the Italian Trade Commission(ICE)held a technical workshop on Italian textile machinery for the production of technical textiles and nonwovens in China.The workshop occurred in Suzhou(Juangsu Province),on May 24th-25th,2007.
46 CFR 282.23 - Hull and machinery insurance.
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...
46 CFR 111.103-9 - Machinery stop stations.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Machinery stop stations. 111.103-9 Section 111.103-9 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) ELECTRICAL ENGINEERING ELECTRIC SYSTEMS-GENERAL REQUIREMENTS Remote Stopping Systems § 111.103-9 Machinery stop stations. (a) Each forced...
Occupational incidents with self-propelled machinery in Austrian agriculture.
Mayrhofer, Hannes; Quendler, Elisabeth; Boxberger, Josef
2013-01-01
Tractors, self-propelled harvesting machinery, and material handling machinery are the most commonly used self-propelled machineries in Austrian agriculture, and they have similarities in main accident scenarios. Statistical data of all occupational incidents with these machines reported between 2008 and 2010 were analyzed to obtain information about the circumstances of the incidents, and about the victims and their work environments. Criteria of recognized occupational incidents provided by the Austrian Social Insurance Institution for Farmers were analyzed according to machinery category by means of cross-tabulation and chi-square tests. The results were discussed and evaluated in the context of the literature. The results of the analysis of the databases show that 786 occupational incidents with tractors, self-propelled harvesting machinery, and material handling machinery occurred in Austrian agriculture between 2008 and 2010. There were 231 occupational incidents in 2008; the number rose to 268 in 2009 and to 286 in 2010. A total of 41 incidents were fatal. For the machinery categories analyzed, the majority of injured victims were male, older than 40 years, Austrian citizens, and managers of a mixed-agricultural farm. A large number of the incidents occurred in all machinery categories by loss of control during operating a vehicle.
Evolution and diversification of the basal transcription machinery.
Duttke, Sascha H C
2015-03-01
Transcription initiation was once thought to be regulated primarily by sequence-specific transcription factors with the basal transcription machinery being largely invariant. Gradually it became apparent that the basal transcription machinery greatly diversified during evolution and new studies now demonstrate that diversification of the TATA-binding protein (TBP) family yielded specialized and largely independent transcription systems.
A qualitative model structure sensitivity analysis method to support model selection
Van Hoey, S.; Seuntjens, P.; van der Kwast, J.; Nopens, I.
2014-11-01
The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria.
Estimation and variable selection for generalized additive partial linear models
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.
Parametric pattern selection in a reaction-diffusion model.
Directory of Open Access Journals (Sweden)
Michael Stich
Full Text Available We compare spot patterns generated by Turing mechanisms with those generated by replication cascades, in a model one-dimensional reaction-diffusion system. We determine the stability region of spot solutions in parameter space as a function of a natural control parameter (feed-rate where degenerate patterns with different numbers of spots coexist for a fixed feed-rate. While it is possible to generate identical patterns via both mechanisms, we show that replication cascades lead to a wider choice of pattern profiles that can be selected through a tuning of the feed-rate, exploiting hysteresis and directionality effects of the different pattern pathways.
46 CFR 119.465 - Ventilation of spaces containing diesel machinery.
2010-10-01
... 46 Shipping 4 2010-10-01 2010-10-01 false Ventilation of spaces containing diesel machinery. 119... MACHINERY INSTALLATION Specific Machinery Requirements § 119.465 Ventilation of spaces containing diesel machinery. (a) A space containing diesel machinery must be fitted with adequate means, such as...
Dynamic organization of the mitochondrial protein import machinery.
Straub, Sebastian P; Stiller, Sebastian B; Wiedemann, Nils; Pfanner, Nikolaus
2016-11-01
Mitochondria contain elaborate machineries for the import of precursor proteins from the cytosol. The translocase of the outer mitochondrial membrane (TOM) performs the initial import of precursor proteins and transfers the precursors to downstream translocases, including the presequence translocase and the carrier translocase of the inner membrane, the mitochondrial import and assembly machinery of the intermembrane space, and the sorting and assembly machinery of the outer membrane. Although the protein translocases can function as separate entities in vitro, recent studies revealed a close and dynamic cooperation of the protein import machineries to facilitate efficient transfer of precursor proteins in vivo. In addition, protein translocases were found to transiently interact with distinct machineries that function in the respiratory chain or in the maintenance of mitochondrial membrane architecture. Mitochondrial protein import is embedded in a regulatory network that ensures protein biogenesis, membrane dynamics, bioenergetic activity and quality control.
Linear regression model selection using p-values when the model dimension grows
Pokarowski, Piotr; Teisseyre, Paweł
2012-01-01
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied for families of linear regression models. We prove that such procedures are consistent i.e. the minimal true model is chosen with probability tending to 1 even when the number of models under consideration slowly increases with a sample size. The simulation study indicates that introduced methods perform promisingly when compared with Akaike and Bayesian Information Criteria.
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.
