Minimal Set Of Variables Research Articles

Theory of similarity and ultrasonic testing

Application of similarity theory (dimensional analysis) to ultrasonic (UT) testing is discussed using as an illustration the example of concrete blocks. The concept of dimensional analysis makes it possible to represent a physical system via a minimal set of variables, the so-called π groups, which are the dimensionless quantities. These dimensionless parameters, characterizing the respective phenomenon, should have approximately equal values for the real object and for the model. Small concrete samples have been tested in the lab conditions using various UT probes and techniques. Two dimensionless criteria, valid and similar for the real field case and for the laboratory samples, have been introduced: total attenuations of UT waves and ratios of void size to the UT wavelength. To get total attenuations in the field conditions for thick concrete block, similar to the total attenuations measured in lab experiments on small samples, one should apply the low-frequency UT waves 60-100kHz. However, these UT waves will be able to reliably detect only the large inhomogeneities (e.g. voids), whose size is about half of the used UT wave, i.e. inhomogeneities with dimensions larger than 20-40mm.

Bounding the family-wise error rate in local causal discovery using Rademacher averages

Many algorithms have been proposed to learn local graphical structures around target variables of interest from observational data, focusing on two sets of variables. The first one, called Parent–Children (PC) set, contains all the variables that are direct causes or consequences of the target while the second one, known as Markov boundary (MB), is the minimal set of variables with optimal prediction performances of the target. In this paper we introduce two novel algorithms for the PC and MB discovery tasks with rigorous guarantees on the Family-Wise Error Rate (FWER), that is, the probability of reporting any false positive in output. Our algorithms use Rademacher averages, a key concept from statistical learning theory, to properly account for the multiple-hypothesis testing problem arising in such tasks. Our evaluation on simulated data shows that our algorithms properly control for the FWER, while widely used algorithms do not provide guarantees on false discoveries even when correcting for multiple-hypothesis testing. Our experiments also show that our algorithms identify meaningful relations in real-world data.

Defining entrepreneurial success to improve guidance services: a study with a comprehensive database from Andalusia

The aim of this paper is to assess the level of success achieved by entrepreneurs. The concept of success has many subjective facets, and it needs to be evaluated to reach other higher objectives, such as improving support systems for entrepreneurs. The usual pre-existing focuses for the evaluation of business performance are analysed and adapted. Based on a real case, the most relevant variables for detecting success are studied and an algorithmic process (based on decision trees) is established to ascertain whether an entrepreneur has achieved success. The data refers to entrepreneurs from Andalusia, the European region with the highest unemployment rates and where support for entrepreneurship is on the agenda of all political parties. The model specifies a minimal set of variables to evaluate success in each case. Subsequently, a simple set of 29 questions is also offered, serving to classify most entrepreneurs (over 98% of 2221 individuals in the case analysed) by their level of success. An objective procedure to measure the success of entrepreneurs is given. Such method is based on artificial intelligence and on three focuses: positioning, expectations and evolution. Both the variables used in this case and the 29 questions necessary to classify the entrepreneurs by their level of success are explicitly provided.

Stator Blades Manufacturing Geometrical Variability in Axial Compressors and Impact on the Aeroelastic Excitation Forces

AbstractThe manufacturing geometrical variability is a source of uncertainty, which cannot be avoided in the realization of machinery components. Deviations of a part geometry from its nominal design are inevitably present due to the manufacturing process. In the case of the aeroelastic forced response problem within axial compressors, these uncertainties may affect the vibration characteristics. For this reason, the impact of geometrical uncertainties due to the manufacturing process onto the modal forcing of axial compressor blades is investigated in this study. The research focuses on the vibrational behavior of an axial compressor rotor blisk. In particular, the amplitude of the forces acting as a source of excitation on the vibrating blades is studied. The geometrical variability of the upstream stator is investigated as input uncertainty. The variability is modeled starting from a series of optical surface scans. A stochastic model is created to represent the measured manufacturing geometrical deviations from the nominal model. A data reduction methodology is proposed in order to represent the uncertainty with a minimal set of variables. The manufacturing geometrical variability model allows to represent the input uncertainty and probabilistically evaluate its impact on the aeroelastic problem. An uncertainty quantification is performed in order to evaluate the resulting variability on the modal forcing acting on the vibrating rotor blades. Of particular interest is the possible rise of low engine orders due to the mistuned flow field along the annulus. A reconstruction algorithm allows the representation of the variability during one rotor revolution. The uncertainty on low harmonics of the modal rotor forcing can be therefore identified and quantified.

