Spiking Chemical Sensor (SCS): A new platform for neuro-chemical sensing

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Dissipation of mechanical work and temperature rise in AS4/PEEK thermoplastic composite

The dissipated mechanical work per cycle of sinusoidal stress in the thermoplastic composite material AS4/PEEK was measured as a function of stress amplitude for fixed frequency and fiber orientation. The experimental result shows that the dissipated work per cycle is proportional to the square of the stress amplitude. Using the concept of the equivalent isotropic material, it is shown that the relaxation modulus satisfies a proportionality condition. Also, the rate of temperature rise due to sinusoidal stresses has been measured as a function of stress amplitude. The result shows that the rate of temperature rise is not proportional to the square of the stress amplitude

Solvable non-Markovian dynamic network

Non-Markovian processes are widespread in natural and human-made systems, yet explicit modeling and analysis of such systems is underdeveloped. We consider a non-Markovian dynamic network with random link activation and deletion (RLAD) and heavy-tailed Mittag-Leffler distribution for the interevent times. We derive an analytically and computationally tractable system of Kolmogorov-like forward equations utilizing the Caputo derivative for the probability of having a given number of active links in the network and solve them. Simulations for the RLAD are also studied for power-law interevent times and we show excellent agreement with the Mittag-Leffler model. This agreement holds even when the RLAD network dynamics is coupled with the susceptible-infected-susceptible spreading dynamics. Thus, the analytically solvable Mittag-Leffler model provides an excellent approximation to the case when the network dynamics is characterized by power-law-distributed interevent times. We further discuss possible generalizations of our result

Association of radiation belt electron enhancements with earthward penetration of Pc5 ULF waves: a case study of intense 2001 magnetic storms

Geospace magnetic storms, driven by the solar wind, are associated with increases or decreases in the fluxes of relativistic electrons in the outer radiation belt. We examine the response of relativistic electrons to four intense magnetic storms, during which the minimum of the Dst index ranged from −105 to −387 nT, and compare these with concurrent observations of ultra-low-frequency (ULF) waves from the trans-Scandinavian IMAGE magnetometer network and stations from multiple magnetometer arrays available through the worldwide SuperMAG collaboration. The latitudinal and global distribution of Pc5 wave power is examined to determine how deep into the magnetosphere these waves penetrate. We then investigate the role of Pc5 wave activity deep in the magnetosphere in enhancements of radiation belt electrons population observed in the recovery phase of the magnetic storms. We show that, during magnetic storms characterized by increased post-storm electron fluxes as compared to their pre-storm values, the earthward shift of peak and inner boundary of the outer electron radiation belt follows the Pc5 wave activity, reaching L shells as low as 3–4. In contrast, the one magnetic storm characterized by irreversible loss of electrons was related to limited Pc5 wave activity that was not intensified at low L shells. These observations demonstrate that enhanced Pc5 ULF wave activity penetrating deep into the magnetosphere during the main and recovery phase of magnetic storms can, for the cases examined, distinguish storms that resulted in increases in relativistic electron fluxes in the outer radiation belts from those that did not

The mathematics of human contact: Developing a model for social interaction in school children

In this paper, we provide a statistical analysis of high-resolution contact pattern data within primary and secondary schools as collected by the SocioPatterns collaboration. Students are graphically represented as nodes in a temporally evolving network, in which links represent proximity or interaction between students. This article focuses on link- and node-level statistics, such as the on- and off-durations of links as well as the activity potential of nodes and links. Parametric models are fitted to the on- and off-durations of links, inter-event times and node activity potentials and, based on these, we propose a number of theoretical models that are able to reproduce the collected data within varying levels of accuracy. By doing so, we aim to identify the minimal network-level properties that are needed to closely match the real-world data, with the aim of combining this contact pattern model with epidemic models in future work

Robust moving horizon state estimation for uncertain linear systems using linear matrix inequalities

This paper investigates the problem of state estimation for linear-time-invariant (LTI) discrete-time systems subject to structured feedback uncertainty and bounded disturbances. The proposed Robust Moving Horizon Estimation (RMHE) scheme computes at each sample time tight bounds on the uncertain states by solving a linear matrix inequality (LMI) optimization problem based on the available noisy input and output data. In comparison with conventional approaches that use offline calculation for the estimation, the suggested scheme achieves an acceptable level of performance with reduced conservativeness, while the online computational time is maintained relatively low. The effectiveness of the proposed estimation method is assessed via a numerical example

Tracking control for directional drilling systems using robust feedback model predictive control

A rotary steerable system (RSS) is a drilling technology which has been extensively studied and used for over the last 20 years in hydrocarbon exploration and it is expected to drill complex curved borehole trajectories. RSSs are commonly treated as dynamic robotic actuator systems, driven by a reference signal and typically controlled by using a feedback loop control law. However, due to spatial delays, parametric uncertainties and the presence of disturbances in such an unpredictable working environment, designing such control laws is not a straightforward process. Furthermore, due to their inherent delayed feedback, described by delay differential equations (DDE), directional drilling systems have the potential to become unstable given the requisite conditions. This paper proposes a Robust Model Predictive Control (RMPC) scheme for industrial directional drilling, which incorporates a simplified model described by ordinary differential equations (ODE), taking into account disturbances and system uncertainties which arise from design approximations within the formulation of RMPC. The stability and computational efficiency of the scheme are improved by a state feedback strategy computed offline using Robust Positive Invariant (RPI) sets control approach and model reduction techniques. A crucial advantage of the proposed control scheme is that it computes an optimal control input considering physical and designer constraints. The control strategy is applied in an industrial directional drilling configuration represented by a DDE model and its performance is illustrated by simulations