Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

© 2017 IOP Publishing Ltd. Objective. This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations. Approach. To reason about possible user goals, a general user-agnostic Bayesian update rule is devised to be recursively applied upon the arrival of evidences, i.e. user input and user gaze. Experiments were conducted with healthy subjects within robotic embodiment settings to evaluate the proposed method. These experiments varied along three factors: the type of the robot/environment (simulated and physical), the type of the interface (keyboard or BCI), and the way goal recognition (GR) is used to guide a simple shared control (SC) driving scheme. Main results. Our results show that the proposed GR algorithm is able to track and infer the hidden user goals with relatively high precision and recall. Further, the realized SC driving scheme benefits from the output of the GR system and is able to reduce the user effort needed to accomplish the assigned tasks. Despite the fact that the BCI requires higher effort compared to the keyboard conditions, most subjects were able to complete the assigned tasks, and the proposed GR system is additionally shown able to handle the uncertainty in user input during SSVEP-based interaction. The SC application of the belief vector indicates that the benefits of the GR module are more pronounced for BCIs, compared to the keyboard interface. Significance. Being based on intuitive heuristics that model the behavior of the general population during the execution of navigation tasks, the proposed GR method can be used without prior tuning for the individual users. The proposed methods can be easily integrated in devising more advanced SC schemes and/or strategies for automatic BCI self-adaptations

Optimal Design of a Trickle Bed Reactor for Light Fuel Oxidative Desulfurization based on Experiments and Modelling

YesIn this work, the performance of oxidative desulfurization (ODS) of dibenzothiophene (DBT) in light gas oil (LGO) is evaluated with a homemade manganese oxide (MnO2/γ-Al2O3) catalyst. The catalyst is prepared by Incipient Wetness Impregnation (IWI) method with air under moderate operating conditions. The effect of different reaction parameters such as reaction temperature, liquid hour space velocity and initial concentration of DBT are also investigated experimentally. Developing a detailed and a validated trickle bed reactor (TBR) process model that can be employed for design and optimization of the ODS process, it is important to develop kinetic models for the relevant reactions with high accuracy. Best kinetic model for the ODS process taking into account hydrodynamic factors (mainly, catalyst effectiveness factor, catalyst wetting efficiency and internal diffusion) and the physical properties affecting the oxidation process is developed utilizing data from pilot plant experiments. An optimization technique based upon the minimization of the sum of the squared error between the experimental and predicted composition of oxidation process is used to determine the best parameters of the kinetic models. The predicted product conversion showed very good agreement with the experimental data for a wide range of the operating condition with absolute average errors less than 5%

Kilonova Luminosity Function Constraints Based on Zwicky Transient Facility Searches for 13 Neutron Star Merger Triggers during O3

We present a systematic search for optical counterparts to 13 gravitational wave (GW) triggers involving at least one neutron star during LIGO/Virgo's third observing run (O3). We searched binary neutron star (BNS) and neutron star black hole (NSBH) merger localizations with the Zwicky Transient Facility (ZTF) and undertook follow-up with the Global Relay of Observatories Watching Transients Happen (GROWTH) collaboration. The GW triggers had a median localization area of 4480 deg², a median distance of 267 Mpc, and false-alarm rates ranging from 1.5 to 10⁻²⁵ yr⁻¹. The ZTF coverage in the g and r bands had a median enclosed probability of 39%, median depth of 20.8 mag, and median time lag between merger and the start of observations of 1.5 hr. The O3 follow-up by the GROWTH team comprised 340 UltraViolet/Optical/InfraRed (UVOIR) photometric points, 64 OIR spectra, and three radio images using 17 different telescopes. We find no promising kilonovae (radioactivity-powered counterparts), and we show how to convert the upper limits to constrain the underlying kilonova luminosity function. Initially, we assume that all GW triggers are bona fide astrophysical events regardless of false-alarm rate and that kilonovae accompanying BNS and NSBH mergers are drawn from a common population; later, we relax these assumptions. Assuming that all kilonovae are at least as luminous as the discovery magnitude of GW170817 (−16.1 mag), we calculate that our joint probability of detecting zero kilonovae is only 4.2%. If we assume that all kilonovae are brighter than −16.6 mag (the extrapolated peak magnitude of GW170817) and fade at a rate of 1 mag day⁻¹ (similar to GW170817), the joint probability of zero detections is 7%. If we separate the NSBH and BNS populations based on the online classifications, the joint probability of zero detections, assuming all kilonovae are brighter than −16.6 mag, is 9.7% for NSBH and 7.9% for BNS mergers. Moreover, no more than 10⁻⁴, or φ > 30° to be consistent with our limits. We look forward to searches in the fourth GW observing run; even 17 neutron star mergers with only 50% coverage to a depth of −16 mag would constrain the maximum fraction of bright kilonovae to <25%

Economic Evaluation of Vampire Bat (\u3ci\u3eDesmodus rotundus\u3c/i\u3e) Rabies Prevention in Mexico

Vampire bat rabies causes significant impacts within its endemic range in Mexico. These impacts include livestock mortality, animal testing costs, post-exposure prophylaxis costs, and human mortality risk. Mitigation of the impacts can be achieved by vaccinating livestock and controlling vampire bat populations. A benefit- cost analysis was performed to examine the economic efficiency of these methods of mitigation, and Monte Carlo simulations were used to examine the impact that uncertainty has on the analysis. We found that livestock vaccination is efficient, with benefits being over six times higher than costs. However, bat control is inefficient because benefits are very unlikely to exceed costs. It is concluded that when these mitigation methods are judged by the metric of economic efficiency, livestock vaccination is desirable but bat control is not

Analysis of the quadrupole deformation of

We present an extraction of the E2/M1 ratio of the Δ(1232) from experimental data applying an effective Lagrangian model. We compare the result obtained with different nucleonic models and we reconcile the experimental results with the lattice QCD calculations