Commercial Classification and Location Modelling: Integrating Different Perspectives on Commercial Location and Structure

Commercial classification is essential to describe and compare the spatial patterns of commercial activity. Most classification systems consider a large set of dimensions that include detailed features such as store ownership or development type. Since new business models are continually being developed, the need to revise classification systems is constant. This makes generalisation hard, thus hindering the comparison of commercial structures in different places and periods. Recent studies have focused on cluster analysis and a smaller number of variables to gain insights into commercial structures, directly addressing this issue. Systematic bottom-up classification generates comparable structures, which is essential to contrast policy results in different situations. Furthermore, since form or accessibility are usually considered in classifications, cluster membership is precluded from most retail location models, often relying on the latter as an explanatory variable. Hence, a new classification system is proposed, based on cluster analysis (k-means) and a minimal set of variables: density, diversity, and clustering. This classification was implemented in 1995, 2002, and 2010 in Lisbon. Cross-sectional analysis of the commercial structures shows the system accurately describes commercial location and change, suggesting it can be generalised as a classification system. Since the minimal dataset also allows for cluster membership to be used on location models, the relationship between commercial classification and location modelling could be strengthened, reinforcing the role of commercial studies in urban planning and policymaking.

Which variables are essential for renewable energies?

ABSTRACTEssential Variables are defined as a minimal set of variables that explain the state of the system. They are crucial for predicting its developments, and support metrics that measure its evolution. The variables should be relevant to meet requirements of stakeholders and be technically and economically feasible for systematic observation. A definition of Essential Renewable Energies Variables is proposed linked with their identification in several domains in renewable energy using a bottom-up and user-driven approach, and spanning over several years of documented interaction with stakeholders. Lists of variables are proposed in hydropower, solar, wind, and marine energies. It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm.

Comparison of Detailed and Simplified Models of Human Atrial Myocytes to Recapitulate Patient Specific Properties.

Computer studies are often used to study mechanisms of cardiac arrhythmias, including atrial fibrillation (AF). A crucial component in these studies is the electrophysiological model that describes the membrane potential of myocytes. The models vary from detailed, describing numerous ion channels, to simplified, grouping ionic channels into a minimal set of variables. The parameters of these models, however, are determined across different experiments in varied species. Furthermore, a single set of parameters may not describe variations across patients, and models have rarely been shown to recapitulate critical features of AF in a given patient. In this study we develop physiologically accurate computational human atrial models by fitting parameters of a detailed and of a simplified model to clinical data for five patients undergoing ablation therapy. Parameters were simultaneously fitted to action potential (AP) morphology, action potential duration (APD) restitution and conduction velocity (CV) restitution curves in these patients. For both models, our fitting procedure generated parameter sets that accurately reproduced clinical data, but differed markedly from published sets and between patients, emphasizing the need for patient-specific adjustment. Both models produced two-dimensional spiral wave dynamics for that were similar for each patient. These results show that simplified, computationally efficient models are an attractive choice for simulations of human atrial electrophysiology in spatially extended domains. This study motivates the development and validation of patient-specific model-based mechanistic studies to target therapy.

Towards the automated analysis and database development of defibrillator data from cardiac arrest.

Background. During resuscitation of cardiac arrest victims a variety of information in electronic format is recorded as part of the documentation of the patient care contact and in order to be provided for case review for quality improvement. Such review requires considerable effort and resources. There is also the problem of interobserver effects. Objective. We show that it is possible to efficiently analyze resuscitation episodes automatically using a minimal set of the available information. Methods and Results. A minimal set of variables is defined which describe therapeutic events (compression sequences and defibrillations) and corresponding patient response events (annotated rhythm transitions). From this a state sequence representation of the resuscitation episode is constructed and an algorithm is developed for reasoning with this representation and extract review variables automatically. As a case study, the method is applied to the data abstraction process used in the King County EMS. The automatically generated variables are compared to the original ones with accuracies ≥90% for 18 variables and ≥85% for the remaining four variables. Conclusions. It is possible to use the information present in the CPR process data recorded by the AED along with rhythm and chest compression annotations to automate the episode review.

A New Severity of Illness Scale Using a Subset of Acute Physiology and Chronic Health Evaluation Data Elements Shows Comparable Predictive Accuracy*

