Arbitrary Threshold Value Research Articles

An algorithm for the domination number and neighbor-component order connectivity of a unicycle

The graph parameter domination is considered perhaps one of the fastest-growing areas in graph theory since the fundamental efforts of Berge and Ore in 1962. The domination number of a graph may be determined using the parameter neighbor-component order connectivity which is defined as the minimum number of nodes such that removing these nodes and their adjacent neighbors ensure all remaining components have order less than some given threshold value. Given a threshold value of one, the neighbor-component order connectivity of a graph is equivalent to the well-known parameter domination number of the graph. The problem of computing the domination number of an arbitrary graph is NP-hard, and computing neighbor-component order connectivity of a graph for an arbitrary threshold value is also NP-hard. We will look at the organizational structure of a unicycle graph which aids in modeling the vulnerability of a communication network. In order to assess the vulnerability of this network, we present a polynomial algorithm for computing the neighbor-component order connectivity of a unicycle. We also note that this algorithm covers the case of a threshold value of one and offers an algorithm for the domination number of the unicycle.

A linear algorithm for the neighbor-component order connectivity of arbitrary trees

The graph parameter neighbor-connectivity was first investigated by Gunther and Hartnell in 1987 and provides important information on how reliable a network can be when failures of a node may impact its neighbors. In this model, the failure of a node causes the deletion of its closed neighborhood, i.e., the node and its adjacent neighbors as well. The minimum number of closed neighborhoods whose removal results in an empty, complete, or disconnected subgraph is called the neighbor-connectivity of the graph. Since then component order connectivity models have emerged to address inadequacies in the traditional models of connectivity, namely, that in many real-world networks, when disconnecting a graph one may be left with components that are large enough to still be operable. By adapting neighbor-connectivity to a component order model, we introduced neighbor-component order connectivity, defined as the minimum number of closed neighborhoods that must be removed from a network to ensure all remaining components have order less than some given threshold value. Given a threshold value of one, the neighbor-component order connectivity of a graph is equivalent to the well-known parameter domination number of the graph. The problem of computing the domination number of an arbitrary graph is NP-hard, and computing neighbor-component order connectivity of a graph for an arbitrary threshold value is also NP-hard. Here, we present a linear-time algorithm for computing the neighbor-component order connectivity of an arbitrary tree for an arbitrary threshold value, thus generalizing the classic linear algorithm of Cockayne, Goodman, and Hedetniemi for the domination number of the tree.

Empirical Identification of Intra-Urban Subcentralities: A New Methodological Approach with an Application for a Developing Country

We present a new empirical approach for identifying sub-centers within urban areas and apply it to the São Paulo metropolitan area (SPMA). We use geographically weighted regressions (GWR) to overcome the limitations presented by previous methods, which rely on previous knowledge of the employment distribution and use arbitrary threshold values and band sizes. We find three SBD in 2002 and only two in 2014, suggesting that SPMA is polycentric but presents only one business core that concentrates more than 90% of all employees working in an SBD. We apply the widely recognized method of McMillen and Smith (2003) to our database and compare the results. Our method is more conservative in identifying areas as sub-centers (SBD) and presents lower standard errors.

Partitioning a Set of Vectors with Nonnegative Integer Coordinates Using Logical Hardware

This article considers problems of partition of a set of vectors with nonnegative integer coordinates into two classes with respect to a threshold value and a threshold relation using adaptable logical networks. The correctness of the corresponding algorithms for implementing such a partition is proved for an arbitrary threshold value and a dimension of vectors.

Definition of Extreme El Niño and Its Impact on Projected Increase in Extreme El Niño Frequency

AbstractDuring extreme El Niño events, the Intertropical Convergence Zone moves to the normally cold and dry east equatorial Pacific, resulting in a nonlinear rainfall increase with sea surface temperature in the region. An arbitrary threshold value of boreal winter total rainfall (e.g., 5 mm d−1) in the east equatorial Pacific was used by previous studies to capture this feature. Under greenhouse warming, the frequency of extreme El Niño events is projected to increase, so is the mean east equatorial Pacific rainfall. Is the projected frequency increase a consequence of the mean rainfall increase? We show that the projection is not significantly influenced by the increased mean rainfall. Instead, the increased frequency accounts for approximately 50% of the mean rainfall increase. Using upward atmospheric vertical velocity for defining extreme El Niño reaffirms the conclusion that the increased frequency results from increased probability of atmospheric deep convection, as the eastern equatorial Pacific warms faster than the surrounding regions.