The grouting handbook a step-by-step guide for foundation design and machinery installation
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
Prediction of Farmers’ Income and Selection of Model ARIMA
Institute of Scientific and Technical Information of China (English)
2010-01-01
Based on the research technology of scholars’ prediction of farmers’ income and the data of per capita annual net income in rural households in Henan Statistical Yearbook from 1979 to 2009,it is found that time series of farmers’ income is in accordance with I(2)non-stationary process.The order-determination and identification of the model are achieved by adopting the correlogram-based analytical method of Box-Jenkins.On the basis of comparing a group of model properties with different parameters,model ARIMA(4,2,2)is built up.The testing result shows that the residual error of the selected model is white noise and accords with the normal distribution,which can be used to predict farmers’ income.The model prediction indicates that income in rural households will continue to increase from 2009 to 2012 and will reach the value of 2 282.4,2 502.9,2 686.9 and 2 884.5 respectively.The growth speed will go down from fast to slow with weak sustainability.
BUILDING ROBUST APPEARANCE MODELS USING ON-LINE FEATURE SELECTION
Energy Technology Data Exchange (ETDEWEB)
PORTER, REID B. [Los Alamos National Laboratory; LOVELAND, ROHAN [Los Alamos National Laboratory; ROSTEN, ED [Los Alamos National Laboratory
2007-01-29
In many tracking applications, adapting the target appearance model over time can improve performance. This approach is most popular in high frame rate video applications where latent variables, related to the objects appearance (e.g., orientation and pose), vary slowly from one frame to the next. In these cases the appearance model and the tracking system are tightly integrated, and latent variables are often included as part of the tracking system's dynamic model. In this paper we describe our efforts to track cars in low frame rate data (1 frame/second) acquired from a highly unstable airborne platform. Due to the low frame rate, and poor image quality, the appearance of a particular vehicle varies greatly from one frame to the next. This leads us to a different problem: how can we build the best appearance model from all instances of a vehicle we have seen so far. The best appearance model should maximize the future performance of the tracking system, and maximize the chances of reacquiring the vehicle once it leaves the field of view. We propose an online feature selection approach to this problem and investigate the performance and computational trade-offs with a real-world dataset.
Stochastic group selection model for the evolution of altruism
Silva, A T C; Silva, Ana T. C.
1999-01-01
We study numerically and analytically a stochastic group selection model in which a population of asexually reproducing individuals, each of which can be either altruist or non-altruist, is subdivided into $M$ reproductively isolated groups (demes) of size $N$. The cost associated with being altruistic is modelled by assigning the fitness $1- \\tau$, with $\\tau \\in [0,1]$, to the altruists and the fitness 1 to the non-altruists. In the case that the altruistic disadvantage $\\tau$ is not too large, we show that the finite $M$ fluctuations are small and practically do not alter the deterministic results obtained for $M \\to \\infty$. However, for large $\\tau$ these fluctuations greatly increase the instability of the altruistic demes to mutations. These results may be relevant to the dynamics of parasite-host systems and, in particular, to explain the importance of mutation in the evolution of parasite virulence.
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...... to noise ratios. A new plot of our time series $C_{p}$ statistic is highly informative about the choice of model....... of squares of one step ahead standardized prediction errors, when the parameters are estimated, differs from the chi-squared distribution by a term which tends to infinity at a lower rate than $\\chi _{n}^{2}$. We further prove that, in the prediction error decomposition, the term involving the sum...
On Model Specification and Selection of the Cox Proportional Hazards Model*
Lin, Chen-Yen; Halabi, Susan
2013-01-01
Prognosis plays a pivotal role in patient management and trial design. A useful prognostic model should correctly identify important risk factors and estimate their effects. In this article, we discuss several challenges in selecting prognostic factors and estimating their effects using the Cox proportional hazards model. Although a flexible semiparametric form, the Cox’s model is not entirely exempt from model misspecification. To minimize possible misspecification, instead of imposing tradi...
Radial Domany-Kinzel models with mutation and selection
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.
Ultrastructural model for size selectivity in glomerular filtration.
Edwards, A; Daniels, B S; Deen, W M
1999-06-01
A theoretical model was developed to relate the size selectivity of the glomerular barrier to the structural characteristics of the individual layers of the capillary wall. Thicknesses and other linear dimensions were evaluated, where possible, from previous electron microscopic studies. The glomerular basement membrane (GBM) was represented as a homogeneous material characterized by a Darcy permeability and by size-dependent hindrance coefficients for diffusion and convection, respectively; those coefficients were estimated from recent data obtained with isolated rat GBM. The filtration slit diaphragm was modeled as a single row of cylindrical fibers of equal radius but nonuniform spacing. The resistances of the remainder of the slit channel, and of the endothelial fenestrae, to macromolecule movement were calculated to be negligible. The slit diaphragm was found to be the most restrictive part of the barrier. Because of that, macromolecule concentrations in the GBM increased, rather than decreased, in the direction of flow. Thus the overall sieving coefficient (ratio of Bowman's space concentration to that in plasma) was predicted to be larger for the intact capillary wall than for a hypothetical structure with no GBM. In other words, because the slit diaphragm and GBM do not act independently, the overall sieving coefficient is not simply the product of those for GBM alone and the slit diaphragm alone. Whereas the calculated sieving coefficients were sensitive to the structural features of the slit diaphragm and to the GBM hindrance coefficients, variations in GBM thickness or filtration slit frequency were predicted to have little effect. The ability of the ultrastructural model to represent fractional clearance data in vivo was at least equal to that of conventional pore models with the same number of adjustable parameters. The main strength of the present approach, however, is that it provides a framework for relating structural findings to the size
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.