Severity of illness scores have gained considerable interest for their use in predicting outcomes such as mortality and length of stay. The most sophisticated scoring systems require the collection of numerous physiologic measurements, making their use in real-time difficult. A severity of illness score based on a few parameters that can be captured electronically would be of great benefit. Using a machine-learning technique known as particle swarm optimization, we attempted to reduce the number of physiologic parameters collected in the Acute Physiology, Age, and Chronic Health Evaluation IV system without losing predictive accuracy. Retrospective cohort study of ICU admissions from 2007 to 2011. Eighty-six ICUs at 49 U.S. hospitals where an Acute Physiology, Age, and Chronic Health Evaluation IV system had been installed. 81,087 admissions, of which 72,474 did not have any missing values. None. Machine-learning algorithms were used to come up with the minimal set of variables that were capable of yielding an accurate severity of illness score: the Oxford Acute Severity of Illness Score. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score were developed on admissions during 2007-2009 and validated on admissions during 2010-2011. The most parsimonious Oxford Acute Severity of Illness Score consisted of seven physiologic measurements, elective surgery, age, and prior length of stay. Predictive models of ICU mortality using Oxford Acute Severity of Illness Score achieved an area under the receiver operating characteristic curve of 0.88 and calibrated well. A reduced severity of illness score had discrimination and calibration equivalent to more complex existing models. This was accomplished in large part using machine-learning algorithms, which can effectively account for the nonlinear associations between physiologic parameters and outcome.

An investigation into the use of MMCTP to tune accelerator source parameters and testing its clinical application

This paper presents an alternative method to tune Monte Carlo electron beam parameters to match measured data using a minimal set of variables in order to reduce the model setup time prior to clinical implementation of the model. Monte Carlo calculations provide the possibility of a powerful treatment planning verification technique. The nonstandardized and nonautomated process of tuning the required accelerator model is one of the reasons for delays in the clinical implementation of Monte Carlo techniques. This work aims to establish and verify an alternative tuning method that can be carried out in a minimal amount of time, allowing it to be easily implemented in a clinical setting by personnel with minimal experience with Monte Carlo methods. This tuned model can then be incorporated into the MMCTP system to allow the system to be used as a second dose calculation check for IMRT plans. The technique proposed was used to establish the primary electron beam parameters for accelerator models for the Varian Clinac 2100 6 MV photon beam using the BEAMnrc Monte Carlo system. The method is intended to provide a clear, direct, and efficient process for tuning an accelerator model using readily available clinical quality assurance data. The tuning provides a refined model, which agrees with measured dose profile curves within 1.5% outside the penumbra or 3 mm in the penumbra, for square fields with sides of 3 cm up to 30 cm. These models can then be employed as the basis for Monte Carlo recalculations of dose distributions, using the MMCTP system, for clinical treatment plans, providing an invaluable assessment tool. This was tested on six IMRT plans and compared to the measurements performed for the pretreatment QA process. These Monte Carlo values for the average dose to the chamber volume agreed with measurements to within 0.6%.PACS number: 87.55.km

FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach

BackgroundThe interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.ResultsWe propose a new and fast Markov Blanket-based method, FEPI-MB (Fast EPistatic Interactions detection using Markov Blanket), for epistatic interactions detection. The Markov Blanket is a minimal set of variables that can completely shield the target variable from all other variables. Learning of Markov blankets can be used to detect epistatic interactions by a heuristic search for a minimal set of SNPs, which may cause the disease. Experimental results on both simulated data sets and a real data set demonstrate that FEPI-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.ConclusionsFEPI-MB algorithm outperforms other computational methods for detection of epistatic interactions in terms of both the power and sample-efficiency. Moreover, compared to other Markov Blanket learning methods, FEPI-MB is more time-efficient and achieves a better performance.

Guidelines for assessment of bone microstructure in rodents using micro–computed tomography

Use of high-resolution micro-computed tomography (microCT) imaging to assess trabecular and cortical bone morphology has grown immensely. There are several commercially available microCT systems, each with different approaches to image acquisition, evaluation, and reporting of outcomes. This lack of consistency makes it difficult to interpret reported results and to compare findings across different studies. This article addresses this critical need for standardized terminology and consistent reporting of parameters related to image acquisition and analysis, and key outcome assessments, particularly with respect to ex vivo analysis of rodent specimens. Thus the guidelines herein provide recommendations regarding (1) standardized terminology and units, (2) information to be included in describing the methods for a given experiment, and (3) a minimal set of outcome variables that should be reported. Whereas the specific research objective will determine the experimental design, these guidelines are intended to ensure accurate and consistent reporting of microCT-derived bone morphometry and density measurements. In particular, the methods section for papers that present microCT-based outcomes must include details of the following scan aspects: (1) image acquisition, including the scanning medium, X-ray tube potential, and voxel size, as well as clear descriptions of the size and location of the volume of interest and the method used to delineate trabecular and cortical bone regions, and (2) image processing, including the algorithms used for image filtration and the approach used for image segmentation. Morphometric analyses should be based on 3D algorithms that do not rely on assumptions about the underlying structure whenever possible. When reporting microCT results, the minimal set of variables that should be used to describe trabecular bone morphometry includes bone volume fraction and trabecular number, thickness, and separation. The minimal set of variables that should be used to describe cortical bone morphometry includes total cross-sectional area, cortical bone area, cortical bone area fraction, and cortical thickness. Other variables also may be appropriate depending on the research question and technical quality of the scan. Standard nomenclature, outlined in this article, should be followed for reporting of results.

