Heuristic Criteria Research Articles

Spatial distribution of non-linear interdependency measures for focal hemisphere identification in epileptic patients from interictal intracranial EEG

We study performance, stability and spatial distribution of three previously proposed [1,2] non-linear measures of interdependency between time series (named H, M and S) used to classify, from interictal recordings, the epileptogenic hemisphere in patients with drug-resistant mesial temporal lobe epilepsy (MTLE). Two electrodes penetrate the hippocampal region in the two hemispheres and include 10 recording contacts each. We consider only data recorded during interictal periods [3]. All measures are introduced through the reconstruction of a state-space for the recorded signals, using the widely adopted embedding approach, and quantify in different ways the average distance between neighbours in phase space for one signal and the distance between the corresponding equal-time partners in the other signal. One hemisphere is classified as focal if the average between-contacts interdependency value is significantly greater than the one for the other hemisphere. We investigate the dependence of the interdependency measures and associated performances on the inter-electrode distance, on the relevant parameters and non-stationarities across interictal periods. Two of the three measures (H and M) provide good and similar classification performances, as well as similar spatial distributions. For M, ten cases are correctly classified, one case is incorrectly classified and for four cases, M values are statistically indistinguishable for the two hemispheres. For the correctly classified cases, M shows long-range between-contacts interdependencies for the focal hemisphere (see Figure 1). We also show how interdependencies vary inside one interictal period and between different interictal periods. The role of the parameters entering the analysis is systematically studied to provide heuristic criteria for their choice. Two of the studied nonlinear measures are found to be adequate for the classification of the focal hemisphere. The observed long-range interdependency for focal hemispheres is consistent with the expected propensity of pathological nervous tissue to be entrained in paroxysmal synchronous activity. We suggest that the observed non-stationarity of interdependencies across interictal periods could be used to improve the classification performance.

Molecular Dynamics Study of a Polymeric Reverse Osmosis Membrane

Molecular dynamics (MD) simulations are used to investigate the properties of an atomic model of an aromatic polyamide reverse osmosis membrane. The monomers forming the polymeric membrane are cross-linked progressively on the basis of a heuristic distance criterion during MD simulations until the system interconnectivity reaches completion. Equilibrium MD simulations of the hydrated membrane are then used to determine the density and diffusivity of water within the membrane. Given a 3 MPa pressure differential and a 0.125 microm width membrane, the simulated water flux is calculated to be 1.4x10(-6) m/s, which is in fair agreement with an experimental flux measurement of 7.7x10(-6) m/s.

Interpolation among reduced‐order matrices to obtain parameterized models for design, optimization and probabilistic analysis

AbstractModel reduction has significant potential in design, optimization and probabilistic analysis applications, but including the parameter dependence in the reduced‐order model (ROM) remains challenging. In this work, interpolation among reduced‐order matrices is proposed as a means to obtain parameterized ROMs. These ROMs are fast to evaluate and solve, and can be constructed without reference to the original full‐order model. Spline interpolation of the reduced‐order system matrices in the original space and in the space tangent to the Riemannian manifold is compared with Kriging interpolation of the predicted outputs. A heuristic criterion to select the most appropriate interpolation space is proposed. The interpolation approach is applied to a steady‐state thermal design problem and probabilistic analysis via Monte Carlo simulation of an unsteady contaminant transport problem. Copyright © 2009 John Wiley & Sons, Ltd.

A literature survey on indicators for characterisation and optimisation of SAR distributions in deep hyperthermia, a plea for standardisation

Purpose: To evaluate the predictive value of SAR indicators by assessing the correlation of a SAR indicator with the corresponding predicted temperature. Ultimately, this should lead to a number of verified SAR indicators for characterization and optimization of a predicted SAR distribution.Methods: A literature survey is followed by an evaluation of the SAR indicators on their functionality, using a set of heuristic classification criteria. To obtain an objective assessment of the predictive value for SAR characterisation, all SAR indicators are evaluated by correlating the value of the SAR indicator to the predicted target temperature when heated with the BSD2000 Sigma 60 applicator. Two methods were followed. First, the specificity of the SAR indicator to target temperature was assessed for each of the 36 patient-specific models, using 30 randomly chosen phase and amplitude settings. Secondly, each SAR indicator was used as a goal function to assess its suitability for optimisation purposes.Results: Only a selected number of SAR indicators correlate well with tumour/target-temperature. Hence, for target-related properties, an adequate set of SAR indicators is found in the literature. For hotspots, modifications are desirable. For optimisation purposes, improved objective functions have been defined.Conclusions: From the correlation of the SAR indicators with tumour temperature, a preferred set of SAR indicators is derived:For target heating, ‘average SAR ratio’, ‘Hotspot-target SAR ratio’, and ‘homogeneity coefficient’ provide suitable objective criteria, while for hotspot reduction, ‘Hotspot-target SAR ratio’ is considered the most useful indicator.For optimisation procedures, ‘Hotspot-target SAR ratio’ is currently the most suitable objective function.

