Yes / Based on the particle-in-cell computing method, a debris mass model has been established to simulate debris flow fan formation over large downstream areas. Under the assumption that the debris medium is an assembly of many small, identical debris particle masses, the overall flowbehavior is obtained by averaging the flow parameters of neighboring debris masses at fixed grid points. The equation of motion for each debris mass is based on the depthaveraged Navier-Stokes equation in two horizontal dimensions. The friction slope of debris flow is modeled by combining the effects of both the liquid phase (slurry composed ofwater and fine particles) modeled as a Bingham fluid and solid phase (coarse particles) in the debris mixture. The rheological parameters are evaluated according to the density and particle size distribution of the debris material. Convergence of the method is demonstrated by repeatedly doubling the number of debris masses employed in the computation until insignificant change is observed. The debris mass model is demonstrated through a prototype application to a documented 1991 debris flowdeposited in the lower reach of the Shawan Ravine inYunnan Province, China. The final alluvial fan was formed by eight consecutive debris flow events, each lasting about 2000 seconds with a discharge rate of 250 m3/s. The simulation results are in good agreement with field observations. The general features of debris fan development and configuration are well predicted.

The observation of solar and atmospheric neutrino oscillations place bounds on the mass differences. However, these probes are insensitive to the absolute mass. To date, cosmology has provided the best bounds on the total neutrino mass. These bounds are based on a degenerate mass model. With the increasing precision of cosmological data, we investigate the effect of the neutrino mass hierarchy. The precision of the parameter estimates stems from precise observations of the cosmic microwave background. However, the effect of neutrino mass hierarchy on this observation is smaller than the cosmic variance. Therefore, we rely on the measurement of the matter power spectrum for hierarchy effects. We propose the use of importance sampling rather than the commonly used Markov chain Monte Carlo. Importance sampling takes advantage of the microwave background's statistical insensitivity to hierarchy. We present forecasted bounds due to Planck and the proposed CMBPol. We also discuss the needed precision for future galaxy surveys in detecting the effect of neutrino mass hierarchy. / text

Yes / Background: Individuals with lower-limb amputation can use running specific prostheses (RSP) that store and then return elastic energy during stance. However, it is unclear whether varying the stiffness category of the same RSP affects spring-mass behaviour during self-selected, submaximal speed running in individuals with unilateral transtibial amputation. Research question: The current study investigates how varying RSP stiffness affects limb stiffness, running performance, and associated joint kinetics in individuals with a unilateral transtibial amputation. Methods: Kinematic and ground reaction force data were collected from eight males with unilateral transtibial amputation who ran at self-selected submaximal speeds along a 15 m runway in three RSP stiffness conditions; recommended habitual stiffness (HAB) and, following 10-minutes of familiarisation, stiffness categories above (+1) and below (-1) the HAB. Stance-phase centre of mass velocity, contact time, limb stiffness’ and joint/RSP work were computed for each limb across RSP stiffness conditions. Results: With increased RSP stiffness, prosthetic limb stiffness increased, whilst intact limb stiffness decreased slightly (p

In this thesis we present a computational study of “ion crystals”, the interesting patterns in which ions arrange themselves in ion traps such as Paul and Penning traps. In ion crystals the ions are in equilibrium due to the balance of the repulsive forces between the ions and the overall tendency of the ion trap to pull ions towards the trap centre. We have carried out a detailed investigation of ion crystals in Paul traps by solving their equations of motion numerically. We also propose a model called the spring–mass model to explain the formation of ion crystals. This model is far more efficient than direct numerical simulation for predicting ion crystal structures. Finally, we demonstrate that there is a power law relating distance of an ion from the trap centre in ion crystals to the applied RF voltage amplitude.

We present a novel control strategy for running which is robust to disturbances, and makes excellent use of passive dynamics for energy economy. The motivation for our control strategy is based on observations of animals, which are able to economically walk and run over varying terrain and ground dynamics. It is well-known that steady-state animal running can be approximated by spring-mass models, but these passive dynamic models describe only steady-state running and are sensitive to disturbances that animals can accommodate. While animals rely on their passive dynamics for energy economy, they also incorporate active control for disturbance rejection. The same approach can be used for spring-mass walking and running, but an active controller is needed that interferes minimally with the passive dynamics of the system. We demonstrate, in simulation, how force control combined with a leg spring stiffness tuned for the desired hopping frequency provides robustness to disturbances on a model for robot hopping, while maintaining the energy economy of a completely passive system during steady-state operation. Our strategy is promising for robotics applications, because there is a clear distinction between the passive dynamic behavior of the model and the active controller, it does not require sensing of the environment, and it is based on a sound theoretical background that is compatible with existing high-level controllers for ideal spring-mass models. / Graduation date: 2012