Modeling selective elimination of quiescent cancer cells from bone marrow.
Cavnar, Stephen P; Rickelmann, Andrew D; Meguiar, Kaille F; Xiao, Annie; Dosch, Joseph; Leung, Brendan M; Cai Lesher-Perez, Sasha; Chitta, Shashank; Luker, Kathryn E; Takayama, Shuichi; Luker, Gary D
2015-08-01
Patients with many types of malignancy commonly harbor quiescent disseminated tumor cells in bone marrow. These cells frequently resist chemotherapy and may persist for years before proliferating as recurrent metastases. To test for compounds that eliminate quiescent cancer cells, we established a new 384-well 3D spheroid model in which small numbers of cancer cells reversibly arrest in G1/G0 phase of the cell cycle when cultured with bone marrow stromal cells. Using dual-color bioluminescence imaging to selectively quantify viability of cancer and stromal cells in the same spheroid, we identified single compounds and combination treatments that preferentially eliminated quiescent breast cancer cells but not stromal cells. A treatment combination effective against malignant cells in spheroids also eliminated breast cancer cells from bone marrow in a mouse xenograft model. This research establishes a novel screening platform for therapies that selectively target quiescent tumor cells, facilitating identification of new drugs to prevent recurrent cancer. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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.
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.
Bayesian Model Selection With Network Based Diffusion Analysis
Directory of Open Access Journals (Sweden)
Andrew eWhalen
2016-04-01
Full Text Available A number of recent studies have used Network Based Diffusion Analysis (NBDA to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA. To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.
Mathematical Modeling of the Agriculture Crop Technology
Directory of Open Access Journals (Sweden)
D. Drucioc
1999-02-01
Full Text Available The organized structure of computer system for economic and ecological estimation of agriculture crop technologies is described. The system is composed of six interconnected blocks. The linear, non-linear and stochastic mathematical models for machinery sizing and selection in farm-level cropping system is presented in the mathematical model block of computer system.
Ribosome evolution: Emergence of peptide synthesis machinery
Indian Academy of Sciences (India)
Koji Tamura
2011-12-01
Proteins, the main players in current biological systems, are produced on ribosomes by sequential amide bond (peptide bond) formations between amino-acid-bearing tRNAs. The ribosome is an exquisite super-complex of RNA-proteins, containing more than 50 proteins and at least 3 kinds of RNAs. The combination of a variety of side chains of amino acids (typically 20 kinds with some exceptions) confers proteins with extraordinary structure and functions. The origin of peptide bond formation and the ribosome is crucial to the understanding of life itself. In this article, a possible evolutionary pathway to peptide bond formation machinery (proto-ribosome) will be discussed, with a special focus on the RNA minihelix (primordial form of modern tRNA) as a starting molecule. Combining the present data with recent experimental data, we can infer that the peptidyl transferase center (PTC) evolved from a primitive system in the RNA world comprising tRNA-like molecules formed by duplication of minihelix-like small RNA.
Occupational Accidents with Agricultural Machinery in Austria.
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.
Effect of amyloids on the vesicular machinery: implications for somatic neurotransmission.
Das, Anand Kant; Pandit, Rucha; Maiti, Sudipta
2015-07-01
Certain neurodegenerative diseases are thought to be initiated by the aggregation of amyloidogenic proteins. However, the mechanism underlying toxicity remains obscure. Most of the suggested mechanisms are generic in nature and do not directly explain the neuron-type specific lesions observed in many of these diseases. Some recent reports suggest that the toxic aggregates impair the synaptic vesicular machinery. This may lead to an understanding of the neuron-type specificity observed in these diseases. A disruption of the vesicular machinery can also be deleterious for extra-synaptic, especially somatic, neurotransmission (common in serotonergic and dopaminergic systems which are specifically affected in Alzheimer's disease (AD) and Parkinson's disease (PD), respectively), though this relationship has remained unexplored. In this review, we discuss amyloid-induced damage to the neurotransmitter vesicular machinery, with an eye on the possible implications for somatic exocytosis. We argue that the larger size of the system, and the availability of multi-photon microscopy techniques for directly visualizing monoamines, make the somatic exocytosis machinery a more tractable model for understanding the effect of amyloids on all types of vesicular neurotransmission. Indeed, exploring this neglected connection may not just be important, it may be a more fruitful route for understanding AD and PD.
The peroxisomal protein import machinery displays a preference for monomeric substrates.