The Role of Financial Variables in Predicting Economic Activity in the Euro Area

The U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of variables as well as growth in the Rest of the World (an aggregation of seven small countries) and selected combinations of financial variables. Impulse responses (in-sample) show that shocks to financial variables influence real activity. However, according to out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric, this macro-financial linkage would be weak: financial indicators do not improve short and medium term forecasts of real activity in the euro area, even when their timely availability, relative to GDP, is exploited. This result is partly due to the 'average' nature of the RMSE metric: when forecasting ability is assessed as if in real time (conditionally on the information available at the time of the forecast), we find that models using financial variables would have been preferred, ex ante, in several episodes, in particular between 1999 and 2002. This result suggests that one should not discard, on the basis of RMSE statistics, the use of predictive models that include financial variables if there is a theoretical prior that a financial shock is affecting growth.

The Role of Financial Variables in Predicting Economic Activity in the Euro Area

The U.S. business cycle typically leads the European cycle by a few quarters and this can be used to forecast euro area GDP. We investigate whether financial variables carry additional information. We use vector autoregressions (VARs) which include the U.S. and the euro area GDPs as a minimal set of variables as well as growth in the Rest of the World (an aggregation of seven small countries) and selected combinations of financial variables. Impulse responses (in-sample) show that shocks to financial variables influence real activity. However, according to out-of-sample forecast exercises using the Root Mean Square Error (RMSE) metric, this macro-financial linkage would be weak: financial indicators do not improve short and medium term forecasts of real activity in the euro area, even when their timely availability, relative to GDP, is exploited. This result is partly due to the 'average' nature of the RMSE metric: when forecasting ability is assessed as if in real time (conditionally on the information available at the time of the forecast), we find that models using financial variables would have been preferred, ex ante, in several episodes, in particular between 1999 and 2002. This result suggests that one should not discard, on the basis of RMSE statistics, the use of predictive models that include financial variables if there is a theoretical prior that a financial shock is affecting growth.

A computational study of synaptic mechanisms of partial memory transfer in cerebellar vestibulo-ocular-reflex learning

There is a debate regarding whether motor memory is stored in the cerebellar cortex, or the cerebellar nuclei, or both. Memory may be acquired in the cortex and then be transferred to the cerebellar nuclei. Based on a dynamical system modeling with a minimal set of variables, we theoretically investigated possible mechanisms of memory transfer and consolidation in the context of vestibulo-ocular reflex learning. We tested different plasticity rules for synapses in the cerebellar nuclei and took robustness of behavior against parameter variation as the criterion of plausibility of a model variant. In the most plausible scenarios, mossy-fiber nucleus-neuron synapses or Purkinje-cell nucleus-neuron synapses are plastic on a slow time scale and store permanent memory, whose content is passed from the cerebellar cortex storing transient memory. In these scenarios, synaptic strengths are potentiated when the mossy-fiber afferents to the nuclei are active during a pause in Purkinje-cell activities. Furthermore, assuming that mossy fibers create a limited variety of signals compared to parallel fibers, our model shows partial memory transfer from the cortex to the nuclei.

Lyapunov Exponents without Rescaling and Reorthogonalization

We present a new method for the computation of Lyapunov exponents utilizing representations of orthogonal matrices applied to decompositions of M or $M \~{M}$ where M is the tangent map. This method uses a minimal set of variables, does not require renormalization or reorthogonalization, can be used to efficiently compute partial Lyapunov spectra, and does not break down when the Lyapunov spectrum is degenerate.

ECONOMIC ACCOUNTS FOR DEVELOPING COUNTRIES

This paper has three parts. The first part attempts to analyse the prevailing situation in developing countries. The second part presents a feasible accounting system for such countries, by and large within the framework of the SNA. The third part goes beyond this and tries to obtain a more concise matrix (covering, in concept, those given in the accounting system) furnishing minimal sets of variables and classifications which one has, in any case, to play within the context of policy issues of developing countries. All three authors are jointly responsible for the first part. Uma Datta Roy Choudhury has drafted the second part. The third part has been drafted by the other two authors.

Topological considerations on minimal sets of variables describing an LCR network

In a linear resistive network, a maximal set, called a basis, of variables that are linearly independent with respect to coefficients that are rational functions in edge admittances is determined. It is shown that the basis is an important concept in linear network analysis in the sense that any network equation can uniquely and minimally be expressible in terms of the elements in a basis. In order to characterize the algebraic structure of the variables in connection with the basis, existence theorems on mappings from given variables to all the source currents and voltages are presented.