High resolution electrical characterisation of organic photovoltaic blends

In this work, electrostatic force microscopy (EFM) and conductive atomic force microscopy (C-AFM) are applied to perform high-resolution electrical characterisation of organic photovoltaic films. These films are composed of the C₆₀-derivative PCBM blended with hole conductive conjugated polymers PPV derivatives or P3HT. It is demonstrated that both EFM and C-AFM are able to electrically evidence phase separation in the blends, suggesting in addition higher density of carriers along interfaces. Correlation between the EFM contrast and the photovoltaic properties of the blends was observed. Local spectroscopy (I-V curves) completes the C-AFM investigations, analysing charge transport mechanisms in the P3HT:PCBM blend. Significant modifications of the local electrical properties of P3HT are shown to occur upon blending. Space charge limited current is evidenced in the blend and a hole mobility of 1.7 × 10⁻² cm² V⁻¹ s⁻¹ is determined for P3HT.     

Community research activities in secure and trustworthy ICT infrastructures

Secure and trusted Information and Communication Technology (ICT) infrastructures are key enablers for the development of a trustworthy Information Society. The European Union, recognising this, has launched over the last few years a number of research initiatives aiming at designing and building secure and dependable ICT systems and networks, which respect citizens’ rights and protect their privacy and personal data. The European Commission is already funding more than 37 R&D projects in this area under the IST programme, which is part of the 6th Framework Programme (2002 to 2006). The paper first presents the main research challenges in the development of secure, dependable and trusted ICT infrastructures. It then describes a representative set of these IST projects dealing with advanced research in network and service security. The paper also presents upcoming opportunities for research funding in this area under the newly launched ICT programme, part of the 7th Framework Programme that extends from 2007 to 2013.     

Accelerated testing of on‐board diagnostics

Modern products frequently feature monitors designed to detect actual or impending malfunctions. False alarms (Type I errors) or excessive delays in detecting real malfunctions (Type II errors) can seriously reduce monitor utility. Sound engineering practice includes physical evaluation of error rates. Type II error rates are relatively easy to evaluate empirically. However, adequate evaluation of a low Type I error rate is difficult without using accelerated testing concepts, inducing false alarms using artificially low thresholds and then selecting production thresholds by appropriate extrapolation, as outlined here. This acceleration methodology allows for informed determination of detection thresholds and confidence in monitor performance with substantial reductions over current alternatives in time and cost required for monitor development. Copyright © 2006 John Wiley & Sons, Ltd.     

Platform ontologies for the model-driven architecture

The model-driven architecture enables the deployment of software applications on different platforms. It is based on a pattern in which a platform-independent model is transformed into a platform-specific model, given a platform model (PM). Currently, the model transformations used for this task implicitly assume this PM, which makes it unclear whether a model transformation can be used for platforms other than the one assumed. In order to target very specific platforms and platform variants, model transformations must be reusable beyond their assumed platform. We propose an explicit PM that can be used to reason about platform dependencies of model transformations and their applicability to specific platforms. In addition, we show how PMs can be integrated in a model-driven configuration management framework.     

Quantification of Protein‐Induced Membrane Remodeling Kinetics In Vitro with Lipid Multilayer Gratings

The dynamic self‐organization of lipids in biological systems is a highly regulated process that enables the compartmentalization of living systems at micro‐ and nanoscopic scales. Consequently, quantitative methods for assaying the kinetics of supramolecular remodeling such as vesicle formation from planar lipid bilayers or multilayers are needed to understand cellular self‐organization. Here, a new nanotechnology‐based method for quantitative measurements of lipid–protein interactions is presented and its suitability for quantifying the membrane binding, inflation, and budding activity of the membrane‐remodeling protein Sar1 is demonstrated. Lipid multilayer gratings are printed onto surfaces using nanointaglio and exposed to Sar1, resulting in the inflation of lipid multilayers into unilamellar structures, which can be observed in a label‐free manner by monitoring the diffracted light. Local variations in lipid multilayer volume on the surface is used to vary substrate availability in a microarray format. A quantitative model is developed that allows quantification of binding affinity (K _D) and kinetics (k_on and k_off). Importantly, this assay is uniquely capable of quantifying membrane remodeling. Upon Sar1‐induced inflation of single bilayers from surface supported multilayers, the semicylindrical grating lines are observed to remodel into semispherical buds when a critical radius of curvature is reached.     