Freitas, Marta O; Francisco, Tânia; Rodrigues, Tony A; Lismont, Celien; Domingues, Pedro; Pinto, Manuel P; Grou, Cláudia P; Fransen, Marc; Azevedo, Jorge E
2015-04-01
Peroxisomal matrix proteins are synthesized on cytosolic ribosomes and transported by the shuttling receptor PEX5 to the peroxisomal membrane docking/translocation machinery, where they are translocated into the organelle matrix. Under certain experimental conditions this protein import machinery has the remarkable capacity to accept already oligomerized proteins, a property that has heavily influenced current models on the mechanism of peroxisomal protein import. However, whether or not oligomeric proteins are really the best and most frequent clients of this machinery remain unclear. In this work, we present three lines of evidence suggesting that the peroxisomal import machinery displays a preference for monomeric proteins. First, in agreement with previous findings on catalase, we show that PEX5 binds newly synthesized (monomeric) acyl-CoA oxidase 1 (ACOX1) and urate oxidase (UOX), potently inhibiting their oligomerization. Second, in vitro import experiments suggest that monomeric ACOX1 and UOX are better peroxisomal import substrates than the corresponding oligomeric forms. Finally, we provide data strongly suggesting that although ACOX1 lacking a peroxisomal targeting signal can be imported into peroxisomes when co-expressed with ACOX1 containing its targeting signal, this import pathway is inefficient.
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
Improving permafrost distribution modelling using feature selection algorithms
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
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
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...... to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous...... 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....
Multiphysics modeling of selective laser sintering/melting
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
Patch-based generative shape model and MDL model selection for statistical analysis of archipelagos
DEFF Research Database (Denmark)
Ganz, Melanie; Nielsen, Mads; Brandt, Sami
2010-01-01
We propose a statistical generative shape model for archipelago-like structures. These kind of structures occur, for instance, in medical images, where our intention is to model the appearance and shapes of calcifications in x-ray radio graphs. The generative model is constructed by (1) learning...... a patch-based dictionary for possible shapes, (2) building up a time-homogeneous Markov model to model the neighbourhood correlations between the patches, and (3) automatic selection of the model complexity by the minimum description length principle. The generative shape model is proposed...... as a probability distribution of a binary image where the model is intended to facilitate sequential simulation. Our results show that a relatively simple model is able to generate structures visually similar to calcifications. Furthermore, we used the shape model as a shape prior in the statistical segmentation...
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.
Hyperopt: a Python library for model selection and hyperparameter optimization
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.
NVC Based Model for Selecting Effective Requirement Elicitation Technique
Directory of Open Access Journals (Sweden)
Md. Rizwan Beg
2012-10-01
Full Text Available Requirement Engineering process starts from gathering of requirements i.e.; requirements elicitation. Requirementselicitation (RE is the base building block for a software project and has very high impact onsubsequent design and builds phases as well. Accurately capturing system requirements is the major factorin the failure of most of software projects. Due to the criticality and impact of this phase, it is very importantto perform the requirements elicitation in no less than a perfect manner. One of the most difficult jobsfor elicitor is to select appropriate technique for eliciting the requirement. Interviewing and Interactingstakeholder during Elicitation process is a communication intensive activity involves Verbal and Nonverbalcommunication (NVC. Elicitor should give emphasis to Non-verbal communication along with verbalcommunication so that requirements recorded more efficiently and effectively. In this paper we proposea model in which stakeholders are classified by observing non-verbal communication and use it as a basefor elicitation technique selection. We also propose an efficient plan for requirements elicitation which intendsto overcome on the constraints, faced by elicitor.
Scaling limits of a model for selection at two scales
Luo, Shishi; Mattingly, Jonathan C.
2017-04-01
The dynamics of a population undergoing selection is a central topic in evolutionary biology. This question is particularly intriguing in the case where selective forces act in opposing directions at two population scales. For example, a fast-replicating virus strain outcompetes slower-replicating strains at the within-host scale. However, if the fast-replicating strain causes host morbidity and is less frequently transmitted, it can be outcompeted by slower-replicating strains at the between-host scale. Here we consider a stochastic ball-and-urn process which models this type of phenomenon. We prove the weak convergence of this process under two natural scalings. The first scaling leads to a deterministic nonlinear integro-partial differential equation on the interval [0,1] with dependence on a single parameter, λ. We show that the fixed points of this differential equation are Beta distributions and that their stability depends on λ and the behavior of the initial data around 1. The second scaling leads to a measure-valued Fleming–Viot process, an infinite dimensional stochastic process that is frequently associated with a population genetics.
China＇s Textile Machinery Industry：Where to Go？
Institute of Scientific and Technical Information of China (English)
2012-01-01
China＇s textile machinery especially cotton-spinning machinery, after several decades of development, has been relatively mature and closer to the foreign advanced level day by day; however, there are still many problems. Since China＇s entry into WTO, domestic textile enterprises have seized this excellent opportunity to obtain fast development and progress. Currently, accelerating the pace of technological innovation for the development of series of products, vigorously implementing the export strategy to seek new ways for technical upgrade, and exploring the new R＆D mode for China＇s textile machinery are hanging over the enterprises＇ heads.
