Kinetic Monte Carlo simulation of low-pressure chemical vapor deposition of silicon nitride: Impact of gas flow rate and temperature on silicon cluster size and density

In the present study, the deposition process of SiN_x thin films obtained by a low-pressure chemical vapor deposition technique with a mixture of disilane (Si₂H₆) and ammonia (NH₃) was simulated by using the kinetic Monte Carlo method. A new pattern describing the distribution of ammonia molecules in the simulation matrix was proposed. The influences of the NH₃/Si₂H₆ gas flow ratio and the deposition temperature on the obtained films structure in terms of silicon cluster size and density were analyzed. The simulation results indicate that an increase in the gas flow ratio leads to the deposition of amorphous silicon clusters characterized by small sizes. Nevertheless, an increase in the temperature values of the process provokes an enhancement in the silicon cluster size along with a decrease in their density.     

Alarm management practices in natural gas processing plants

In industrial data sets, groups of variables often move together. Monitoring all these variables may result in many nuisance alarms. However, it is possible to take advantage of redundant information to design and reduce the size of alarm sets. The present work reports the application of an alarm management protocol based on alarm priorization to three large Natural Gas Processing Plants, during a three year period, and also investigates the use of different correlation analyses techniques as tools to assist in the further reduction of the number of alarms. The results show that the adopted practices enable the reduction of alarms.     

Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.     

Cluster ensembles: A survey of approaches with recent extensions and applications

Cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy and robustness across different data collections. This meta-learning formalism also helps users to overcome the dilemma of selecting an appropriate technique and the corresponding parameters, given a set of data to be investigated. Almost two decades after the first publication of a kind, the method has proven effective for many problem domains, especially microarray data analysis and its down-streaming applications. Recently, it has been greatly extended both in terms of theoretical modelling and deployment to problem solving. The survey attempts to match this emerging attention with the provision of fundamental basis and theoretical details of state-of-the-art methods found in the present literature. It yields the ranges of ensemble generation strategies, summarization and representation of ensemble members, as well as the topic of consensus clustering. This review also includes different applications and extensions of cluster ensemble, with several research issues and challenges being highlighted.     

Multivariate methods using mixtures: Correspondence analysis,scaling and pattern-detection

Matrices of binary or count data are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. These likelihood-based one-mode and two-mode fuzzy clusterings provide maximum likelihood estimation of parameters and the options of using likelihood ratio tests or information criteria for model comparison. Geometric developments focused on pattern detection give likelihood-based analogues of various techniques in multivariate analysis, including multidimensional scaling, association analysis, ordination, correspondence analysis, and the construction of biplots. Illustrative examples demonstrate the effectiveness of these visualisations for identifying patterns of ecological significance (e.g. abrupt versus slow species turnover).     

Clustering household energy-saving behaviours by behavioural attribute

Research on household energy conservation often categorizes targeted behaviours by their behavioural attributes (e.g., savings, cost, frequency). The most common distinction in the literature divides behaviours as follows: (1) low-impact, low-cost, repetitive behaviours that result in a loss of comfort or curtailment behaviours and (2) high-impact, high-cost, infrequent behaviours that result in no loss of amenities or efficiency behaviours. However, such categorizations have often been ad hoc and two-dimensional (e.g., low-impact vs. high-impact, low-cost vs. high-cost). In contrast, we systematically assess a large range of household energy-saving behaviours (N=261) across nine attributes – energy savings, cost, frequency of performance, required skill level, observability, locus of decision, household function, home topography, and appliance topography. By clustering behaviours according to these attributes, we discern four clusters of energy-saving behaviours: family style, call an expert, household management and weekend project. We discuss the implications of these clusters for intervention design.     

