The Comparisons of Four Splitting Rules for Fitting a Classification Tree with Simulation and an Application Related to Albuminuria Data in Type 2 Diabetes Mellitus

The objective of this study was to compare the performances of splitting rules for predicting an ordinal response with simulation and a real data set. In the case of simulations, we compared across the methods using different sample sizes and the number of independent variables by employing the Monte Carlo simulation method. In the real data application, an analysis was performed with 265 cases. The results showed that the performances of the generalized Gini with the linear and quadratic costs of misclassification were better suited for analysis based on the gamma ordinal association measure and misclassification error rate than the other approaches. According to the gamma ordinal association measure, the generalized Gini (linear and quadratic) to the major risk factors determined for albuminuria in type 2 diabetes mellitus patients showed a slightly better performance than the other approaches. The predictive capability of splitting rules based on generalized Gini for predicting an ordinal response can be used for different sample sizes, number of independent variables and potential future suitable classification data problems. Consequently, our study will move towards choosing the generalized Gini (linear or quadratic) as the splitting rule and evaluate the data by using the Classification Trees (CT) in future studies, focusing on predicting an ordinal response

A Domain Analysis to Specify Design Defects and Generate Detection Algorithms

Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud and maintenance. Consequently, several defect detection approaches\ud and tools have been proposed in the literature. However, we are not\ud aware of any approach that defines and reifies the process of generating\ud detection algorithms from the existing textual descriptions of defects.\ud In this paper, we introduce an approach to automate the generation\ud of detection algorithms from specifications written using a domain-specific\ud language. The domain-specific is defined from a thorough domain analysis.\ud We specify several design defects, generate automatically detection\ud algorithms using templates, and validate the generated detection\ud algorithms in terms of precision and recall on Xerces v2.7.0, an\ud open-source object-oriented system

From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma

Patterns of genomic and phenomic diversity in wine and table grapes.

Grapes are one of the most economically and culturally important crops worldwide, and they have been bred for both winemaking and fresh consumption. Here we evaluate patterns of diversity across 33 phenotypes collected over a 17-year period from 580 table and wine grape accessions that belong to one of the world's largest grape gene banks, the grape germplasm collection of the United States Department of Agriculture. We find that phenological events throughout the growing season are correlated, and quantify the marked difference in size between table and wine grapes. By pairing publicly available historical phenotype data with genome-wide polymorphism data, we identify large effect loci controlling traits that have been targeted during domestication and breeding, including hermaphroditism, lighter skin pigmentation and muscat aroma. Breeding for larger berries in table grapes was traditionally concentrated in geographic regions where Islam predominates and alcohol was prohibited, whereas wine grapes retained the ancestral smaller size that is more desirable for winemaking in predominantly Christian regions. We uncover a novel locus with a suggestive association with berry size that harbors a signature of positive selection for larger berries. Our results suggest that religious rules concerning alcohol consumption have had a marked impact on patterns of phenomic and genomic diversity in grapes

The co-evolution of number concepts and counting words

Humans possess a number concept that differs from its predecessors in animal cognition in two crucial respects: (1) it is based on a numerical sequence whose elements are not confined to quantitative contexts, but can indicate cardinal/quantitative as well as ordinal and even nominal properties of empirical objects (e.g. ‘five buses’: cardinal; ‘the fifth bus’: ordinal; ‘the #5 bus’: nominal), and (2) it can involve recursion and, via recursion, discrete infinity. In contrast to that, the predecessors of numerical cognition that we find in animals and human infants rely on finite and iconic representations that are limited to cardinality and do not support a unified concept of number. In this paper, I argue that the way such a unified number concept could evolve in humans is via verbal sequences that are employed as numerical tools, that is, sequences of words whose elements are associated with empirical objects in number assignments. In particular, I show that a certain kind of number words, namely the counting sequences of natural languages, can be characterised as a central instance of verbal numerical tools. I describe a possible scenario for the emergence of such verbal numerical tools in human history that starts from iconic roots and that suggests that in a process of co-evolution, the gradual emergence of counting sequences and the development of an increasingly comprehensive number concept supported each other. On this account, it is language that opened the way for numerical cognition, suggesting that it is no accident that the same species that possesses the language faculty as a unique trait, should also be the one that developed a systematic concept of number

