Anstine D.T.,Northwest Nazarene University |
Lightner D.A.,University of Nevada, Reno
Monatshefte fur Chemie | Year: 2014
Three new linear pentapyrrole rubinoid analogs: 2,3,7,8,17,18,22,23- octamethyl-12,13-bis-(2-carboxyethyl)-1,10,15,24,25,27,28,29-octahydro-27H- pentapyrrin-1,24-dione and 2,3,8,12,13,17,22,23-octamethyl-7,18-bis-(2- carboxyethyl)-1,10,15,24,25,26,27,28,29-octahydro-27H-pentapyrrin-1,24-dione as well as its 7,18-dihexanoic acid analog were synthesized, respectively, from 2,3,7,8-tetramethyl-(10H)-dipyrrin-2-one, from 2,3,8-trimethyl-7-[2- (methoxycarbonyl)ethyl]-(10H)-dipyrrinone, and from 2,3,8-trimethyl-7-[5- (methoxycarbonyl)pentyl]-(10H)-dipyrrinone.13C NMR and1H NMR measurements in (CD3)2SO confirmed the pentapyrrole structures, while1H NMR data indicate intramolecular hydrogen bonding between the CO2H and dipyrrinone groups. Molecular mechanics modeling studies suggest stable U-shape conformations capable of encapsulating small planar aromatic molecules. © 2014 Springer-Verlag Wien.
Rodriguez G.A.,Vanderbilt University |
Lonai J.D.,Northwest Nazarene University |
Mernaugh R.L.,Vanderbilt University |
Weiss S.M.,Vanderbilt University
Nanoscale Research Letters | Year: 2014
A porous silicon (PSi) Bloch surface wave (BSW) and Bloch sub-surface wave (BSSW) composite biosensor is designed and used for the size-selective detection of both small and large molecules. The BSW/BSSW structure consists of a periodic stack of high and low refractive index PSi layers and a reduced optical thickness surface layer that gives rise to a BSW with an evanescent tail that extends above the surface to enable the detection of large surface-bound molecules. Small molecules were detected in the sensor by the BSSW, which is a large electric field intensity spatially localized to a desired region of the Bragg mirror and is generated by the implementation of a step or gradient refractive index profile within the Bragg mirror. The step and gradient BSW/BSSW sensors are designed to maximize both resonance reflectance intensity and sensitivity to large molecules. Size-selective detection of large molecules including latex nanospheres and the M13KO7 bacteriophage as well as small chemical linker molecules is reported. © 2014 Rodriguez et al.; licensee Springer.
Wang X.,Northwest Nazarene University
Proceedings of the International Joint Conference on Neural Networks | Year: 2012
Good ensemble methods require accurate and diverse individual classifiers, but the relationship between the diversity of individual classifiers and the accuracy of an ensemble method is not clear. In this paper, we propose a novel model called COB (core, outlier, and boundary) to quantitatively measure the accuracies of majority voting ensembles for binary classification. In this model, we first divide data items into three subsets, core, outlier, and boundary, based on the prediction correctness of these items from individual classifiers in an ensemble method. Then we measure the accuracy of the ensemble method for each subset and combine the results together. We tested the performance of the COB model on 32 datasets from the UCI repository. The experiments use three different ensemble methods (bagging, random forests, and a randomized ensemble), two different numbers of individual classifiers (7 and 51), and three different individual machine learning algorithms (decision trees, k-nearest neighbors, and support vector machines). All 24 experiments showed less than 5% average absolute errors for 32 datasets between the accuracies by the COB model and the actual accuracies of ensembles. Also the experiments showed that the COB model performed significantly better than the binomial model. The COB model suggests that to achieve a high accuracy for an ensemble method, weak individual classifiers should be partly diverse instead of fully diverse, that is, be diverse on correctly predicted items but in agreement on some incorrectly predicted items. © 2012 IEEE.
Bankard J.,Northwest Nazarene University
Journal of Religion and Health | Year: 2015
Traditional moral philosophy has long focused on rationality, principled thinking, and good old-fashioned willpower, but recent evidence strongly suggests that moral judgments and prosocial behavior are more heavily influenced by emotion and intuition. As the evidence mounts, rational traditions emphasizing deliberative analysis and conscious decision making are called into question. The first section highlights some compelling evidence supporting the primacy of affective states in motivating moral judgments and behavior. The real challenge is finding a way to align intuition with desired behavior. In cool reflective states, one may desire to be a kind and loving person. But when it is time to act, the moment is often accompanied by strong affect-laden intuitions. I argue that if affective states are the primary motivators of behavior, then moral sentiments must be trained through habituation in order to increase prosocial behavior. The second section provides empirical evidence linking emotional training with increased prosociality. To highlight this connection, focus is placed on the relationship between habitual meditation training, compassion, and prosocial behavior. Recent studies by Antoine Lutz, Richard Davidson, Susanne Leiberg, and others show that various meditation practices can dramatically affect the human person at various levels, i.e., increased physical health, neural restructuring, regulation and development of emotions, and increased helping behavior, to name a few. The current article focuses on the impact the habit of loving-kindness meditation (LKM) has on compassion and prosocial behavior. Recent studies strongly support the conclusion that LKM training hones compassion and ultimately leads to an increase in compassionate behavior. © 2015, Springer Science+Business Media New York.
Wang X.,Northwest Nazarene University
Proceedings of the International Joint Conference on Neural Networks | Year: 2011
The κ-nearest neighbors (κ-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact κ-NN algorithm called κMκNN (κ-Means for κ-Nearest Neighbors) that uses the κ-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The κMκNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, κd-trees, or ball-tree, κMκNN uses a simple κ-means clustering method to preprocess the training dataset. In the searching stage, given a query object, κMκNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of κMκNN is surprisingly good compared to the traditional κ-NN algorithm and tree-based κ-NN algorithms such as κd-trees and ball-trees. On a collection of 20 datasets with up to 106 records and 104 dimensions, κMκNN shows a 2- to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional κ-NN algorithm for 16 datasets. Furthermore, κMκNN performs significant better than a κd-tree based κ-NN algorithm for all datasets and performs better than a ball-tree based κ-NN algorithm for most datasets. The results show that κMκNN is effective for searching nearest neighbors in high dimensional spaces. © 2011 IEEE.