The Role of Premotor Areas in Dual Tasking in Healthy Controls and Persons With Multiple Sclerosis: An fNIRS Imaging Study

Persons with multiple sclerosis (pwMS) experience declines in physical and cognitive abilities and are challenged by dual-tasks. Dual-tasking causes a drop in performance, or what is known as dual-task cost (DTC). This study examined DTC of walking speed (WS) and cognitive performance (CP) in pwMS and healthy controls (HCs) and the effect of dual-tasking on cortical activation of bilateral premotor cortices (PMC) and bilateral supplementary motor area (SMA). Fourteen pwMS and 14 HCs performed three experimental tasks: (1) single cognitive task while standing (SingCog); (2) single walking task (SingWalk); and (3) dual-task (DualT) that included concurrent performance of the SingCog and SingWalk. Six trials were collected for each condition and included measures of cortical activation, WS and CP. WS of pwMS was significantly lower than HC, but neuropsychological (NP) measures were not significantly different. pwMS and HC groups had similar DTC of WS, while DTC of CP was only significant in the MS group; processing speed and visual memory predicted 55% of this DTC. DualT vs. SingWalk recruited more right-PMC activation only in HCs and was associated with better processing speed. DualT vs. SingCog recruited more right-PMC activation and bilateral-SMA activation in both HC and pwMS. Lower baseline WS and worse processing speed measures in pwMS predicted higher recruitment of right-SMA (rSMA) activation suggesting maladaptive recruitment. Lack of significant difference in NP measures between groups does not rule out the influence of cognitive factors on dual-tasking performance and cortical activations in pwMS, which might have a negative impact on quality of life

Behavior-Based Information Seeking in Digital Libraries. Search Optimization in the Humboldt Digital Library

Digital libraries are providing an increasing amount of data, which is normally structured in a classical way by documents and described by metadata as keywords. The data, even in scientific systems such as digital libraries and virtual research environments, will contain a great amount of noise or information unnecessary for our personal interests. Although there has been a lot of progress in the field of information retrieval, search techniques and other content finding methods, there is still much to be done in the field of information retrieval based on user behavior. This paper presents an approach deployed in the Humboldt Digital Library (HDL) to facilitate the retrieval of relevant information to the users of the system, making recommendations of paragraphs based on their profile and the behavior of other users who share similar profiles. The Humboldt digital library represents an innovative system of open access to the legacy of Alexander von Humboldt in a digital form on the Internet (www.avhumboldt.net). It contributes to the key question, how to present interconnected data in a proper form using information technologies

Dataset used in Defining Digital Libraries

<p>This dataset is part of a research paper on the definition of digital libraries. <br>Even though we claim that this list provides an exhaustive coverage of works that contain definitions of digital library, we are aware that potentially there can be other definitions which could be appended.</p

Kriging-based optimization design of deep tunnel in the rheological Burger rock

International audienceAbstract The principal purpose of this work consists in optimizing the support system of a deep tunnel accounting for the uncertainty of the time-dependent behaviour of the surrounding rock, which is described by the rheological Burger law. The stochastic approach is chosen for this aim. On one hand the Quantile Monte Carlo (QMC) simulation is used to determine the optimal design variables (i.e., the thickness of two liners). On the other hand, the well-known Kriging metamodeling technique is undertaken to approximate the limit state function in the augmented reliability space (i.e., the tensor product between the random variable space and the design variable space). The adopted optimization process allows to derive the optimal tunnel support that verifies two failure modes, namely the support capacity criterion and the maximum tunnel convergence

Kriging-based reliability analysis of the long-term stability of a deep drift constructed in the Callovo-Oxfordian claystone

International audienc

Effect of spatial variability of creep rock on the stability of a deep double-lined drift