Annual runoff regional frequency analysis in Sicily

This paper performs annual runoff frequency analysis in Sicily, Italy using the index runoff method. Homogeneous regions, not necessarily defined by geographical boundaries but sharing common features from a morphologic and climatic standpoint, have been identified. For each region a single probability distribution function can be fitted to the available annual runoff data scaled by the index runoff.Starting from an initial dataset of 105 hydrometric stations, 57 stations have been selected using heuristic criteria associated with the Hosking and Wallis’s discordancy measure. The Mantel tests have been then applied to find out the physical and morphological parameters best correlated with the runoff using the distances matrix approach. These parameters have been successively used in the cluster analysis to delineate homogeneous regions and the Hosking and Wallis’s heterogeneity test has been applied to the clusters in order to verify their homogeneity.Further analysis has been carried out to test the goodness-of-fit of four different candidate distributions of the scaled annual runoff using the chi-square test. For all the identified homogeneous regions the three-parameters lognormal distribution has been proved the most suitable distribution for the dimensionless annual runoff.In order to predict the runoff quantiles at ungauged sites, the procedure finally envisages the estimation of mean annual runoff (index runoff) by multiple regression analysis, providing expressions that relate mean annual runoff to some climatic and morphologic features of the basins.

Sensor Systems for Monitoring Functional Status in Assisted Living Facility Residents

This article provides results of an expert review of data displays for a sensor system used to monitor functional abilities in older adults. The research took place at TigerPlace, an assisted living facility where the sensor system is currently being evaluated. A checklist of 16 heuristic criteria was used to evaluate the sensor data displays, with consideration to the users of the system: residents, their families, and health care providers. Results of this expert review indicate that flexibility and efficiency of use, help and documentation, navigation, and skills were not well developed in the sensor data displays. Conversely, sensor data displays were rated highly for their aesthetic value and the ample visual contrast on the main display components. Through the use of a sensor system, new ways of detecting functional decline in elderly residents of assisted living facilities can be accomplished.

Patterns of memory: a normative taxonomy of the Wide Range Assessment of Memory and Learning-Second Edition (WRAML-2).

Memory is arguably the most important function of cognition. When left undetected, memory impairments are linked to life long underachievement and negative social consequences. Given that the construct of memory is multidimensional, the current study examined patterns of multiple indicators associated with memory across individuals ranging in age from 5 to 85 years who had been administered the Wide Range Assessment of Memory and Learning-Second Edition (WRAML-2). Multistage cluster analysis with independent age replications was used to empirically identify normative profiles in a sample of (n = 1172) typically developing individuals. This procedure considered how various indicators of memory operate in concert by accounting for the nonlinear multivariate relationships among them. Results supported nine common (or core) profile types that satisfied all formal heuristic and statistical criteria, including complete coverage, satisfactory within-type homogeneity, between-type dissimilarity, and replicability. A summary of the defining characteristics for each profile is provided.

Joint Receive Antenna Selection for Multi-User MIMO Systems with Vector Precoding

Vector precoding is a nonlinear broadcast precoding scheme in the downlink of multi-user MIMO systems which outperforms linear precoding and THP (Tomlinson-Harashima Precoding). This letter discusses the problem of joint receive antenna selection in the multi-user MIMO downlink with vector precoding. Based on random matrix analysis, we derive a simple heuristic selection criterion using singular value decomposition (SVD) and carry out an exhaustive search to determine for each user which receive antenna should be used. Simulation results reveal that receive antenna selection using our proposed criterion obtains the same diversity order as the optimal selection criterion.

Over the Long-Run? Short-Run Impact and Long-Run Consequences of Stakeholder Management

The stakeholder view of the firm has been justified under instrumental and normative bases. Whereas the instrumental basis argues that “enlightened stakeholder management” is a necessary precondition to seek shareholders’ value maximization, the normative basis relies on the observance of ethical norms by managers and the notion that the stakeholders should be treated as “ends.” Some scholars argue that both views actually converge. However, this article provides empirical evidence of the negative effects of stakeholder management in shareholders’ value in the short run and the positive effects over the long run, using a longitudinal database of 658 U.S. firms. Given the difficulties of anticipating the instrumental long-term financial effects of short-run decisions affecting the different stakeholders, the authors’ findings support the view of the normative basis for stakeholder theory based on ethics, norms, and heuristic criteria as a way to solve conflicts among the claims of different stakeholders.

Optimization of a Two Degree of Freedom System Acting as a Dynamic Vibration Absorber

This paper deals with the problem of finding the optimal stiffnesses and damping coefficients of a two degree of freedom (2DOF) system acting as a dynamic vibration absorber (DVA) on a beam structure. In this sense, a heuristic criterion for the optimization problem will be developed to contemplate this particular type of DVA. Accordingly, it is planned to minimize the amplitude of vibration in predetermined points of the main structure. Two optimizations will be proposed for two DVAs of 1DOF to compare their performances with the optimized 2DOF system. A simulated annealing algorithm is used to obtain the DVA’s optimal parameters for minimum amplitude in a given point of the beam. The best configuration depends on the location of the absorbers on the beam and, for a fixed location, on the distribution of the stiffness constants.

