Promising Subsets Research Articles

2D‐MoX2 (X = S, Se, Te) and Their Nanocomposite Toward Sensing Application: A Review

Abstract2D materials, owing to their nearly atomic thickness, have emerged as promising candidates across a broad spectrum of next‐generation devices and systems. In the post‐graphene era, molybdenum‐based dichalcogenides (MoX2, where X = S, Se, Te), possessing a graphene‐like structure, represent one of the most promising subsets among 2D transition metal dichalcogenides (TMDs) due to their extensively researched and distinctive electronic, optical, and mechanical properties. Further with their distinct properties of different phases (2H, 1T) make it attractive for both fundamental and applied research. It finds diverse applications, spanning from optoelectronics to catalysis and sensor development. In this review article, the unique crystal structural properties of MoX2 are highlighted and their different synthesis methods, incorporating recent advancements in synthesis approaches discussed. Subsequently the recent development of MoX2 nanocomposite based on carbon, metal, metal oxide and various polymer discussed. Finally, the key challenges impeding the advancement of sensing applications and propose avenues for future development, drawing upon the current progress in 2D MoX2 and their nanocomposites also find mention in this review.

Modeling Hydrogen Release from Water with Borane and Alane Catalysts: A Unified Reaction Valley Approach.

The unified reaction valley approach combined with the local vibrational mode and ring puckering analysis is applied to investigate the hydrogen evolution from water in the presence of small hydrides such as BH3, metal hydrides as AlH3, and their derivatives. We studied a series of reactions involving BH3, AlH3, B2H6, Al2H6, and AlH3BH3 with one- and two-water molecules, considering multiple reaction paths. In addition, the influence of the aqueous medium was examined. A general reaction mechanism was identified for most of the reactions. Those that deviate could be associated with unusually high reaction barriers with no hydrogen release. The charge transfer along the reaction path suggests that a viable hydrogen release is achieved when the catalyst adopts the role of a charge donor during the chemical processes. The puckering analysis showed that twistboat and boat forms are the predominant configurations in the case of an intermediate six-membered ring formation, which influences the activation barrier. The local mode analysis was used as a tool to detect the H-H bond formation as well as to probe catalyst regenerability. Based on the correlation between the activation energy and the change in the charge separation for cleaving O-H and B(Al)-H bonds, two promising subsets of reactions could be identified along with prescriptions for lowering the reaction barrier individually with electron-donating/withdrawing substituents.

Large-scale object selection and trajectory planning for multi-target space debris removal missions

Upcoming active space debris removal missions will most likely attempt to remove several objects per mission. The design of such missions involves the selection of the objects to be removed, as well as the optimisation of the visit sequence and the orbital transfers interconnecting them. In this work a branch-and-bound-based algorithm is presented for the preliminary design of multi-target space debris removal missions. The proposed algorithm comprises two different levels. The upper level, modelled as an Integer Linear Programming problem, deals with the combinatorial complexity of the problem. The lower level, modelled as a Mixed Integer Nonlinear Programming problem, encapsulates the orbital dynamics. Throughout the problem resolution, the upper level selects promising subsets of a pool of candidate objects of space debris, so that a removed threat value is maximised. Each of these subsets is passed through to the lower level, which ensures that there is a feasible trajectory that allows to rendezvous, in a specific sequence, with each and every object in the subset, while prescribed mission duration and Δv constraints are fulfilled. This framework is able to exploit the structure of the problem so that instances with large pools of candidate objects can be efficiently solved while achieving the certificates of optimality that branch-and-bound methods provide.

Software Fault Prediction Using Deep Learning Algorithms

Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.