Safe design and construction of machinery regulation, practice and performance
Bluff, Elizabeth
2015-01-01
The origin of this book is the compelling evidence that a high proportion of machinery-related deaths and injuries are attributable to genuine and serious risks originating within machine design and construction. This trend continues despite significant legal obligations, notably the European regulatory regime giving effect to the Machinery Directive (among others), and a substantial body of specialist knowledge originating in the disciplines of human factors and safety engineering. Grounded in empirical research with machinery manufacturers, this book aims to elucidate the factors and process
Development and improvement: Status of Textile Machinery Manufacturing in China
Institute of Scientific and Technical Information of China (English)
2001-01-01
The past over 20 years of reform & open-door practices has witnessed an amazingly rapid development in China’s textile machinery manufacturing.1. The change in the production scale: the number oftextile machinery manufacturers grew from over 150 plants listed exceptionally in the category of textile industrial system in the old traditional planned economy to the over 500 plants that come from all sectors of industries engaged in textile machinery manufacturing in the new socialist market-driven economy. The production output value grew from 870 million RMB(Chinese Yuan) in 1987 to 14.7 billion RMB, 18
The ESCRT machinery: new roles at new holes.
Olmos, Y; Carlton, J G
2016-02-01
The ESCRT machinery drives a diverse collection of membrane remodeling events, including multivesicular body biogenesis, release of enveloped retroviruses and both reformation of the nuclear envelope and cytokinetic abscission during mitotic exit. These events share the requirement for a topologically equivalent membrane remodeling for their completion and the cells deployment of the ESCRT machinery in these different contexts highlights its functionality as a transposable membrane-fission machinery. Here, we will examine recent data describing ESCRT-III dependent membrane remodeling and explore new roles for the ESCRT-III complex at the nuclear envelope.
Robustness and epistasis in mutation-selection models
Wolff, Andrea; Krug, Joachim
2009-09-01
We investigate the fitness advantage associated with the robustness of a phenotype against deleterious mutations using deterministic mutation-selection models of a quasispecies type equipped with a mesa-shaped fitness landscape. We obtain analytic results for the robustness effect which become exact in the limit of infinite sequence length. Thereby, we are able to clarify a seeming contradiction between recent rigorous work and an earlier heuristic treatment based on mapping to a Schrödinger equation. We exploit the quantum mechanical analogy to calculate a correction term for finite sequence lengths and verify our analytic results by numerical studies. In addition, we investigate the occurrence of an error threshold for a general class of epistatic landscapes and show that diminishing epistasis is a necessary but not sufficient condition for error threshold behaviour.
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.
Agent-Based vs. Equation-based Epidemiological Models:A Model Selection Case Study
Energy Technology Data Exchange (ETDEWEB)
Sukumar, Sreenivas R [ORNL; Nutaro, James J [ORNL
2012-01-01
This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation based model and an agent based model of the 1918 Spanish flu and we leverage data published in the literature for our case- study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.
Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection
Li, Gang; McDonald, Geoff L.; Zhao, Qing
2017-01-01
This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.
Exploring GPS Data for Operational Analysis of Farm Machinery
Directory of Open Access Journals (Sweden)
Ramin Shamshiri
2013-04-01
Full Text Available Global Positioning System (GPS has made a great evolution in different aspects of modern agricultural sectors. Today, a growing number of crop producers are using GPS and other modern electronic and computer equipments to practice Site Specific Management (SSM and precision agriculture. This technology has the potential in agricultural mechanization by providing farmers with a sophisticated tool to measure yield on much smaller scales as well as precisely determination and automatic storing of variables such as field time, working area, machine travel distance and speed, fuel consumption and yield information. This study focuses on how to interpret and process raw GPS data for operational analysis of farm machinery. Exact determinations of field activities using GPS data along with accurate measurements and records of yield provide an integrated tool to calculate field efficiency and field machine index which in turn increases machine productivity and labor saving. The results can also provide graphical tools for visualizing machine operator’s performance as well as making decision on field and machine size and selection.
Model selection by LASSO methods in a change-point model
Ciuperca, Gabriela
2011-01-01
The paper considers a linear regression model with multiple change-points occurring at unknown times. The LASSO technique is very interesting since it allows the parametric estimation, including the change-points, and automatic variable selection simultaneously. The asymptotic properties of the LASSO-type (which has as particular case the LASSO estimator) and of the adaptive LASSO estimators are studied. For this last estimator the oracle properties are proved. In both cases, a model selection criterion is proposed. Numerical examples are provided showing the performances of the adaptive LASSO estimator compared to the LS estimator.
Institute of Scientific and Technical Information of China (English)
2010-01-01
@@ Internal Impacts on Textile Machinery Sector 1.Industrial Scale Like many other industrial sectors,the textile machinery industry was also affected by global financial crisis that led to credit crunch in the important markets where consumers'spending plummeted.The textile machinery manufacturers ran into a lot of problems in the balance sheets when international markets were structured in a way that is different from domestic market in terms of consumers'demand.It is very hard for these manufacturers to gain some market share simply by shifting their export-oriented products to local clients in hope to overcome difficulties.
Chain-Wise Generalization of Road Networks Using Model Selection
Bulatov, D.; Wenzel, S.; Häufel, G.; Meidow, J.
2017-05-01
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.
