Evidential-EM Algorithm Applied to Progressively Censored Observations

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data

Belief Hierarchical Clustering

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool

Second-Order Belief Hidden Markov Models

Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions. Previous works in belief HMMs have been focused on the first-order HMMs. We extend them to the second-order model

High-rate deposition of microcrystalline silicon p-i-n solar cells in the high pressure depletion regime

Hydrogenated microcryst. silicon films (micro c-Si:H) deposited at high deposition rates (.apprx.2 nm/s) by means of the very-high-frequency deposition technique in the high pressure depletion regime have been integrated into single junction p-i-n solar cells. It is demonstrated that micro c-Si:H solar cells can be optimized using a twofold approach. First the bulk properties, deposited under steady-state plasma conditions, are optimized by monitoring the presence of cryst. grain boundaries in micro c-Si:H. These hydrogenated cryst. grain boundaries can easily be detected via the cryst. surface hydrides contribution to the narrow high stretching modes by IR transmission spectroscopy. The cryst. grain boundaries suffer from post-deposition oxidn. which results in a reduced red response of the solar cell. The absence of these cryst. surfaces in an as-deposited micro c-Si:H matrix reflects the device grade microcryst. bulk material. Second, the prevention of silane back-diffusion from the background during the initial growth is a necessity to deposit a uniform micro c-Si:H phase over the entire film thickness. The initial growth is optimized while preserving the optimized bulk properties deposited under steady-state conditions, using initial profiling of plasma parameters such as the silane flow and the very-high-frequency power d. Solar cell devices with efficiency of 8.0% at a micro c-Si:H deposition rate of 2.0 nm/s are obtained using the presented approach

Evidential Communities for Complex Networks

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure

Charting new territory for organizational ethnography: Insights from a team-based video ethnography of reinsurance trading in Lloyd’s of London

Increasing complexity, fragmentation, mobility, pace, and technological intermediation of organizational life make “being there” increasingly difficult. Where do ethnographers have to be, when, for how long, and with whom to “be there” and grasp the practices, norms, and values that make the situation meaningful to natives? These novel complexities call for new forms of organizational ethnography. The purpose of this paper is to discuss the above issues

CRAF phase 1, a framework to identify coastal hotspots to storm impacts

Low-frequency high-impact storms can cause flood and erosion over large coastal areas, which in turn can lead to a significant risk to coastal occupation, producing devastation and immobilising cities and even countries. It is therefore paramount to evaluate risk along the coast at a regional scale through the identification of storm impact hotspots. The Coastal Risk Assessment Framework Phase 1 (CRAF1) is a screening process based on a coastal-index approach that assesses the potential exposure of every kilometre along the coast to previously identified hazards. CRAF1 integrates both hazard (e.g. overwash, erosion) and exposure indicators to create a final Coastal Index (CI). The application of CRAF1 at two contrasting case studies (Ria Formosa, Portugal and the Belgian coast), validated against existing information, demonstrates the utility and reliability of this framework on the identification of hotspots. CRAF1 represents a powerful and useful instrument for coastal managers and/or end-users to identify and rank potential hotspot areas in order to define priorities and support disaster reduction plans

Rational Design of Sustainable Liquid Microcapsules for Spontaneous Fragrance Encapsulation

The high volatility, water-immiscibility, and light/oxygen-sensitivity of most aroma compounds represent a challenge to their incorporation in liquid consumer products. Current encapsulation methods entail the use of petroleum-based materials, initiators, and crosslinkers as well as mixing, heating, and purification steps. Hence, more efficient and eco-friendly approaches to encapsulation must be sought. Herein, we propose a simple method by making use of a pre-formed amphiphilic polymer and employing the Hansen Solubility Parameters approach to determine which fragrances could be encapsulated by spontaneous coacervation in water. The coacervates do not precipitate as solids but they remain suspended as colloidally stable liquid microcapsules, as demonstrated by fluorescence correlation spectroscopy. The effective encapsulation of fragrance is proven through confocal Raman spectroscopy, while the structure of the capsules is investigated by means of cryo FIB/SEM, confocal laser scanning microscopy, and small-angle X-ray scattering