CORR Insights®: Pathologically Benign Lymph Nodes Can Mimic Malignancy on Imaging in Patients With Angiomatoid Fibrous Histiocytoma.

CORR Insights®: Pathologically Benign Lymph Nodes Can Mimic Malignancy on Imaging in Patients With Angiomatoid Fibrous Histiocytoma.

Development and examination of new algorithms of traveltime detection in GPS/acoustic geodetic data for precise and automated analysis

A GPS/acoustic (GPS/A) geodetic observation technique allows us to determine far offshore plate motion in order to understand the mechanism of megathrust earthquakes. In this technique, the distance between a sea-surface platform and seafloor transponders is estimated using the two-way traveltimes (TWT) of acoustic signals. TWTs are determined by maximizing the cross-correlation coefficient between the transmitted and returned signals. However, this analysis caused significantly wrong detection of TWT when the correlogram has an enlarged secondary envelope due to the enlarged amplitude of multiple signals depending on the relative spatial geometry between the ship and the transponder. The handled manual rereading of thousands of correlograms to obtain correct TWTs needs enormous time, and human errors may cause. To prevent these difficulties, an automated TWT determination procedure is valid to process numerous GPS/A data efficiently not only without human errors but also with high precision. We developed automated methods for precisely analyzing GPS/A data. Method 1: The maximum peak in the observed correlogram is read, and a synthetic correlogram is then subtracted from the observation. Then, the same operation is applied to the subtracted waveform. This procedure is iterated until the correlation coefficient lowers than a pre-defined threshold. A true traveltime is defined as the fastest traveltime during the iterations. Method 2: The observed correlograms are divided into several groups based on their similarity through cluster analysis, and a master waveform in each group is selected. Then, the traveltime residual between the maximum and true peaks in the master waveform is manually evaluated. The obtained residual is employed as the correction value for each slave waveform. Further, we employed a seismic data projection to visually inspect the reliability of obtained results. We confirmed that both new methods accurately correct misreadings in the current method, which amount to 0.4–0.8 ms roughly corresponding to 30–60 cm difference in the slant range. Thus, the proposed algorithms significantly improve the estimation of the transponder location. Further analyses are required to determine the arbitrary threshold values and to construct fully automated algorithms.

Multiple straight-line fitting using a Bayes factor

This paper introduces a Bayesian approach to solve the problem of fitting multiple straight lines to a set of 2D points. Other approaches use many arbitrary parameters and threshold values, the proposed criterion uses only the parameters of the measurement errors. Models with multiple lines are useful in many applications, this paper analyzes the performance of the new approach to solve a classical problem in robotics: finding a map of lines from laser measurements. Tests show that the Bayesian approach obtains reliable models.

Original Methodology and Algorithm Able to Identify Visible Noisy in Images

Image statistical parameters are used in this paper for specific artifact detection and elimination. Based on an analytical representation of these parameters the artifact is defined as absolute range values of special statistical parameters.The left or right artifact position against the vertical border is classified when the detected rows number is over the count threshold and the proportion between rows number of the sides is over position threshold value. Because the detection is referred to the rows of image matrix it is necessary to check the artifact homogeneity from the rows congruency point of view. Basically, there is an arbitrary congruency threshold value under which the rows are classified as non artifact area.

