Matrices representation of multi soft-sets and its application

In previous paper, we introduced a concept of multi-soft sets and used it for finding reducts. However, the comparison of the proposed reduct has not been presented yet, especially with rough-set based reduct. In this paper, we present matrices representation of multi-soft sets. We define AND and OR operations on a collection of such matrices and apply it for finding reducts and core of attributes in a multi-valued information system. Finally, we prove that our proposed technique for reduct is equivalent to Pawlak’s rough reduct.

A randomised controlled trial of the effectiveness of soft silicone multi-layered foam dressings in the prevention of sacral and heel pressure ulcers in trauma and critically ill patients: The border trial

The prevention of hospital acquired pressure ulcers in critically ill patients remains a significant clinical challenge. The aim of this trial was to investigate the effectiveness of multi-layered soft silicone foam dressings in preventing intensive care unit (ICU) pressure ulcers when applied in the emergency department to 440 trauma and critically ill patients. Intervention group patients (n = 219) had Mepilex^® Border Sacrum and Mepilex^® Heel dressings applied in the emergency department and maintained throughout their ICU stay. Results revealed that there were significantly fewer patients with pressure ulcers in the intervention group compared to the control group (5 versus 20, P = 0·001). This represented a 10% difference in incidence between the groups (3·1% versus 13·1%) and a number needed to treat of ten patients to prevent one pressure ulcer. Overall there were fewer sacral (2 versus 8, P = 0·05) and heel pressure ulcers (5 versus 19, P = 0·002) and pressure injuries overall (7 versus 27, P = 0·002) in interventions than in controls. The time to injury survival analysis indicated that intervention group patients had a hazard ratio of 0·19 (P = 0·002) compared to control group patients. We conclude that multi-layered soft silicone foam dressings are effective in preventing pressure ulcers in critically ill patients when applied in the emergency department prior to ICU transfer.

Labeled random finite sets and the bayes multi-target tracking filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.

A multi-agent framework for financial investment planning

If a company or person wants to invest a lot of money, where, when, and how should the investment go? A multi-agent based Financial Investment Planner may give some reasonable answers to the above question. Good advice is mainly based on adequate information, rich knowledge, and great
skills to use knowledge and information. To this end, this planner consists of four principal components information gathering agents that are responsible for gathering relevant information on the Internet, data mining agents that are in charge of discovering knowledge from retrieved information as well as other relevant databases, group decision making agents that can effectively use available knowledge and appropriate information to make reasonable decisions (investment advice), and a graphical user interface that interacts with users. This paper is focused on the group decision making part. The design and implementation of an agent-based hybrid intelligent system - agent-based soft computing society are detailed.

Clustering based multi-label classification for image annotation and retrieval

This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.

Multi-scale modelling of DP590 steel

Dual Phase (DP) steel one of the Advanced High Strength Steels (AHSS) has a two phase microstructure where soft and hard phase acts together to offer a high strength composite effect. The high strength, however, must be balanced with ductility so that complex parts and designs can be manufactured from AHSS sheets. However, during forming certain grades of DP steel a sudden crack can occur without any intimation of necking. Thus, due to this abnormal forming behaviour, is difficult to accurately predict because most classical modelling approaches are not designed for such micro-structurally heterogeneous materials. These modelling approaches are generally based on an average representation of the material behaviour in a continuum mechanics formulation. This works for materials that are homogenous, or at least could be assumed to be homogenous at scales lower than the naked eye can see. However, for a material like AHSS, the microstructure plays a significant role in dictating the mechanical behaviour at the macro-scale. This paper studies the multi-scale modelling ofDP590 steel. It is found that the sufficient accuracy can be achieved from multi-scale modelling while comparing with the experiments.

Generalized Bonferroni mean operators in multi-criteria aggregation

In this paper we provide a systematic investigation of a family of composed aggregation functions which generalize the Bonferroni mean. Such extensions of the Bonferroni mean are capable of modeling the concepts of hard and soft partial conjunction and disjunction as well as that of k-tolerance and k-intolerance. There are several interesting special cases with quite an intuitive interpretation for application.

