Microstructured air-silica fibres: Recent developments in modelling, manufacturing and experiment

The main modelling methods devoted to microstructured air-silica optical fibres (MOFS) are presented and discussed. Then, the specific propagation properties ofMOFS are studied in detail. Characteristics measured on fibres manufactured in our laboratory or reported in the literature are analysed. A large number of potential and demonstrated applications are presented and the obtained performances are discussed. A particular attention is given to hollow-core photonic bandgap fibres and their applications.     

Polarization independence of a one-dimensional grating in conical mounting

We demonstrate theoretically a polarization-independent guided-mode resonant filter with only a one dimensional grating. A rigorous method, the modal method by Fourier expansion, is used to compute the diffracted efficiencies of the grating. Wave-vector analysis fails to correctly design a polarization-independent structure. We show that a rigorous analysis of the resonances must be employed to obtain such a device; using a pole approach, we study the effects of grating parameters on the resonances of both polarizations.     

Neuromuscular transmission on the rebound

Recent work at the zebrafish neuromuscular junction (NMJ) has shown that positively charged acetylcholine (ACh), at the high concentrations reached in the cleft during neuromuscular transmission, blocks acetylcholine receptors (AChRs) as soon as they open. Thus after two ACh molecules bind and open the channel, a third molecule enters and blocks the pore at a site resembling that for block by local anesthetics, suggesting that ACh is the endogenous anesthetic of the NMJ. Recovery from open channel block results in a rebound synaptic current only after ACh is cleared from the cleft. Kinetic modeling of other AChRs suggests that a rebound current is generated at all vertebrate NMJs, from fish to frogs to mammals. Open channel block prolongs the current at fast zebrafish NMJs in order to more effectively spread charge along the fibers, akin to multiple central synapses spread over dendrites. Together these findings indicate the need for a fundamental revision of current thinking about neuromuscular transmission at many levels, including channel structure, function and pharmacology.     

Constraint based approach for the steady-state simulation of complex systems: Application to ship control

When the steady states are largely predominant with respect to transitional phases, steady-state simulation seems sufficient to predict the behavior of a complex system. Over the past 20 years, different modeling languages and dedicated tools have been developed to improve steady state simulation.In this paper, focus is made on steady-state simulation for system control and design. A model combining an emission sub-model with a ship propulsion sub-model was implemented in a constraint programming (CP) approach. It will help to determine the efficiency (i.e. the ability to model and solve the problem) and complexity of implementation (i.e. difficulties encountered during the implementation) of this approach.First, requirements for the steady-state simulation of complex systems are defined. Then, CP approach is shown to be able to answer these issues through experiments. This approach is then compared to one of the main simulation languages: Modelica.Although the two approaches (i.e Modelica and CP) are able to reverse models, the study shows that the use of Modelica principles for steady-state simulation involves some crippling limitations, such as the non-management of under/over-constrained systems, or inequalities.This study also shows that the constraint programming approach permits to meet some needs for steady-state simulation not yet covered by current approaches.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Adapting groupware systems to changes in the collaborator’s context of use

Owing to the dynamic nature of collaborative environments, the software intended to support collaborative work should adapt itself to the different situations that may occur. This requirement is related to the concept of “context of use”, which has been considered as an important aspect in the design of interactive systems. Nevertheless, two main problems about this concept have been identified by current research in context-aware computing: (1) most of the studies have mainly focused on the context of a single user, so the context of multiple users involved in a common endeavor remains little explored, and (2) adaptability in context-aware systems generally takes into account a reduced number of contextual variables (mainly the user’s location and platform). In this paper, we firstly re-conceptualize the notion of “context of use”, in order to consider the main characteristics of collaborative environments. Based on this new notion, we then design and implement a framework that allows application developers to specify the adaptability of groupware systems in terms of the state of activities, roles, collaborators’ location, available resources, and other typical variables of working groups. This framework has been generalized from scenarios that highlight dynamic situations presented in real collaborative settings. Finally, we validate our proposal by a set of applications that are able to adapt their user interface and functionality, when significant changes are produced in the environment, the working group, and/or the used devices.     

High and very high temperature reactor research for multipurpose energy applications

Ten years ago, the European High Temperature Reactor (HTR) Technology Network (HTR-TN) launched a programme for developing HTR Technology, which expanded so far through 4 successive Euratom Framework Programmes. Many projects have been performed - in particular the RAPHAEL project in the 6th Euratom Framework Programme and presently ARCHER in the 7th - in line with the Network strategy that identified cogeneration of process heat and power as the main specific mission of HTR. HTR can indeed address the growing energy needs of industry presently fully relying on fossil fuel combustion with a CO₂-lean generation technology, thanks to its high operating temperature and to its unique flexibility obtained from its large thermal inertia and its low power.Relying on the legacy of the former European leadership in HTR technology, this programme has addressed specific developments required for industrial process heat applications and for increasing HTR performances (higher temperatures and fuel burn-up). Decisive achievements have been obtained concerning fuel manufacturing and irradiation behaviour, key components and their materials, safety, computer code validation and specific HTR waste (fuel and graphite) management. Key experiments have been performed or are still ongoing: irradiation of graphite, fuel and vessel materials and the corresponding post-irradiation examinations, safety tests and isotopic analyses; thermal-hydraulic tests of an Intermediate Heat Exchanger mock-up in helium; air ingress experiments for a block type core, etc. Through Euratom participation in the Generation IV International Forum (GIF), these achievements contribute to international cooperation.HTR-TN strategy has been recently integrated by the “Sustainable Nuclear Energy Technology Platform” (SNE-TP) as one of the 3 “pillars” of its global nuclear strategy. It is also in line with the orientations and the timing of the “Strategic Energy Technology Plan (SET-Plan)” for the development of CO₂-lean energy technologies, and thus strengthens the nuclear option in a future European energy mix.Nuclear cogeneration for industrial process heat applications is a major innovation and a major challenge, requiring large-scale demonstration to prove its industrial viability. To enable demonstration, it is necessary not only to develop an appropriate nuclear heat source, but also to develop coupling technologies and to adapt industrial processes to the coupling with a HTR. This requires a close partnership between the conventional and the nuclear technology holders as the base of a Nuclear Cogeneration Industrial Initiative.Recently the project EUROPAIRS initiated by HTR-TN together with process heat user industries has set the bases of such a strategic partnership.     

Structure and Reactivity of Spruce Mechanical Pulp Lignins Part I. Bleaching and Photoyellowing of in situ Lignins

This paper reports the structural investigation of a series of in situ spruce lignins in wood, thermomechanical pulp (TMP) and the corresponding bleached (BTMP) and photoyellowed (YBTMP) samples. This was achieved by using an original two-step degradative technique, thioacidolysis followed by Raney nickel desulfuration. The determination of thioacidolysis monomeric and dimeric products allowed an estimate of various lignin building units and interunit bonds, respectively. It was observed that the thermomechanical and bleaching treatments did not affect spruce lignin structure to an appreciable extent. On the contrary, the photoyellowing treatment caused marked structural changes in lignin, particularly when run in severe conditions. These changes were essentially a decrease in the amount of β-O-4 and β-1 interunit bonds and a relative increase in catechol units and in vanillin end-groups.