Developing predictive systems models to address complexity and relevance for ecological risk assessment

Ecological risk assessments (ERAs) are not used as well as they could be in risk management. Part of the problem is that they often lack ecological relevance; that is, they fail to grasp necessary ecological complexities. Adding realism and complexity can be difficult and costly. We argue that predictive systems models (PSMs) can provide a way of capturing complexity and ecological relevance cost-effectively. However, addressing complexity and ecological relevance is only part of the problem. Ecological risk assessments often fail to meet the needs of risk managers by not providing assessments that relate to protection goals and by expressing risk in ratios that cannot be weighed against the costs of interventions. Once more, PSMs can be designed to provide outputs in terms of value-relevant effects that are modulated against exposure and that can provide a better basis for decision making than arbitrary ratios or threshold values. Recent developments in the modeling and its potential for implementation by risk assessors and risk managers are beginning to demonstrate how PSMs can be practically applied in risk assessment and the advantages that doing so could have.

Evaluating taxonomic adjacency as a source of soil map uncertainty

SummarySoil types or map units are considered to be taxonomically adjacent if they differ in only one criterion, defined by an arbitrary threshold value. By treating soil types as nodes of a graph and taxonomic adjacency as the graph edges connecting nodes, algebraic graph theory can be used to produce a measurement of the uncertainty in a soil map associated with arbitrary classification boundaries between soil types. The largest eigenvalue of the adjacency matrix of a graph, the spectral radius, is an indication of network complexity. A larger spectral radius indicates a more complex network, and a greater degree of uncertainty or potential error associated with taxonomic adjacency. Benchmark values of spectral radius for cases of no taxonomic adjacency, including a single pair of adjacent soils, a chain or cycle‐type graph structure and a fully connected graph, are established so that taxonomic adjacency indices based on the spectral radius can be established. Examples are shown from two contrasting USA soil landscapes in the Ouachita Mountains, Arkansas, and the coastal plain of North Carolina, using both US Soil Taxonomy and the world reference base. The taxonomic adjacency indices are also useful in assessing soil richness and pedodiversity, with smaller values indicating a greater likelihood that identified soils represent distinct entities.

2A1-N01 Study on Visual Feedback Control of Optogenetic Muscle-Powered Bioactuator(マイクロロボット・マイクロマシン)

In our study, we used the dorsal vessel (DV, heart-like tissue) of Drosophila melanogaster (Dmel, fruit fly) larva as a bioactuator. This paper reports on the closed loop visual feedback system to control the contraction rates of the DV in vivo. We investigated the response of the DVs that express channelrhodopsin(ChR2) and/or halorhodopsin(NpHR) to blue and red light. We found that DV expressing ChR2 increased contraction rates under blue light stimulation, while DV expressing NpHR stopped contracting under red light stimulation. In our feedback experiments, we managed to keep the contraction frequency of the DV in vivo above an arbitrary threshold value using the visual feedback system we developed.

Reducing code wastage in orthogonal variable spreading factor-based wideband code division multiple access networks

SUMMARY Most third-generation and beyond wideband code division multiple access networks use the orthogonal variable spreading factor code tree for channelization codes. The codes in this code tree are limited and the performance of a wireless network depends upon the code assignment for new calls. In this paper, we introduce a term called ‘wastage capacity’, which gives us the amount of wastage caused when a code (single or multiple) with a data rate higher than the rate of the incoming call is assigned to it. We suggest two methods to keep wastage capacity below an arbitrary threshold value or zero. In the first method, we devised an algorithm in which wastage up to a certain threshold would be tolerated and the minimum rakes to get this wastage capacity were identified. In the second approach, we reduced the wastage capacity to zero irrespective of the number of rakes at the expense of higher cost and complexity. Copyright © 2011 John Wiley & Sons, Ltd.