Place effects on environmental views

How people respond to questions involving the environment depends partly on individual characteristics. Characteristics such as age, gender, education, and ideology constitute the well-studied social bases of environmental concern, which have been explained in terms of cohort effects or of cognitive and cultural factors related to social position. It seems likely that people\u27s environmental views depend not only on personal characteristics but also on their social and physical environments. This hypothesis has been more difficult to test, however. Using data from surveys in 19 rural U.S. counties, we apply mixed-effects modeling to investigate simple place effects with respect to locally focused environmental views. We find evidence for two kinds of place effects. Net of individual characteristics, specific place characteristics have the expected effect on related environmental views. Local changes are related to attitudes about regulation and growth. For example, respondents more often perceive rapid development as a problem, and favor environmental rules that restrict development, in rural counties with growing populations. Moreover, they favor conserving resources for the future rather than using them now to create jobs in counties that have low unemployment. After we controlled for county growth, unemployment and jobs in resource based industries, and individual social-position and ideological factors, there remains significant place-to-place variation in mean levels of environmental concern. Even with both kinds of place effects in the models, the individual level predictors of environmental concern follow patterns expected from previous research. Concern increases with education among Democrats, whereas among Republicans, the relationship is attenuated or reversed. The interaction marks reframing of environmental questions as political wedge issues, through nominally scientific counterarguments aimed at educated, ideologically receptive audiences. © 2010, by the Rural Sociological Society

On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg

Assessing the impact of political economy factors on rules of origin under NAFTA

Rules of origin are legitimate policy instruments to prevent trade deflection in a preferential trade agreement short of a customs union. Trade deflection takes place when a product imported into the preferential trade agreement through the member with the lowest external tariff is transhipped to a higher-tariff member, while yielding a benefit for the re-exporter. Yet, when captured by special interest groups, rules of origin can restrict trade beyond what is needed to prevent trade deflection. By how much do political economy factors account for the stringency of rules of origin? This study quantifies the impact of both determinants - those considered"justifiable"because they prevent trade deflection and those deemed to arise from"political economy"forces - on the restrictiveness of rules of origin under the North American Free Trade Agreement, approximated by a restrictiveness index. The main finding is that political economy forces, especially from the United States, raised significantly the restrictiveness of the rules of origin. Indeed, in industries where political-economy forces were strong prior to the North American Free Trade Agreement, as when the U.S. Most Favored Nation tariff was high or the revealed comparative advantage of Mexico (the United States) was strong (weak), more stringent rules of origin were introduced. Thus, stricter rules of origin are associated with higher production costs reducing the potential benefits of enhanced market access that is initially pursued by this type of agreement.Free Trade,Economic Theory&Research,Trade Policy,Trade Law,Debt Markets

Thrust Joint Manipulation Utilization by Us Physical Therapists

Study Design: Online survey study. Objective: To determine physical therapists’ utilization of thrust joint manipulation (TJM) and their comfort level in using TJM between the cervical, thoracic, and lumbar regions of the spine. We hypothesized that physical therapists who use TJM would report regular use and comfort providing it to the thoracic and lumbar spines, but not so much for the cervical spine. Background: Recent surveys of first professional physical therapy degree programs have found that TJM to the cervical spine is not taught to the same degree as to the thoracic and lumbar spines. Methods: We developed a survey to capture the required information and had a Delphi panel of 15 expert orthopedic physical therapists reviewed it and provide constructive feedback. A revised version of the survey was sent to the same Delphi panel and consensus was obtained on the final survey instrument. The revised survey was made available to any licensed physical therapists in the USA using an online survey system, from October 2014 through June 2015. Results: Of 1014 responses collected, 1000 completed surveys were included for analysis. There were 478 (48%) males; the mean age of respondents was 39.7 ± 10.81 years (range 24 – 92); and mean years of clinical experience was 13.6 ± 10.62. A majority of respondents felt that TJM was safe and effective when applied to lumbar (90.5%) and thoracic (91.1%) spines; however, a smaller percentage (68.9%) felt that about the cervical spine. More therapists reported they would perform additional screening prior to providing TJM to the cervical spine than they would for the lumbar and thoracic spine. Therapists agreed they were less likely to provide and feel comfortable with TJM in the cervical spine compared to the thoracic and lumbar spine. Finally, therapists who are male; practice in orthopedic spine setting; are aware of manipulation clinical prediction rules; and have manual therapy certification, are more likely to use TJM and be comfortable with it in all 3 regions. Conclusion: Results indicate that respondents do not believe TJM for the cervical spine to be as safe and efficacious as that for the lumbar and thoracic spines. Further, they are more likely to perform additional screening, abstain from and do not feel comfortable performing TJM for the cervical spine. Clinical Relevance: Our research reveals there is a discrepancy between utilization of TJM at different spinal levels. This research provides an opportunity to address variability in clinical practice among physical therapists utilizing TJM