Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

Optimal robust optimization approximation for chance constrained optimization problem

Chance constraints are useful for modeling solution reliability in optimization under uncertainty. In general, solving chance constrained optimization problems is challenging and the existing methods for solving a chance constrained optimization problem largely rely on solving an approximation problem. Among the various approximation methods, robust optimization can provide safe and tractable analytical approximation. In this paper, we address the question of what is the optimal (least conservative) robust optimization approximation for the chance constrained optimization problems. A novel algorithm is proposed to find the smallest possible uncertainty set size that leads to the optimal robust optimization approximation. The proposed method first identifies the maximum set size that leads to feasible robust optimization problems and then identifies the best set size that leads to the desired probability of constraint satisfaction. Effectiveness of the proposed algorithm is demonstrated through a portfolio optimization problem, a production planning and a process scheduling problem.     

On lattice ordered soft sets

Certain type of linguistic terms such as satisfactory, good, very good and excellent have an order among them. In this paper we introduce a new concept of soft sets with some order among the parameters. Some properties of lattice ordered soft sets are given. Lattice ordered soft sets are very useful in particular type of decision making problems where some order exists among the elements of parameters set.     

Some algebraic applications of soft sets

The paper presents an algebraic construction of the basic notions of soft set theory. Here we introduce the definition of bijective soft groups, dependent and independent soft groups. Some of their properties are investigated. Also we investigate the relationships between bijective soft groups and classical groups. In addition, we express some applications of bijective soft groups on coding theory.     

Trajectory-Shaping Guidance with final speed and load factor constraints

This paper describes the design of a guidance law used for guiding a hypersonic gliding vehicle against a ground target from a near-vertical orientation with a specified final speed and a near-zero final load factor. The guidance law consists of two terms: one is Trajectory-Shaping Guidance (TSG) used for steering the vehicle to the target from the specified orientation; the other is Final-Speed-Control Scheme (FSCS) used for controlling the vehicle to perform lateral maneuver to adjust the final speed. Further, the generalized closed form solutions of TSG are obtained from a more general linearized engagement model, where the speed of the vehicle can be an arbitrary positive function of time. By analyzing these solutions, the stability domain of the guidance coefficients is obtained such that the final load factor is zero. This domain is not affected by the change rate of the speed. Thus, according to this analysis, the proposed guidance law can achieve a near zero final load factor by properly selecting the guidance coefficients in the stability domain.     

Continuous space maximal coverage: Insights,advances and challenges

Siting facilities in continuous space to maximize coverage of regional demand is an important planning problem. It is challenging to solve because demand is continuously distributed in a region and facilities may be sited anywhere in the region, suggesting that an infinite number of locations must be considered both as demand for service as well as a potential facility. An ingenious approach for solving continuous space coverage problems is abstracting continuously distributed demand as discrete spatial objects (points, lines or polygons), generating a finite dominating set from continuous space as candidate facility sites, and applying a discrete location coverage model. However, abstracting continuous demand and potential facility sites often introduces errors and uncertainties in coverage of regional demand, rendering findings (service and optimal facility locations) less than definitive. In this paper we examine the continuous space maximal coverage problem. Studies focused on locating antennas in Paradise Valley, Arizona and emergency warning sirens in Dublin, Ohio, are used to highlight issues and challenges in continuous space modeling. Potential paths forward are suggested based on empirical insights.     

Recent progress of green sorbents-based technologies for low concentration CO2 capture

The increased concentration of CO₂ due to continuous breathing and no discharge of human beings in the manned closed space, like spacecraft and submarines, can be a threat to health and safety. Effective removal of low concentration CO₂ from the manned closed space is essential to meet the requirements of long-term space or deep-sea exploration, which is an international frontier and trend. Ionic liquids (ILs), as a widespread and green solvent, already showed its excellent performance on CO₂ capture and absorption, indicating its potential application in low concentration CO₂ capture. In this review, we first summarized the current methods and strategies for direct capture from low concentration CO₂ in both the atmosphere and manned closed spaces. Then, the multi-scale simulation methods of CO₂ capture by ionic liquids are described in detail, including screening ionic liquids by COSMO-RS methods, capture mechanism by density functional theory and molecular dynamics simulation, and absorption process by computational fluid dynamics simulation. Lastly, some typical IL-based green technologies for low concentration CO₂ capture, such as functionalized ILs, co-solvent systems with ILs, and supported materials based on ILs, are introduced, and analyzed the subtle possibility in manned closed spaces. Finally, we look forward to the technology and development of low concentration CO₂ capture, which can meet the needs of human survival in closed space and proposed that supported materials with ionic liquids have great advantages and infinite possibilities in the vital area.     

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.