A simple model of group selection that cannot be analyzed with inclusive fitness
M. van Veelen; S. Luo; B. Simon
2014-01-01
A widespread claim in evolutionary theory is that every group selection model can be recast in terms of inclusive fitness. Although there are interesting classes of group selection models for which this is possible, we show that it is not true in general. With a simple set of group selection models,
ShanghaiTex 2011,Textile Machinery Giants Gather in China
Institute of Scientific and Technical Information of China (English)
Wang Ting
2011-01-01
＂Over 1000 exhibitions from 18 countries and regions,taking an exhibition area of 92000 square meters wide,ShanghaiTex 2011 unveiled the textile machinery industrial exhibition shows in the city of Shanghai this June.
The kinematics of machinery outlines of a theory of machines
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.
Open innovation and supply chain management in food machinery ...
African Journals Online (AJOL)
user
companies are thus building strong supply chain partnerships with business .... Focusing on studies referring to food and food machinery fields, Sarkar and Costa (2008) reviews three examples of open ...... Harvard Business Review, Vol. 80,.
Therapeutic interventions to disrupt the protein synthetic machinery in melanoma.
Kardos, Gregory R; Robertson, Gavin P
2015-09-01
Control of the protein synthetic machinery is deregulated in many cancers, including melanoma, to increase the protein production. Tumor suppressors and oncogenes play key roles in protein synthesis from the transcription of rRNA and ribosome biogenesis to mRNA translation initiation and protein synthesis. Major signaling pathways are altered in melanoma to modulate the protein synthetic machinery, thereby promoting tumor development. However, despite the importance of this process in melanoma development, involvement of the protein synthetic machinery in this cancer type is an underdeveloped area of study. Here, we review the coupling of melanoma development to deregulation of the protein synthetic machinery. We examine existing knowledge regarding RNA polymerase I inhibition and mRNA translation focusing on their inhibition for therapeutic applications in melanoma. Furthermore, the contribution of amino acid biosynthesis and involvement of ribosomal proteins are also reviewed as future therapeutic strategies to target deregulated protein production in melanoma.
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
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...... manufacturing systems, sensors and materials, CAD/CAM/CAE for precision machinery, computation/numerical method, intelligent system and ap- proach, vibration engineering, mechanism design, and fluid- dynamics/thermodynamics....
Designing a Machinery Control System (MCS) Security Testbed
2014-09-01
smart carrier machinery control system SCADA supervisory control and data acquisition SPST single pole single throw TF functional test TE exception...in Supervisory Control and Data Acquisition ( SCADA ) systems, industrial control systems (ICS’s) and machinery control systems (MCS’s). Today’s modern...for newly discovered security flaws. The concern over vulnerabilities in SCADA systems is due to the equipment they control and their impact, as an
A Study of Sugarcane Leaf-Removal Machinery during Harvest
2010-01-01
Problem statement: Sugarcane leaf-removing tools could help speed up sugarcane harvest and reduce contamination. Moreover, leaf-removal machinery can solve the problems of sugarcane burning and workers can increase sugarcane harvest production too. The purpose of this research was to study the use of leaf-removal machinery in the post-harvest production of sugarcane to reduce harvest production time and contaminant. Approach: This study focused on the LK92-11 variety of sugarcane having a har...
Empirical evaluation of scoring functions for Bayesian network model selection.
Liu, Zhifa; Malone, Brandon; Yuan, Changhe
2012-01-01
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also
Origins of the machinery of recombination and sex.
Cavalier-Smith, T
2002-02-01
Mutation plays the primary role in evolution that Weismann mistakenly attributed to sex. Homologous recombination, as in sex, is important for population genetics--shuffling of minor variants, but relatively insignificant for large-scale evolution. Major evolutionary innovations depend much more on illegitimate recombination, which makes novel genes by gene duplication and by gene chimaerisation--essentially mutational forces. The machinery of recombination and sex evolved in two distinct bouts of quantum evolution separated by nearly 3 Gy of stasis; I discuss their nature and causes. The dominant selective force in the evolution of recombination and sex has been selection for replicational fidelity and viability; without the recombination machinery, accurate reproduction, stasis, resistance to radical deleterious evolutionary change and preservation of evolutionary innovations would be impossible. Recombination proteins betray in their phylogeny and domain structure a key role for gene duplication and chimaerisation in their own origin. They arose about 3.8 Gy ago to enable faithful replication and segregation of the first circular DNA genomes in precellular ancestors of Gram-negative eubacteria. Then they were recruited and modified by selfish genetic parasites (viruses; transposons) to help them spread from host to host. Bacteria differ fundamentally from eukaryotes in that gene transfer between cells, whether incidental to their absorptive feeding on DNA and virus infection or directly by plasmids, involves only genomic fragments. This was radically changed by the neomuran revolution about 850 million years ago when a posibacterium evolved into the thermophilic cenancestor of eukaryotes and archaebacteria (jointly called neomurans), radically modifying or substituting its DNA-handling enzymes (those responsible for transcription as well as for replication, repair and recombination) as a coadaptive consequence of the origin of core histones to stabilise its
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.
Using nonlinear models in fMRI data analysis: model selection and activation detection.