Faceted crack initiation characteristics for high-cycle and very-high-cycle fatigue of a titanium alloy under different stress ratios

Ultrasonic fatigue tests were conducted at the stress ratios of −1, −0.5, −0.1, 0.1 and 0.5 for a Ti–6Al–4V alloy in high-cycle and very-high-cycle fatigue regimes. Experimental results showed that faceted crack initiation was the main failure mode for specimens at the stress ratios of −0.1, 0.1 and 0.5, and multi-site faceted crack initiation was observed at the stress ratios of 0.1 and 0.5. The measurements indicated that the number of facets increased with the increase of stress ratio. Based on the observations, the mechanism of faceted crack initiation was proposed, i.e., (i) cleavage of isolated primary α grains in cluster; (ii) gradual growth of originated cracks (facets), and the coalescence of adjacent facets; and (iii) coalesced facets forming a main crack in the cluster. Moreover, a model based on Poisson defect distribution is proposed to describe the effects of stress ratio on faceted crack initiation, which is in agreement with the experimental results.     

Application of metaheuristics-based clustering algorithm to item assignment in a synchronized zone order picking system

Warehousing management policy is a crucial issue in logistic management. It must be managed effectively and efficiently to reduce the production cost as well as the customer satisfaction. Synchronized zoning system is a warehousing management policy which aims to increase the warehouse utilization and customer satisfaction by reducing the customer waiting time. This policy divides a warehouse into several zones where each zone has its own picker who can work simultaneously. Herein, item assignment plays an important role since it influences the order processing performance. This study proposes an application of metaheuristic algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA), for item assignment in synchronized zoning system. The original PSO and GA algorithms are modified so that it is suitable for solving item assignment problem. The datasets with different sizes are used for method validation. Results obtained by PSO and GA are then compared with the result of an existing algorithm. The experimental results showed that PSO and GA can perform better than the existing algorithm. These results also show that PSO has better performance than GA, especially for bigger problems. It proves that item assignment policy obtained by PSO and GA has higher utilization rates than the existing algorithm.     

A label field fusion model with a variation of information estimator for image segmentation

This paper proposes a new and reliable segmentation approach based on a fusion framework for combining multiple region-based segmentation maps (with any number of regions) to provide a final improved (i.e., accurate and consistent) segmentation result. The core of this new combination model is based on a consensus (cost) function derived from the recent information Theory based variation of information criterion, proposed by Meila, and allowing to quantify the amount of information that is lost or gained in changing from one clustering to another. In this case, the resulting consensus energy-based segmentation fusion model can be efficiently optimized by exploiting an iterative steepest local energy descent strategy combined with a connectivity constraint. This new framework of segmentation combination, relying on the fusion of inaccurate, quickly and roughly calculated, spatial clustering results, emerges as an appealing alternative to the use of complex segmentation models existing nowadays. Experiments on the Berkeley Segmentation Dataset show that the proposed fusion framework compares favorably to previous techniques in terms of reliability scores.     

Multivariate statistical analysis for monitoring the hydrocarbon potentiality of the source rocks in the North Western Desert,Egypt

The authors aimed to examine a multivariate statistical analysis approach for monitoring the hydrocarbon potentiality of source rocks in the North Western Desert to recognize the potentiality of source rocks and subsequently investigate the maturation level of the proven potential source formations for oil preservation. To achieve this aim, they applied multivariate statistics analysis (principle component analysis [PCA], cluster analysis, and correlation coefficient) and Rock-Eval pyrolysis analyses. The results showed that both PCA and cluster analyses have showed two types of source rocks. The first is classified as poor to fair source rock and immature to marginally mature source rock, the second is considered as fair to good source rock and mature to post mature source rock. PCA extracted two independent factors, which explained 81.87% of the total variance. The first type of source rock was responsible for 50% of the total variance and was best represented by variables TOC, S2, OI, and R_o, and the second one was responsible for 31.65% of the total variance and was represented by the HI variable. Pearson correlation coefficient showed a significant positive correlation between TOC and S2 or R_o. A positive correlation between S2 and HI or OI and also between OI and R_o was observed, while no significant correlation was noticed between HI and R_o.