Probability, proof, and clinical significance

Statistical tests help distinguish true differences (associations) from chance and result in a P value which is an estimation of probability that the results are due to chance. An arbitrary test threshold value (eg, usually alpha = .05) is used to distinguish results that are assumed to be due to chance from the results that are due to other factors. The bottom line: If the probability that the results are due to chance is less than the threshold value (P < .05), it is assumed the differences are due to these other factors (eg, true differences in treatment effects). However, we may be wrong 5% of the time. Significance testing in itself does not take into account factors which may bias study results. The possible exception to this is multivariate analysis, which is a subject for future articles. Sample size and random variation play an important role in whether a result is statistically significant or not and, together with expected effect size, whether the study is “powered” to detect statistical differences. A statistically significant result does not “prove” anything and does not establish a causal relationship between the exposure and outcome. The finding of an “association” does not mean that the association is causal in most instances. This is a topic for future articles. Although a result may be statistically significant, the effect size (ie, magnitude of the effect) may not be biologically or clinically important. In critiquing, designing, and reporting studies, the minimal clinically important difference is important to consider. While statistical testing is an important part of research analysis, its limitations, uses, and misuses need to be considered in order to put results of a study in the proper context. In the next issue, we tackle the topic of confounding, an important source of potential bias which needs to be considered in all studies regardless of design.

Impact response of polymer‐coated grinding spheres

PurposeThe purpose of this paper is to evaluate the effect that polymer coat has on the impact behavior of grinding sphere and to find possible subsection of parameter space in which grinding sphere wear could be reduced.Design/methodology/approachNumerical analysis is based on axisymmetric finite elements that were developed using symbolic tool AceGen. Comparing stress response of elastic and visco‐elastic material revealed that for high strain rates observed in impacts both behave the same and that is why elastic elements were used in simulations.FindingsImpact velocity, coat thickness and polymer material properties were varied in a parametric case study of polymer‐coated sphere impact. Decrease of the pressure on the surface of grinding sphere indicates that polymer layer can be effective in reducing grinding media wear, but in order to maintain adequate impact pressure to do the grinding the impact velocity has to be increased. Both upper and lower limit for impact velocity were determined for some arbitrary pressure threshold values. This shows that combining measured threshold values of specific material with results from presented numerical tool could provide valuable guides for finding optimum stirred media milling operation parameters.Originality/valueIn this work, the authors develop numerical tools with the aim of supporting experimental development of polymer coat capable of reducing grinding media wear.

Circulating Progesterone Levels and Ongoing Pregnancy Rates in Controlled Ovarian Stimulation Cycles for In Vitro Fertilization: Analysis of Over 4000 Cycles

background: The influence of elevated serum progesterone levels during in vitro fertilization/intracytoplasmic sperm injection (IVF/ ICSI) cycles on pregnancy rates is a matter of continued debate among fertility clinicians. Efforts to resolve this question have been impeded by the various assays used to measure progesterone and the different, arbitrary threshold values for defining ‘high’ progesterone levels. methods: A non-interventional, retrospective, observational, single-centre cohort study evaluated the relationship between serum progesterone levels on the day of human chorionic gonadotrophin (hCG) administration and the ongoing pregnancy rate in 4032 patients undergoing IVF/ICSI cycles using gonadotrophin-releasing hormone (GnRH) analogues for pituitary down-regulation. results: Ongoing pregnancy rates were inversely associated with serum progesterone levels on the day of hCG (P , 0.001 for overall trend), irrespective of the GnRH analogue used for pituitary down-regulation. Patients with serum progesterone levels ≤1.5 ng/ml had significantly higher ongoing pregnancy rates than those with progesterone levels .1.5 ng/ml (31.0 versus 19.1%; P ¼ 0.00006); odds ratio, 0.53 (95% confidence interval, 0.38 –0.72). Multivariate regression analysis showed that daily follicle-stimulating hormone dose, number of oocytes and estradiol values on the day of hCG administration were positively associated with progesterone levels (P , 0.0001 for all). Serum progesterone levels were significantly greater in women treated with GnRH agonists (n ¼ 1177) versus antagonists (n ¼ 2855; 0.84+ 0.67 versus 0.75+ 0.66 ng/ml; P ¼ 0.0003). conclusions: Elevated serum progesterone levels on the day of hCG administration is associated with reduced ongoing pregnancy rates. In particular, serum progesterone levels of .1.5 ng/ml were associated with lower ongoing pregnancy rates following IVF/ICSI cycles irrespective of the GnRH analogue used for pituitary down-regulation.