Inertial measurement units (IMUs) and wearable sensors have enabled athlete monitoring and research to become more ecologically valid due to their small size and low cost. IMUs and accelerometers that are placed on the body close to the point of impact and that record at sufficiently high frequencies have demonstrated the highest validity when measuring temporal gait event moments such as ground contact time (GCT) and flight time (FT) as well as peak forces (PF) during upright running. While the use of IMUs has increased in the sport performance and athlete monitoring realm, the potential of the technology’s ability to estimate running force-time curves utilizing the two-mass model (TMM) remains unexplored. The purpose of this study was two-fold. First, was to determine the validity of measuring temporal gait events and peak forces utilizing a commercially available shank-mounted inertial measurement unit. Second, was to determine the validity of force-time curves generated from the TMM utilizing data from shank-mounted inertial measurement units. Ten subjects voluntarily completed submaximal treadmill tests equipped with a force plate while wearing shank-mounted IMUs on each leg. Using the raw data from the IMUs, GCT, FT, total step time (ST), PF, and two-mass model-based force-time (F-t) curves were generated for 25 steps at 8 different speeds. Paired sample T-tests were performed on the gait events and peak force between the IMU and treadmill with both individual step comparison and averages per each speed. 95% confidence intervals were calculated for each timepoint of the force time curves. No statistically significant differences (p > 0.05) and nearly perfect relationships were observed for the step averages for each speed with FT, ST, and PF. Confidence intervals of the corrected mean difference suggest that F-t curves calculated from the TMM may not be valid when assessing the running population as a whole. When performing a sub-group analysis of skilled runners and recreational runners, F-t curves derived from shank-mounted IMUs may be more valid in skilled runners than recreational runners. In skilled runners, the 95% CI for the mean difference contained zero within the first 60% of the GCT duration, whereas the 95% CI recreational runners contained a zero-value in a smaller percentage of the GCT located only in the middle of the GCT at the curve peak height. The results of this study suggest that interchangeability between shank-mounted IMUs and force plates may be very limited when estimating temporal gait events and kinetics. While agreement was low between F-t curves after the peak in skilled runners, use of shank-mounted IMUs to estimate F-t curves may have several benefits still in skilled runners when assessing peak forces and force development from initial contact until peak force.

No description available.

Every movement has a goal. For reaching, the goal is to move the hand to a specific location. For locomotion, however, goals for each step cycle are unclear and veiled by the automatic nature of lower limb control. What mechanical variables does the nervous system "care" about during locomotion? Abundant evidence from the biomechanics literature suggests that the force generated on the ground, or endpoint force, is an important task variable during hopping and running. Hopping and running are called bouncing gaits for the reason that the endpoint force trajectory is like that of bouncing on a pogo stick. In this work, I captured kinematics and kinetics of human bouncing gaits, and tested whether structure in the inherent step-to-step variability is consistent with control of endpoint force. I found that joint torques covary from step to step to stabilize only peak force. When two limbs are used to generate force on the ground at the same time, individual forces of the limbs are not stabilized, but the total peak force is stabilized. Moreover, passive dynamics may be exploited during forward progression. These results suggest that the number of kinetic goals is minimal, and this simple control scheme involves goals for discrete times during the gait cycle. Uncovering biomechanical goals of locomotion provides a functional context for understanding how complex joints, muscles, and neural circuits are coordinated.