Deneux, Thomas; Faugeras, Olivier
2006-10-01
There is an increasing interest in using physiologically plausible models in fMRI analysis. These models do raise new mathematical problems in terms of parameter estimation and interpretation of the measured data. In this paper, we show how to use physiological models to map and analyze brain activity from fMRI data. We describe a maximum likelihood parameter estimation algorithm and a statistical test that allow the following two actions: selecting the most statistically significant hemodynamic model for the measured data and deriving activation maps based on such model. Furthermore, as parameter estimation may leave much incertitude on the exact values of parameters, model identifiability characterization is a particular focus of our work. We applied these methods to different variations of the Balloon Model (Buxton, R.B., Wang, E.C., and Frank, L.R. 1998. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39: 855-864; Buxton, R.B., Uludağ, K., Dubowitz, D.J., and Liu, T.T. 2004. Modelling the hemodynamic response to brain activation. NeuroImage 23: 220-233; Friston, K. J., Mechelli, A., Turner, R., and Price, C. J. 2000. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 12: 466-477) in a visual perception checkerboard experiment. Our model selection proved that hemodynamic models better explain the BOLD response than linear convolution, in particular because they are able to capture some features like poststimulus undershoot or nonlinear effects. On the other hand, nonlinear and linear models are comparable when signals get noisier, which explains that activation maps obtained in both frameworks are comparable. The tools we have developed prove that statistical inference methods used in the framework of the General Linear Model might be generalized to nonlinear models.
Effects of Parceling on Model Selection: Parcel-Allocation Variability in Model Ranking.
Sterba, Sonya K; Rights, Jason D
2016-01-25
Research interest often lies in comparing structural model specifications implying different relationships among latent factors. In this context parceling is commonly accepted, assuming the item-level measurement structure is well known and, conservatively, assuming items are unidimensional in the population. Under these assumptions, researchers compare competing structural models, each specified using the same parcel-level measurement model. However, little is known about consequences of parceling for model selection in this context-including whether and when model ranking could vary across alternative item-to-parcel allocations within-sample. This article first provides a theoretical framework that predicts the occurrence of parcel-allocation variability (PAV) in model selection index values and its consequences for PAV in ranking of competing structural models. These predictions are then investigated via simulation. We show that conditions known to manifest PAV in absolute fit of a single model may or may not manifest PAV in model ranking. Thus, one cannot assume that low PAV in absolute fit implies a lack of PAV in ranking, and vice versa. PAV in ranking is shown to occur under a variety of conditions, including large samples. To provide an empirically supported strategy for selecting a model when PAV in ranking exists, we draw on relationships between structural model rankings in parcel- versus item-solutions. This strategy employs the across-allocation modal ranking. We developed software tools for implementing this strategy in practice, and illustrate them with an example. Even if a researcher has substantive reason to prefer one particular allocation, investigating PAV in ranking within-sample still provides an informative sensitivity analysis.
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang
2014-12-01
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.
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.
Exploitation of host clock gene machinery by hepatitis viruses B and C.
Vinciguerra, Manlio; Mazzoccoli, Gianluigi; Piccoli, Claudia; Tataranni, Tiziana; Andriulli, Angelo; Pazienza, Valerio
2013-12-21
Many aspects of cellular physiology display circadian (approximately 24-h) rhythms. Dysfunction of the circadian clock molecular circuitry is associated with human health derangements, including neurodegeneration, increased risk of cancer, cardiovascular diseases and the metabolic syndrome. Viruses triggering hepatitis depend tightly on the host cell synthesis machinery for their own replication, survival and spreading. Recent evidences support a link between the circadian clock circuitry and viruses' biological cycle within host cells. Currently, in vitro models for chronobiological studies of cells infected with viruses need to be implemented. The establishment of such in vitro models would be helpful to better understand the link between the clock gene machinery and viral replication/viral persistence in order to develop specifically targeted therapeutic regimens. Here we review the recent literature dealing with the interplay between hepatitis B and C viruses and clock genes.
Continuous time limits of the Utterance Selection Model
Michaud, Jérôme
2016-01-01
In this paper, we derive new continuous time limits of the Utterance Selection Model (USM) for language change (Baxter et al., Phys. Rev. E {\\bf 73}, 046118, 2006). This is motivated by the fact that the Fokker-Planck continuous time limit derived in the original version of the USM is only valid for a small range range of parameters. We investigate the consequences of relaxing these constraints on parameters. Using the normal approximation of the multinomial approximation, we derive a new continuous time limit of the USM in the form of a weak-noise stochastic differential equation. We argue that this weak noise, not captured by the Kramers-Moyal expansion, can not be neglected. We then propose a coarse-graining procedure, which takes the form of a stochastic version of the \\emph{heterogeneous mean field} approximation. This approximation groups the behaviour of nodes of same degree, reducing the complexity of the problem. With the help of this approximation, we study in detail two simple families of networks:...
Estimating seabed scattering mechanisms via Bayesian model selection.