Intelligent Collectives: Theory,Applications, and Research Challenges

ABSTRACT

Nowadays, along with the rapid development of information technologies, very often solving a common problem is entrusted to many autonomous units (people, systems). With such an approach, one can tap into the so-called collective intelligence (CI)—emerging from the collaboration and competition of many individuals. In this paper, we present recent research on CI related to the effectiveness of using the wisdom of crowds to perform a wide range of problems. For this aim, we first introduce a general framework of CI involving key characteristics of intelligent collectives. Next, we focus on the problem of how diversity and collective cardinality influence collective performance. Then, its applications, which are widely used such as prediction markets and Delphi method, will be presented. Furthermore, some research challenges on the capacity of combining CI with other research fields such as machine learning and social networks are also discussed.     

Sourcing strategies in supply risk management: An approximate dynamic programming approach

In recent years, supply chains have become increasingly globalized. As a consequence, the world's supply of all types of parts has become more susceptible to disruptions. Some of these disruptions are extreme and may have global implications. Our research is based on the supply risk management problem faced by a manufacturer. We model the problem as a dynamic program, design and implement approximate dynamic programming (ADP) algorithms to solve it, to overcome the well-known curses of dimensionality. Using numerical experiments, we compare the performance of different ADP algorithms. We then design a series of numerical experiments to study the performance of different sourcing strategies (single, dual, multiple, and contingent sourcing) under various settings, and to discover insights for supply risk management practice. The results show that, under a wide variety of settings, the addition of a third or more suppliers brings much less marginal benefits. Thus, managers can limit their options to a backup supplier (contingent sourcing) or an additional regular supplier (dual sourcing). Our results also show that, unless the backup supplier can supply with zero lead time, using dual sourcing appears to be preferable. Lastly, we demonstrate the capability of the proposed method in analyzing more complicated realistic supply chains.     

What stimulates teachers to integrate ICT in their pedagogical practices? The use of digital learning materials in education

The question ‘What stimulates teachers to integrate Information and Communication Technologies (ICTs) in their pedagogical practices?’ was addressed in the context of teachers’ usage of digital learning materials (DLMs). We adopted Fishbein’s Integrative Model of Behavior Prediction (IMBP), to investigate the various relationships between distal and proximal variables and intention. Mediation analysis revealed that the proximal variables attitude, subjective norm, and self-efficacy towards DLMs were significant predictors of teachers’ intention to use DLMs. The contribution of subjective norm, however, was modest. Attitude, subjective norm and self-efficacy mediated the effects of the following three distal variables on intention: previous use of DLMs, perceived knowledge and skills to use DLMs, and colleagues’ usage of DLMs. Persuasive communication and skills based training seem, therefore, appropriate interventions to promote a positive attitude towards DLMs and improve self-efficacy in using DLMs.     

Joint training of non-negative Tucker decomposition and discrete density hidden Markov models

Non-negative Tucker decomposition (NTD) is applied to unsupervised training of discrete density HMMs for the discovery of sequential patterns in data, for segmenting sequential data into patterns and for recognition of the discovered patterns in unseen data. Structure constraints are imposed on the NTD such that it shares its parameters with the HMM. Two training schemes are proposed: one uses NTD as a regularizer for the Baum–Welch (BW) training of the HMM, the other alternates between initializing the NTD with the BW output and vice versa. On the task of unsupervised spoken pattern discovery from the TIDIGITS database, both training schemes are observed to improve over BW training in terms of pattern purity, accuracy of the segmentation boundaries and accuracy for speech recognition. Furthermore, we experimentally observe that the alternative training of NTD and BW outperforms the NTD regularized BW, BW training and BW training with simulated annealing.