In this work, we propose an approach that allows to explore the potential pathophysiological mechanisms (at neuronal population level) of ictogenesis by combining clinical intracranial electroencephalographic (iEEG) recordings with a neural mass model. IEEG recordings from temporal lobe epilepsy (TLE) patients around seizure onset were investigated. Physiologically meaningful parameters (average synaptic gains of the excitatory, slow and fast inhibitory population, Ae, B and G) were identified during interictal to ictal transition. We analyzed the temporal evolution of four ratios, i.e. Ae/G, Ae/B, Ae/(B + G), and B/G. The excitation/inhibition ratio increased around seizure onset and decreased before seizure offset, suggesting the disturbance and restoration of balance between excitation and inhibition around seizure onset and before seizure offset, respectively. Moreover, the slow inhibition may have an earlier effect on the breakdown of excitation/inhibition balance. Results confirm the decrease in excitation/inhibition ratio upon seizure termination in human temporal lobe epilepsy, as revealed by optogenetic approaches both in vivo in animal models and in vitro. We further explored the distribution of the average synaptic gains in parameter space and their temporal evolution, i.e. the path through the model parameter space, in TLE patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during ictal and returned when the seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from individual patients. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing an identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy. The increase of excitation/inhibition ratios, i.e. Ae/G, Ae/B and Ae/(B+G), around seizure onset makes them potential cues for seizure detection. We explored the feasibility of a model based seizure detection algorithm. A simple thresholding method was employed. We evaluated the algorithm against the manual scoring of a human expert on iEEG samples from patients suffering from different types of epilepsy. Results suggest that Ae/(B+G), i.e. excitation/(slow + fast inhibition) ratio, allowed the best performance and that the algorithm best suited TLE patients. Leave-one-out cross-validation showed that the algorithm achieved 94.74% sensitivity for TLE patients. The median false positive rate was 0.16 per hour, and median detection delay was -1.0 s. Of interest, the values of the threshold determined by leave-one-out cross-validation for TLE patients were quite constant, suggesting a general excitation/inhibition balance baseline in background iEEG among TLE patients. Such a model-based seizure detection approach is of clinical interest and could also achieve good performance for other types of epilepsy provided that more appropriate model, i.e. better describe epileptic EEG waveforms for other types of epilepsy, is implemented. Altogether, this thesis contributes to the field of epilepsy research from two perspectives. Scientifically, it gives new insights into the mechanisms underlying interictal to ictal transition, and facilitates better understanding of epileptic seizures. Clinically, it provides a tool for reviewing EEG data in a more efficient and objective manner and offers an opportunity for on-demand therapeutic devices. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

L'un des enjeux majeurs actuellement en neurosciences est l'élaboration de modèles computationnels capables de reproduire les données obtenues expérimentalement par des méthodes d'imagerie et permettant l'étude de la relation structure-fonction dans le cerveau. Les travaux de modélisation dans cette thèse se situent à deux échelles et l'analyse des modèles a nécessité le développement d'outils théoriques et numériques dédiés. À l'échelle locale, nous avons proposé un nouveau modèle d'équations différentielles ordinaires générant des activités neuronales, caractérisé et classifié l'ensemble des comportements générés, comparé les sorties du modèle avec des données expérimentales et identifié les structures dynamiques sous-tendant la génération de comportements pathologiques. Ce modèle a ensuite été couplé bilatéralement à un nouveau compartiment modélisant les dynamiques de neuromédiateurs et leurs rétroactions sur l'activité neuronale. La caractérisation théorique de l'impact de ces rétroactions sur l'excitabilité a été obtenue en formalisant l'étude des variations d'une valeur de bifurcation en un problème d'optimisation sous contrainte. Nous avons enfin proposé un modèle de réseau, pour lequel la dynamique des noeuds est fondée sur le modèle local, incorporant deux couplages: neuronal et astrocytaire. Nous avons observé la propagation d'informations différentiellement selon ces deux couplages et leurs influences cumulées, révélé les différences qualitatives des profils d'activité neuronale et gliale de chaque noeud, et interprété les transitions entre comportements au cours du temps grâce aux structures dynamiques identifiées dans les modèles locaux. / A current issue in neuroscience is to elaborate computational models that are able to reproduce experimental data recorded with various imaging methods, and allowing us to study the relationship between structure and function in the human brain. The modeling objectives of this work are two scales and the model analysis need the development of specific theoretical and numerical tools. At the local scale, we propose a new ordinary differential equations model generating neuronal activities. We characterize and classify the behaviors the model can generate, we compare the model outputs to experimental data and we identify the dynamical structures of the neural compartment underlying the generation of pathological patterns. We then extend this approach to a new neuro-glial mass model: a bilateral coupling between the neural compartment and a new one modeling the impact of astrocytes on neurotransmitter concentrations and the feedback of these concentrations on neural activity is developed. We obtain a theoretical characterization of these feedbacks impact on neuronal excitability by formalizing the variation of a bifurcation value as a problem of optimization under constraint. Finally, we propose a network model, which node dynamics are based on the local neuro-glial mass model, embedding a neuronal coupling and a glial one. We numerically observe the differential propagations of information according to each of these coupling types and their cumulated impact, we highlight qualitatively distinct patterns of neural and glial activities of each node, and link the transitions between behaviors with the dynamical structures identified in the local models.