Steininger, Gavin; Dosso, Stan E; Holland, Charles W; Dettmer, Jan
2014-10-01
A quantitative inversion procedure is developed and applied to determine the dominant scattering mechanism (surface roughness and/or volume scattering) from seabed scattering-strength data. The classification system is based on trans-dimensional Bayesian inversion with the deviance information criterion used to select the dominant scattering mechanism. Scattering is modeled using first-order perturbation theory as due to one of three mechanisms: Interface scattering from a rough seafloor, volume scattering from a heterogeneous sediment layer, or mixed scattering combining both interface and volume scattering. The classification system is applied to six simulated test cases where it correctly identifies the true dominant scattering mechanism as having greater support from the data in five cases; the remaining case is indecisive. The approach is also applied to measured backscatter-strength data where volume scattering is determined as the dominant scattering mechanism. Comparison of inversion results with core data indicates the method yields both a reasonable volume heterogeneity size distribution and a good estimate of the sub-bottom depths at which scatterers occur.
Binocular rivalry waves in a directionally selective neural field model
Carroll, Samuel R.; Bressloff, Paul C.
2014-10-01
We extend a neural field model of binocular rivalry waves in the visual cortex to incorporate direction selectivity of moving stimuli. For each eye, we consider a one-dimensional network of neurons that respond maximally to a fixed orientation and speed of a grating stimulus. Recurrent connections within each one-dimensional network are taken to be excitatory and asymmetric, where the asymmetry captures the direction and speed of the moving stimuli. Connections between the two networks are taken to be inhibitory (cross-inhibition). As per previous studies, we incorporate slow adaption as a symmetry breaking mechanism that allows waves to propagate. We derive an analytical expression for traveling wave solutions of the neural field equations, as well as an implicit equation for the wave speed as a function of neurophysiological parameters, and analyze their stability. Most importantly, we show that propagation of traveling waves is faster in the direction of stimulus motion than against it, which is in agreement with previous experimental and computational studies.
Modeling neuron selectivity over simple midlevel features for image classification.
Shu Kong; Zhuolin Jiang; Qiang Yang
2015-08-01
We now know that good mid-level features can greatly enhance the performance of image classification, but how to efficiently learn the image features is still an open question. In this paper, we present an efficient unsupervised midlevel feature learning approach (MidFea), which only involves simple operations, such as k-means clustering, convolution, pooling, vector quantization, and random projection. We show this simple feature can also achieve good performance in traditional classification task. To further boost the performance, we model the neuron selectivity (NS) principle by building an additional layer over the midlevel features prior to the classifier. The NS-layer learns category-specific neurons in a supervised manner with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. Through extensive experiments, we demonstrate that this higher level NS-layer notably improves the classification accuracy with our simple MidFea, achieving comparable performances for face recognition, gender classification, age estimation, and object categorization. In particular, our approach runs faster in inference by an order of magnitude than sparse coding-based feature learning methods. As a conclusion, we argue that not only do carefully learned features (MidFea) bring improved performance, but also a sophisticated mechanism (NS-layer) at higher level boosts the performance further.
Zhou, Gongbo; Wang, Houlian; Zhu, Zhencai; Huang, Linghua; Li, Wei
2015-01-01
Harvesting the energy contained in the running environment of rotating machinery would be a good way to supplement energy to the wireless sensor. In this paper, we take piezoelectric bimorph cantilever beam with parallel connection mode as energy collector and analyze the factors which can influence the generation performance. First, a modal response theory model is built. Second, the static analysis, modal analysis, and piezoelectric harmonic response analysis of the wind-induced piezoelectr...
Institute of Scientific and Technical Information of China (English)
WU Bo; CHEN Gang; JIANG Zhengfeng; ZHENG Junyi
2006-01-01
Approximate calculation methods of prevention maintenance period under the random distribution are given, and three kinds of approximate calculation models of prevention maintenance period based on different security demands are come up with according to maintenance problems of machinery systems in modern enterprise and starting with different demands of systems. And then, how to make certain the best maintenance period by using the approximate calculation methods is illustrated by an example.
Institute of Scientific and Technical Information of China (English)
Zheng-yan Lin; Yu-ze Yuan
2012-01-01
Semiparametric models with diverging number of predictors arise in many contemporary scientific areas. Variable selection for these models consists of two components: model selection for non-parametric components and selection of significant variables for the parametric portion.In this paper,we consider a variable selection procedure by combining basis function approximation with SCAD penalty.The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components.With appropriate selection of tuning parameters,we establish the consistency and sparseness of this procedure.
Baudry, Jean-Patrick
2012-01-01
The Integrated Completed Likelihood (ICL) criterion has been proposed by Biernacki et al. (2000) in the model-based clustering framework to select a relevant number of classes and has been used by statisticians in various application areas. A theoretical study of this criterion is proposed. A contrast related to the clustering objective is introduced: the conditional classification likelihood. This yields an estimator and a model selection criteria class. The properties of these new procedures are studied and ICL is proved to be an approximation of one of these criteria. We oppose these results to the current leading point of view about ICL, that it would not be consistent. Moreover these results give insights into the class notion underlying ICL and feed a reflection on the class notion in clustering. General results on penalized minimum contrast criteria and on mixture models are derived, which are interesting in their own right.
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.