Circulating progesterone levels and ongoing pregnancy rates in controlled ovarian stimulation cycles for in vitro fertilization: analysis of over 4000 cycles

The influence of elevated serum progesterone levels during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles on pregnancy rates is a matter of continued debate among fertility clinicians. Efforts to resolve this question have been impeded by the various assays used to measure progesterone and the different, arbitrary threshold values for defining 'high' progesterone levels. A non-interventional, retrospective, observational, single-centre cohort study evaluated the relationship between serum progesterone levels on the day of human chorionic gonadotrophin (hCG) administration and the ongoing pregnancy rate in 4032 patients undergoing IVF/ICSI cycles using gonadotrophin-releasing hormone (GnRH) analogues for pituitary down-regulation. Ongoing pregnancy rates were inversely associated with serum progesterone levels on the day of hCG (P < 0.001 for overall trend), irrespective of the GnRH analogue used for pituitary down-regulation. Patients with serum progesterone levels < or = 1.5 ng/ml had significantly higher ongoing pregnancy rates than those with progesterone levels >1.5 ng/ml (31.0 versus 19.1%; P = 0.00006); odds ratio, 0.53 (95% confidence interval, 0.38-0.72). Multivariate regression analysis showed that daily follicle-stimulating hormone dose, number of oocytes and estradiol values on the day of hCG administration were positively associated with progesterone levels (P < 0.0001 for all). Serum progesterone levels were significantly greater in women treated with GnRH agonists (n = 1177) versus antagonists (n = 2855; 0.84 +/- 0.67 versus 0.75 +/- 0.66 ng/ml; P = 0.0003). Elevated serum progesterone levels on the day of hCG administration is associated with reduced ongoing pregnancy rates. In particular, serum progesterone levels of >1.5 ng/ml were associated with lower ongoing pregnancy rates following IVF/ICSI cycles irrespective of the GnRH analogue used for pituitary down-regulation.

A Quantitative Investigation of the Acceptable Risk Levels of Object-Oriented Metrics in Open-Source Systems

Object-oriented metrics have been validated empirically as measures of design complexity. These metrics can be used to mitigate potential problems in the software complexity. However, there are few studies that were conducted to formulate the guidelines, represented as threshold values, to interpret the complexity of the software design using metrics. Classes can be clustered into low and high risk levels using threshold values. In this paper, we use a statistical model, derived from the logistic regression, to identify threshold values for the Chidamber and Kemerer (CK) metrics. The methodology is validated empirically on a large open-source system-the Eclipse project. The empirical results indicate that the CK metrics have threshold effects at various risk levels. We have validated the use of these thresholds on the next release of the Eclipse project-Version 2.1-using decision trees. In addition, the selected threshold values were more accurate than those were selected based on either intuitive perspectives or on data distribution parameters. Furthermore, the proposed model can be exploited to find the risk level for an arbitrary threshold value. These findings suggest that there is a relationship between risk levels and object-oriented metrics and that risk levels can be used to identify threshold effects.

A New Soft Sensor Based on Recursive Partial Least Squares for Online Melt Index Predictions in Grade-Changing HDPE Operations

Soft Sensors have been developed through phenomenological, empirical and hybrid modeling for quality variable predictions in various chemical processes. In this work a soft sensor based on an empirical model has been developed for the successful predictions of melt index (MI) in grade-changing polymerization of High Density Polyethylene (HDPE) processes. In order to capture the nonlinearity and grade-changing characteristics of the polymerization process efficiently, a recursive partial least squares (RPLS) update as well as a model bias update is applied to the process data successfully. Two schemes have been proposed: scheme-I and scheme-II. Scheme-I makes use of an arbitrary threshold value which selects one of the two update strategies according to the process requirement at a certain updating instance so as to minimize the relative root mean square error (RMSE). On the other hand, with the aim of preventing excessive RPLS update, scheme-II minimizes the number of RPLS update runs (NPR) while maintaining, increasing or sometimes reducing the RMSE obtained from scheme-I. Proposed schemes are compared with other strategies to exhibit their superiority.

Filtering Genes for Cluster and Network Analysis

BackgroundPrior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias.ResultsThis paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks.ConclusionThe methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.