International audienceThis work aims at investigating the effect of aleatoric uncertainty of creep rock properties on the stability of an underground structure. This uncertainty relates to the spatial variability of the mechanical parameters representing the time-dependent behavior of geological rock formation due to the change in its mineralogy. The chosen methodology consists of representing the aleatoric uncertainty of rock properties by random fields, written as correlation functions with respect to the spatial correlation length. The adaptation of the well-known Expansion Optimal Linear Estimationmethod (EOLE) is performed to account for the cross-correlation of the random fields of the viscoplastic parameters of the host rock. Then, the Kriging-based reliability analysis is undertaken with respect to the discretized random fields, which allows elucidating the effect of spatial variability. As an application, the proposed approach is chosen to study the stability in the long-term of a deep double-lined drift within the geological disposal facilities (Cigeo project) conducted by the French National Radioactive Waste Management Agency (Andra). The drift will be excavated in Callovo-Oxfordian (COx) claystone (if the Cigeo project is licensed), considered as a potential host rock for the deep geological nuclear waste disposal in France. The results show that the chosen Kriging metamodel for the reliability analysis can be appropriate for the case of high correlation length represented by a moderate number of random variables (up to about 50) after the discretization of random fields. Further, the consideration of aleatoric uncertainty exhibits a lower probability of exceedance in comparison with the case where spatial variability is ignored. Still, more investigations need to be conducted in the future to conclude this observation

Time-Dependent Behavior of Callovo-Oxfordian Claystone for Nuclear Waste Disposal: Uncertainty Quantification from In-Situ Convergence Measurements

The sustainability of geotechnical infrastructures is closely linked with their long-time behavior. In fact, there is not a straightforward procedure to predict this behavior, and very often, the back analyses of observed data are the best tool to understand their long-time response. In-situ observations of drifts constructed in the Callovo-Oxfordian (COx) claystone, the potential host formation for geological radioactive waste disposal, in France exhibit a progressive convergence. These convergence measurements with quite significant dispersions reveal a considerable uncertainty of time-dependent behavior of this argillaceous rock that can strongly affect the transmit loading to liners, hence the long term stability of the drift. Consequently, the uncertain quantification of the creep behavior of COx claystone presents an important task before analyzing the safety of the waste disposal system. In this work, this challenge was conducted by using the well-known Bayesian inference technique. For this aim, on the one hand, the effectiveness of the classical and hierarchical Bayesian techniques to quantify the epistemic and aleatoric uncertainties of the time-dependent behavior of the host rock were investigated using synthetic data. On the other hand, we dealt with the uncertain quantification of the Lemaitre parameters that characterize the visco-plastic behavior of COx claystone thanks to the real data of in-situ convergence measurements of drifts

Modified AK-MCS method and its application on the reliability analysis of underground structures in the rock mass

This work aims at proposing the methodology on the basis of the extension of the famous reliability analysis, joining the Kriging and Monte Carlo Simulation (AK-MCS) metamodeling technique for analyzing the long-term stability of deep tunnel support constituted by two layers (a concrete liner covered with a compressible layer). A novel active learning function for selecting new training points enriches the Design of Experiment (DoE) of the built surrogate. This novel learning function, combined with an appropriate stopping criterion, improves the original AK-MCS method and significantly reduces the number of calls to the performance function. The efficiency of this modified AK-MCS method is demonstrated through two examples (a well-known academic problem and the case of a deep tunnel dug in the rock working viscoelastic Burgers model). In these examples, we illustrate the accuracy and performance of our method by comparing it with direct MCS and well-known Kriging metamodels (i.e., the classical AK-MCS and EGRA methods)

Time-Dependent Behavior of Callovo-Oxfordian Claystone for Nuclear Waste Disposal: Uncertainty Quantification from In-Situ Convergence Measurements

The sustainability of geotechnical infrastructures is closely linked with their long-time behavior. In fact, there is not a straightforward procedure to predict this behavior, and very often, the back analyses of observed data are the best tool to understand their long-time response. In-situ observations of drifts constructed in the Callovo-Oxfordian (COx) claystone, the potential host formation for geological radioactive waste disposal, in France exhibit a progressive convergence. These convergence measurements with quite significant dispersions reveal a considerable uncertainty of time-dependent behavior of this argillaceous rock that can strongly affect the transmit loading to liners, hence the long term stability of the drift. Consequently, the uncertain quantification of the creep behavior of COx claystone presents an important task before analyzing the safety of the waste disposal system. In this work, this challenge was conducted by using the well-known Bayesian inference technique. For this aim, on the one hand, the effectiveness of the classical and hierarchical Bayesian techniques to quantify the epistemic and aleatoric uncertainties of the time-dependent behavior of the host rock were investigated using synthetic data. On the other hand, we dealt with the uncertain quantification of the Lemaitre parameters that characterize the visco-plastic behavior of COx claystone thanks to the real data of in-situ convergence measurements of drifts