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An important aspect in safety–critical domains is Situational Awareness (SA) where operators consolidate data into an understanding of the situation that needs to be updated dynamically as the situation changes over time. Among existing measures of SA, only physiological measures can assess the cognitive processes associated with SA in real-time. Some studies showed promise in detecting cognitive states associated with SA in complex tasks using brain signals (e.g. electroencephalogram/EEG). In this paper, an analytical methodology is proposed to identify EEG signatures associated with SA on various regions of the brain. A new data set from 32 participants completing the SA test in the PEBL is collected using a 32-channel dry-EEG headset. The proposed method is tested on the new data set and a correlation is identified between the frequency bands of β ( 12 - 30 Hz ) and γ ( 30 - 45 Hz ) and SA. Also, activation of neurons in the left and right hemisphere of the parietal and temporal lobe is observed. These regions are responsible for the visuo-spatial ability and memory and reasoning tasks. Among the presented results, the highest achieved accuracy on test data is 67 % .

The Nyquist–Shannon criterion indicates the sample rate necessary to identify information with particular frequency content from a dynamical system. However, in experimental applications such as the interrogation of a flow field using particle image velocimetry (PIV), it may be impracticable or expensive to obtain data at the desired temporal resolution. To address this problem, we propose a new approach to identify temporal information from undersampled data, using ideas from modal decomposition algorithms such as dynamic mode decomposition (DMD) and optimal mode decomposition (OMD). The novel method takes a vector-valued signal, such as an ensemble of PIV snapshots, sampled at random time instances (but at sub-Nyquist rate) and projects onto a low-order subspace. Subsequently, dynamical characteristics, such as frequencies and growth rates, are approximated by iteratively approximating the flow evolution by a low-order model and solving a certain convex optimisation problem. The methodology is demonstrated on three dynamical systems, a synthetic sinusoid, the cylinder wake at Reynolds number $Re=60$ and turbulent flow past the axisymmetric bullet-shaped body. In all cases the algorithm correctly identifies the characteristic frequencies and oscillatory structures present in the flow.

Large majority of control methodologies used in traffic applications require short-time prediction of the environment. For instance, in widely-used Model Predictive Control [1] employed to reduce fuel and energy consumption of vehicles in a platoon, information about future velocity profiles of leading vehicles is necessary. In such case, the dynamic model should provide information more detailed than prediction of averaged and global quantities. Additionally, if the control input is to be applied at high-frequencies, traffic model must be solved in a short period of time.We propose a novel framework which addresses aforementioned problems by estimating the vehicle velocity at any location in the domain based on the real-time information from induction loops downstream. Additionally, our formulation is linear and low-dimensional (i.e. consists of few degrees of freedom) meaning that the estimation can be executed at high frequencies. First a mapping is constructed from velocities at discrete locations to the smooth continuous field, which is subsequently projected onto its most significant principal components. Next, current state of such system is estimated using Kalman filter by combining the linear, wave-like dynamics of the traffic with the instantaneous information provided by induction loops. Short-term traffic prediction is then achieved by integration of the model forward in time.The proxy methodology is validated using SUMO simulation on the test case of the vehicles approaching a traffic junction. The performance is evaluated based on sampling reconstructed continuous waveform at the locations and timestamps of the vehicles in the reference data and calculating velocity errors. Separate cases are considered where drivers follow Intelligent Driver Model perfectly and with varying levels of uncertainty.

Identifying coherent structures in fluid flows is of great importance for reduced order modelling and flow control. However, extracting such structures from experimental or numerical data obtained from a turbulent flow can be challenging. A number of modal decomposition algorithms have been proposed in recent years which decompose time-resolved snapshots of data into spatial modes, each associated with a single frequency and growth-rate. Most prominently among them is dynamic mode decomposition (DMD). However, DMD-like algorithms create an arbitrary number of modes. It is common practice to then choose a smaller subset of these modes, for the purpose of model reduction and analysis, based on some measure of significance. In this work, we present a method of post-processing DMD modes for extracting a small number of dynamically relevant modes. We achieve this through an iterative approach based on the graph-theoretic notion of maximal cliques to identify clusters of modes and representing each cluster with a single representative mode.

By the reaction of hydrazides of 4-(4-halogenophenyl)-4H-1,2,4-triazol-3-yl-sulfanyl acetic acid with isothiocyanate, 1-acyl-4-substituted thiosemicarbazide derivatives (7–19) were obtained. The cyclization of compounds (7–19) in the presence of 2% NaOH led to the formation of compounds (20–26) containing two 1,2,4-triazole rings connected by a methylenesulfanyl group. The new compounds were tested for their in vitro antimicrobial activity. Some of the tested compounds (9, 12, 18, 21, 22) showed activity against the reference strains of Gram-positive bacteria with the MIC (minimal inhibitory concentration) = 125 to >1000 μg/mL. © 2011 Wiley Periodicals, Inc. Heteroatom Chem 23:117–121, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/hc.20758

The compounds (III) and (IV) are screened for their antimicrobial activity against Gram-positive and Gram-negative bacteria.