Balancing Accuracy and Meaning in Common-Pool Resource Theory

Common-pool resources are managed in complex environments that are amenable to understanding, analysis, and management at multiple levels. This paper develops a heuristic criterion to identify the costs and benefits of adopting various levels of analysis when constructing theory for common- pool resource management. It argues that there is no single optimal level for such analysis. Instead, a trade- off is posed where theories at higher levels tend to be more accurate but less meaningful than theories at lower levels.

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.

Adaptive finite element methods for Cahn–Hilliard equations

We develop a method for adaptive mesh refinement for steady state problems that arise in the numerical solution of Cahn–Hilliard equations with an obstacle free energy. The problem is discretized in time by the backward-Euler method and in space by linear finite elements. The adaptive mesh refinement is performed using residual based a posteriori estimates; the time step is adapted using a heuristic criterion. We describe the space–time adaptive algorithm and present numerical experiments in two and three space dimensions that demonstrate the usefulness of our approach.

Turing Pattern Formation with Two Kinds of Cells and a Diffusive Chemical

We study a three-variable Turing system with two kinds of cells and a diffusive chemical, considering the formation and maintenance of fish skin patterns with multiple pigment cells. The two types of cells are produced from undifferentiated cells. They inhibit the supply rate of the other cell type, forming local clusters of the same cell type. In addition, the cells of one type can be maintained only in the presence of a diffusive chemical produced by the other cell type, resulting in the coexistence of two cell types in heterogeneous spatial patterns. We assume linear interaction among cells and the chemical, and cell supply rates constrained to be positive or zero. We derive the condition for diffusion-driven instability. In one-dimensional model, we examine how the wavelength of the periodic pattern depends on parameters. In the two-dimensional model, we study the condition for spot, stripe or reversed spot pattern to emerge (pattern selection). We discuss heuristic criteria for the pattern selection.

User-Oriented Feature Selection for Machine Learning

The effectiveness of any machine learning algorithm depends, to a large extent, on the selection of a good subset of features or attributes. Most existing methods use the syntactic or statistical information of the data, relying on a heuristic criterion to select features. In this paper, we investigate an alternative less-studied approach called user-oriented feature selection by exploiting the domain-specific semantic information. Given any two features, a user is able to express which one is more important based on the semantic consideration. Such user requirements are formally described by a preference relation on the set of features. Algorithms are proposed to construct a subset of features that is most consistent with the user requirements. Their properties and computational complexity are analysed. User-oriented feature selection offers a new view for machine learning and its potentials need to be further investigated and explored.

A Heuristic Algorithm for Reliability Modeling and Analysis of Grid Systems

Grid computing focuses on large-scale resource sharing. Using a general reliability model for grid computing to relax some impractical assumptions, a heuristic algorithm is presented to evaluate grid program/service reliability. The heuristic algorithm is based on two heuristic criteria that determine the significance of an entity and prune those insignificant ones. Through algorithm analysis, the heuristic algorithm is shown to have a linear complexity. This is much better than the previous algorithms, which are of exponential complexity. Another advantage of the heuristic algorithm is that the running time is controllable by adjusting the parameter of significant level (SL) and significant rate. A regression method is proposed to adjust the SL and predict the running time. Two examples are given

Explanation and comparative dynamics in social science

A static explanation argues that something is so because something else is so. For example, an explanation of a state of affairs in terms of an equilibrium is static. A dynamic explanation, on the other hand, argues the something is so because it has become so. The physicist, for example, views the velocity of a falling stone as a result of force having acted (not merely now acting) on the stone. In this article, the author points out the excessive reliance of many social sciences upon static explanations of social arrangements (equilibria, adjustment, etc.) and the comparative neglect of dynamic (historical) analysis. He also develops heuristic criteria for judging the explanatory value of various social variables in dynamic analysis.

Site selecting algorithms for Nodes B

We present two UMTS site selecting algorithms. The algorithm takes into account propagation predictions as well as the traffic distribution in the target area, considers multiple service classes, transmit power constraints and quality requirements, both in uplink and downlink. For a traffic distribution sample, the proposed algorithms (Heuristic Backtracking Algorithm (HBA) and Genetic Algorithm (GA)) find the minimum number of Nodes B needed to provide the demanded capacity or a low number of nodes according to heuristic criteria.

CODELCO, Chile Programs Its Copper-Smelting Operations

CODELCO’s Chuquicamata Copper Smelter, located in Chile, must meet an environmental standard established in 2002 that limits the sulfur emissions from the copper-production process. This regulation placed new restrictions on the production process, and consequently the heuristic criteria based on years of operator experience no longer resulted in high production levels. We developed a model to maximize production while respecting metallurgic, operational, environmental, and other restrictions. We created two methods of planning daily activities for the plant. We developed a client-server tool that displays optimization results graphically and numerically based on data on operational conditions recorded in real time and data input manually. The smelter has used the system since March 2003. We found an increase in the quantity of copper concentrate processed of 25.4 percent.

Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Abstract Key variable identification for classifications is related to many trouble-shooting problems in process industries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in application for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diagnosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee Eastman process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.