A novel bacterial algorithm with randomness control for feature selection in classification

Feature selection (FS) is an essential data-processing technique to reduce the number of features and improve the classification performance, but it is also a challenging problem because of the large search space and complex interactions between features. Bacterial based algorithms (BAs) are effective population based techniques known for their global searching capability. This paper proposes a novel bacterial algorithm based on control mechanisms and modified population updating strategies for feature selection in classification. The proposed new method, abbreviated as BAFS, employs three parameters to control the randomness of the population and reduce the computational complexity by avoiding the redundant searching for the optimal. To make the solutions suitable for feature selection, the strategies of reproduction and elimination are modified according to the classification performance and occurrence of features, respectively. Feature distribution is measured by the probability that features are appeared in the most promising subsets. The proposed bacterial based feature selection algorithm is used for selecting the best feature subsets on datasets with varying dimensionality. Comparison studies on five bacterial based algorithms indicate that the proposed BAFS outperforms other algorithms by achieving higher classification performance.

Adaption of the Cytokinesis-Block Micronucleus Cytome Assay for Improved Triage Biodosimetry.

The purpose of this work was to adapt a more advanced form of the cytokinesis-block micronucleus (CBMN) cytome assay for triage biodosimetry in the event of a mass casualty radiation incident. We modified scoring procedures for the CBMN cytome assay to optimize field deployability, dose range, accuracy, speed, economy, simplicity and stability. Peripheral blood of 20 donors was irradiated in vitro (0-6 Gy X ray, maximum photon energy 240 keV) and processed for CBMN. Initially, we assessed two manual scoring strategies for accuracy: 1. Conventional scoring, comprised of micronucleus (MN) frequency per 1,000 binucleated (BN) cells (MN/1,000 BN cells); and 2. Evaluation of 1,000, 2,000 and 3,000 cells in total and different cellular subsets based on MN formation and proliferation (e.g., BN cells with and without MN, mononucleated cells). We used linear and logistic regression models to identify the cellular subsets related closest to dose with the best discrimination ability among different doses/dose categories. We validated the most promising subsets and their combinations with 16 blind samples covering a dose range of 0-8.3 Gy. Linear dose-response relationships comparable to the conventional CBMN assay (r(2) = 0.86) were found for BN cells with MN (r(2) = 0.84) and BN cells without MN (r(2) = 0.84). Models of combined cell counts (CCC) of BN cells with and without MN (BN(+MN) and BN(-MN)) with mononucleated cells (Mono) improved this relationship (r(2) = 0.92). Conventional CBMN discriminated dose categories up to 3 Gy with a concordance between 0.96-1.0 upon scoring 1,000 total cells. In 1,000 BN cells, concordances were observed for conventional CBMN up to 4 Gy as well as BN(+MN) or BN(-MN) (about 0.85). At doses of 4-6 Gy, the concordance of conventional CBMN, BN(+MN) and BN(-MN) declined (about 0.55). We found about 20% higher concordance and more precise dose estimates of irradiated and blinded samples for CCC (Mono + BN(+MN)) after scoring 1,000 total cells. Blinded sample analysis revealed that the mean absolute difference (MAD) of dose estimates and the number of dose estimates outside the ±0.5 Gy interval based on CCC (Mono + BN(+MN)) was comparable to conventional CBMN for doses ≤4 Gy when scoring 3,000 total cells or more. At doses >4-8.3 Gy, the MAD of CCC (Mono + BN(+MN)) declined to half of the MADs observed for conventional CBMN, suggesting that the combined cell counts approach improved the discrimination ability. Conventional CBMN at 1,000 total-cell counts performed as efficiently as counting 1,000 BN cells. Discriminating and counting only BN cells with and without MN after 1,000 BN cells at ≤4 Gy revealed performances similar to conventional CBMN. After 3,000 total cells were scored, CCC (Mono + BN(+MN)) was superior to conventional CBMN at >4 Gy up to about 8 Gy. Our modification of CBMN evaluations saved time compared to the widely established semiautomated MN scoring and extended the dose range up to approximately 6 Gy for triage biodosimetry.

Expansion platform type II: testing a treatment strategy

Expansion platform type II: testing a treatment strategy

Bayesian Subset Selection for Reproductive Dispersion Linear Models

AbstractWe propose a full Bayesian subset selection method for reproductive dispersion linear models, which bases on expanding the usual link function to a function that incorporates all possible subsets of predictors by adding indictors as parameters. The vector of indicator variables dictates which predictors to delete. An efficient MCMC procedure that combining Gibbs sampler and Metropolis-Hastings algorithm is presented to approximate the posterior distribution of the indicator variables. The promising subsets of predictors can be identified as those with higher posterior probability. Several numerical examples are used to illustrate the newly developed methodology.