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.

Fracture of DP590 steel : a multi-scale modeling approach

Advanced high strength steel sheets are one of the higher strength advance material developed by the steel industry for automotive bodies. One of the categories of this advanced high strength steel is Dual Phase (DP) steel. This steel consists of a two phase microstructure where soft and hard phase acts together to offer a high strength composite effect. The combination of high strength and ductility exhibited by these sheets allows the design and manufacture of complex parts. However, during forming certain grades of DP steel sudden cracking can occur without any intimation of necking. This abnormal forming behavior is difficult to accurately predict because most classical modelling approaches are not designed for such micro-structurally heterogeneous materials. These modelling approaches are generally based on an average representation of the material behaviour in a continuum mechanics formulation. This works for materials that are homogenous, or at least could be assumed to be homogenous at scales lower than the naked eye can see. However, for a material like advanced high strength steel, the microstructure plays a significant role in dictating the mechanical behavior at the macro-scale. This paper studies the forming and fracture behavior through multi-scale modeling of DPO590 steel. It is found that the sufficient accuracy can be achieved from multi-scale modeling when comparing with experiments.

Teaching soft issues in strategic management with films : arguments and suggestions

Strategic matters are extraordinarily complex and involve many different and interlinked processes and influences. This makes the subject difficult for students lacking managerial experience as they are unaware of the intricacy of the problems being discussed. Strategy requires not just a theoretical understanding of the subject, but also a practical feel for business and organisation. We argue that films can help the instructor because they offer elaborate multidimensional and multi-layered contexts which mirror the reality of business. Moreover, their powerful narratives aid the retention of ideas and encourage engagement with issues. To illustrate the appropriateness of films in teaching strategic management, we review the strategy curriculum, highlight some of the teaching difficulties and show how using films could help students’ learning.

Volume confinement induced microstructural transitions and property enhancements of supramolecular soft materials

The rheological properties of supramolecular soft functional materials are determined by the networks within the materials. This research reveals for the first time that the volume confinement during the formation of supramolecular soft functional materials will exert a significant impact on the rheological properties of the materials. A class of small molecular organogels formed by the gelation of N-lauroyl-L-glutamic acid din-butylamide (GP-1) in ethylene glycol (EG) and propylene glycol (PG) solutions were adopted as model systems for this study. It follows that within a confined space, the elasticity of the gel can be enhanced more than 15 times compared with those under un-restricted conditions. According to our optical microscopy observations and rheological measurements, this drastic enhancement is caused by the structural transition from a multi-domain network system to a single network system once the average size of the fiber network of a given material reaches the lowest dimension of the system. The understanding acquired from this work will provide a novel strategy to manipulate the network structure of soft materials, and exert a direct impact on the micro-engineering of such supramolecular materials in micro and nano scales.

Architecture of supramolecular soft functional materials: from understanding to micro-/nanoscale engineering

This article gives an overview of the current progress of a class of supramolecular soft materials consisting of fiber networks and the trapped liquid. After discussing the up-to-date knowledge on the types of fiber networks and the correlation to the rheological properties, the gelation mechanism turns out to be one of the key subjects for this review. In this concern, the following two aspects will be focused upon: the single fiber network formation and the multi-domain fiber network formation of this type of material. Concerning the fiber network formation, taking place via nucleation, and the nucleation-mediated growth and branching mechanism, the theoretical basis of crystallographic mismatch nucleation that governs fiber branching and formation of three-dimensional fiber networks is presented. In connection to the multi-domain fiber network formation, which is governed by the primary nucleation and the subsequent formation of single fiber networks from nucleation centers, the control of the primary nucleation rate will be considered. Based on the understanding on the the gelation mechanism, the engineering strategies of soft functional materials of this type will be systematically discussed. These include the control of the nucleation and branching-controlled fiber network formation in terms of tuning the thermodynamic driving force of the gelling system and introducing suitable additives, as well as introducing ultrasound. Finally, a summary and the outlook of future research on the basis of the nucleation-growth-controlled fiber network formation are given.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.