A tracking algorithm of infrared sequence based on multi-model integration

2014-2015 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Estimation of potential gains from bank mergers:a novel two-stage cost efficiency DEA model

This paper develops a novel two-stage cost efficiency model to estimate and decompose the potential gains from Mergers and Acquisitions (M&As). In this model, a merged DMU is defined as a combination of two or more candidate DMUs. The merged DMU would surpass the traditional Production Possibility Set (PPS). In order to solve the problem, a Merger Production Possibility Set (PPSM) is constructed. The model minimizes the total cost of the merged DMU while maintaining its outputs at the current level, estimates the overall merger efficiency by comparing its minimal total cost with its actual cost. Moreover, the overall merger efficiency could be decomposed into technical efficiency, harmony efficiency and scale efficiency. We show that the model can be extended to a two-stage structure and these efficiencies can be decomposed to both sub-systems. To show the usefulness of the proposed approach, we applied it to a real dataset of top 20 most competitive Chinese City Commercial Banks (CCBs). We concluded that (1) There exist considerably potential gains for the proposed merged banks. (2) It is also shown that the main impact on potential merger gains are from technical and harmony efficiency. (3) As an interesting result we found that the scale effect works against the merger, indicating that it is not favorable for a full-scale merger

Transcriptional Homeostasis of a Mangrove Species, Ceriops tagal, in Saline Environments, as Revealed by Microarray Analysis

<div><h3>Background</h3><p>Differential responses to the environmental stresses at the level of transcription play a critical role in adaptation. Mangrove species compose a dominant community in intertidal zones and form dense forests at the sea-land interface, and although the anatomical and physiological features associated with their salt-tolerant lifestyles have been well characterized, little is known about the impact of transcriptional phenotypes on their adaptation to these saline environments.</p> <h3>Methodology and Principal findings</h3><p>We report the time-course transcript profiles in the roots of a true mangrove species, <em>Ceriops tagal</em>, as revealed by a series of microarray experiments. The expression of a total of 432 transcripts changed significantly in the roots of <em>C. tagal</em> under salt shock, of which 83 had a more than 2-fold change and were further assembled into 59 unigenes. Global transcription was stable at the early stage of salt stress and then was gradually dysregulated with the increased duration of the stress. Importantly, a pair-wise comparison of predicted homologous gene pairs revealed that the transcriptional regulations of most of the differentially expressed genes were highly divergent in <em>C. tagal</em> from that in salt-sensitive species, <em>Arabidopsis thaliana</em>.</p> <h3>Conclusions/Significance</h3><p>This work suggests that transcriptional homeostasis and specific transcriptional regulation are major events in the roots of <em>C. tagal</em> when subjected to salt shock, which could contribute to the establishment of adaptation to saline environments and, thus, facilitate the salt-tolerant lifestyle of this mangrove species. Furthermore, the candidate genes underlying the adaptation were identified through comparative analyses. This study provides a foundation for dissecting the genetic basis of the adaptation of mangroves to intertidal environments.</p> </div

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

Modelling low velocity impact induced damage in composite laminates

The paper presents recent progress on modelling low velocity impact induced damage in fibre reinforced composite laminates. It is important to understand the mechanisms of barely visible impact damage (BVID) and how it affects structural performance. To reduce labour intensive testing, the development of finite element (FE) techniques for simulating impact damage becomes essential and recent effort by the composites research community is reviewed in this work. The FE predicted damage initiation and propagation can be validated by Non Destructive Techniques (NDT) that gives confidence to the developed numerical damage models. A reliable damage simulation can assist the design process to optimise laminate configurations, reduce weight and improve performance of components and structures used in aircraft construction

Modelling low velocity impact induced damage in composite laminates

The paper presents recent progress on modelling low velocity impact induced damage in fibre reinforced composite laminates. It is important to understand the mechanisms of barely visible impact damage (BVID) and how it affects structural performance. To reduce labour intensive testing, the development of finite element (FE) techniques for simulating impact damage becomes essential and recent effort by the composites research community is reviewed in this work. The FE predicted damage initiation and propagation can be validated by Non Destructive Techniques (NDT) that gives confidence to the developed numerical damage models. A reliable damage simulation can assist the design process to optimise laminate configurations, reduce weight and improve performance of components and structures used in aircraft construction

Social learning against data falsification in sensor networks

Sensor networks generate large amounts of geographically-distributed data. The conventional approach to exploit this data is to first gather it in a special node that then performs processing and inference. However, what happens if this node is destroyed, or even worst, if it is hijacked? To explore this problem, in this work we consider a smart attacker who can take control of critical nodes within the network and use them to inject false information. In order to face this critical security thread, we propose a novel scheme that enables data aggregation and decision-making over networks based on social learning, where the sensor nodes act resembling how agents make decisions in social networks. Our results suggest that social learning enables high network resilience, even when a significant portion of the nodes have been compromised by the attacker