Stochastic search item selection for factor analytic models

In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets.

Bayesian principal component regression with data-driven component selection

Principal component regression (PCR) has two steps: estimating the principal components and performing the regression using these components. These steps generally are performed sequentially. In PCR, a crucial issue is the selection of the principal components to be included in regression. In this paper, we build a hierarchical probabilistic PCR model with a dynamic component selection procedure. A latent variable is introduced to select promising subsets of components based upon the significance of the relationship between the response variable and principal components in the regression step. We illustrate this model using real and simulated examples. The simulations demonstrate that our approach outperforms some existing methods in terms of root mean squared error of the regression coefficient.

A nested partitions framework for beam angle optimization in intensity-modulated radiation therapy

Coupling beam angle optimization with dose optimization in intensity-modulated radiation therapy (IMRT) increases the size and complexity of an already large-scale combinatorial optimization problem. We have developed a novel algorithm, nested partitions (NP), that is capable of finding suitable beam angle sets by guiding the dose optimization process. NP is a metaheuristic that is flexible enough to guide the search of a heuristic or deterministic dose optimization algorithm. The NP method adaptively samples from the entire feasible region, or search space, and coordinates the sampling effort with a systematic partitioning of the feasible region at successive iterations, concentrating the search in promising subsets. We used a ‘warm-start’ approach by initiating NP with beam angle samples derived from an integer programming (IP) model. In this study, we describe our implementation of the NP framework with a commercial optimization algorithm. We compared the NP framework with equi-spaced beam angle selection, the IP method, greedy heuristic and random sampling heuristic methods. The results of the NP approach were evaluated using two clinical cases (head and neck and whole pelvis) involving the primary tumor and nodal volumes. Our results show that NP produces better quality solutions than the alternative considered methods.

A HYBRID FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION

Feature Selection is an important task within Text Categorization, where irrelevant or noisy features are usually present, causing a lost in the performance of the classifiers. Feature Selection in Text Categorization has usually been performed using a filtering approach based on selecting the features with highest score according to certain measures. Measures of this kind come from the Information Retrieval, Information Theory and Machine Learning fields. However, wrapper approaches are known to perform better in Feature Selection than filtering approaches, although they are time-consuming and sometimes infeasible, especially in text domains. However a wrapper that explores a reduced number of feature subsets and that uses a fast method as evaluation function could overcome these difficulties. The wrapper presented in this paper satisfies these properties. Since exploring a reduced number of subsets could result in less promising subsets, a hybrid approach, that combines the wrapper method and some scoring measures, allows to explore more promising feature subsets. A comparison among some scoring measures, the wrapper method and the hybrid approach is performed. The results reveal that the hybrid approach outperforms both the wrapper approach and the scoring measures, particularly for corpora whose features are less scattered over the categories.

Bayesian variable selection for the analysis of microarray data with censored outcomes

A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions. We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers. The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author. mvannucci@stat.tamu.edu Supplementary data are available at Bioinformatics online.

The approach‐dependent, time‐dependent, label‐constrained shortest path problem

AbstractIn this article, we consider an approach‐dependent, time‐dependent, label‐constrained shortest path problem. This is a variant of the shortest path problem defined on a transportation network comprised of a set of nodes and a set of directed arcs such that each arc has an associated label designating a mode of transportation, and an associated travel‐time function that depends on the time of arrival at the tail node, as well as on the link via which this node was approached. A label constraint is specified that controls the admissible sequence of transportation modes, and the approach‐dependent travel‐time feature models turn‐penalties and prohibitions in transportation networks, whereby the time spent at an intersection before entering the next link depends on whether we travel straight through the intersection, or make a right turn at it, or make a left turn at it, as permissible. We propose two effective algorithms to solve this problem and present related theoretical complexity results. In addition, we also explore various heuristic delay estimation and network curtailment schemes that can be used to confine the search to only the more promising subsets of the network to reduce computational effort while preserving near‐optimality. Extensive empirical results using available data for the Portland, OR, and Blacksburg, VA, transportation networks are presented to demonstrate the efficacy of the developed procedures. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(2), 57–67 2006

Subset selection of autoregressive time series models

We propose a solution to select promising subsets of autoregressive time series models for further consideration which follows up on the idea of the stochastic search variable selection procedure in George and McCulloch (1993). It is based on a Bayesian approach which is unconditional on the initial terms. The autoregression stepup is in the form of a hierarchical normal mixture model, where latent variables are used to identify the subset choice. The framework of our procedure is utilized by the Gibbs sampler, a Markov chain Monte Carlo method. The advantage of the method presented is computational: it is an alternative way to search over a potentially large set of possible subsets. The proposed method is illustrated with a simulated data as well as a real data. Copyright © 1999 John Wiley & Sons, Ltd.

Water supply planning under interdependence of actions: Theory and application

An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple‐objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple‐criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.

Feature subset selection for classification of histological images

Classification of histological images is considered in this paper. The task is to distinguish different classes of tumours of the central nervous system on the basis of features extracted from microscopic slides. The number of extracted features is relatively high and some of them seem to be irrelevant for classification of the images. Thus, the main objective of this study is to select such a feature subset that improves the predictive accuracy of the classifier. The wrapper approach is chosen to obtain this aim, where a search for the good subset of features is made using a non-parametric case-base classifier. To guide a search process, a forward beam selection algorithm is introduced. It sequentially adds relevant features in a parallel way for the most promising subsets. It is shown that the proposed approach gives good predictive accuracy for the considered histopathological problem.

Feature Subset Selection within a Simulated Annealing DataMining Algorithm

An overview of the principle feature subset selection methods is given. We investigate a number of measures of feature subset quality, using large commercial databases. We develop an entropic measure, based upon the information gain approach used within ID3 and C4.5 to build trees, which is shown to give the best performance over our databases. This measure is used within a simple feature subset selection algorithm and the technique is used to generate subsets of high quality features from the databases. A simulated annealing based data mining technique is presented and applied to the databases. The performance using all features is compared to that achieved using the subset selected by our algorithm. We show that a substantial reduction in the number of features may be achieved together with an improvement in the performance of our data mining system. We also present a modification of the data mining algorithm, which allows it to simultaneously search for promising feature subsets and high quality rules. The effect of varying the generality level of the desired pattern is also investigated.

Similarity landscapes: A way to detect many structural and sequence motifs in both introns and exons

When investigators undertake searches of DNA databases, they normally discard large numbers of alignments that demonstrate very weak resemblances to each other, retaining only those that show statistically significant levels of resemblance. We show here that a great deal of information can be extracted from these weak alignments by examining them en masse. This is done by building three-dimensional similarity landscapes from the alignments, landscapes that reveal whether an unusual number of individually nonsignificant alignments tend to match up to a particular region of the query sequence being searched. The power of the search is increased by the use of libraries consisting entirely of introns or of exons. We show that (1) similarity landscapes with a variety of features can be generated from both intron and exon libraries, using introns or exons as query sequences; (2) the landscape features are real and not a statistical artifact; (3) well-known protein motifs used as query sequences can generate various landscape features; and (4) there is some evidence for resemblances between short regions of sequence carried by introns and exons. One possible interpretation of these results is that both introns and exons may have been built up during their evolution from short regions of sequence that as a result are now widely distributed throughout eukaryotic genomes. Such an interpretation would imply that these short regions have common ancestry. Alternatively, the wide sharing of short pieces of DNA may reflect regions with particular structural properties that have arisen through convergent evolution. The similarity-landscape approach can be used to detect such widespread structural motifs and sequence motifs in the genome that might be missed by less-global searches. It can also be used in conjunction with algorithms developed for detecting significant multiple alignments by isolating promising subsets of the databases that can be examined in more detail.