Random access parallel microscopy

We introduce a random-access parallel (RAP) imaging modality that uses a novel design inspired by a Newtonian telescope to image multiple spatially separated samples without moving parts or robotics. This scheme enables near-simultaneous image capture of multiple petri dishes and random-access imaging with sub-millisecond switching times at the full resolution of the camera. This enables the RAP system to capture long-duration records from different samples in parallel, which is not possible using conventional automated microscopes. The system is demonstrated by continuously imaging multiple cardiac monolayer and Caenorhabditis elegans preparations.

Dinamika Reaktor Katalitik Aliran Bolak-Balik untuk Oksidasi Emisi Gas Metana

The performance of a reverse flow reactor (RFR) is strongly influenced by the switching time used to alternate the flow direction. This research aimed to study the effect of the switching time on reactor dynamics including the heat propagation along the bed and reaction rate in the oxidation methane for low concentration using catalytic reverse flow reactor. The experimental results show that the reverse flow operating mode can influence heat propagation along the reactor and reaction conversion. Based on the three switching times was tested, the temperature dynamics formed were in the sliding regime. The effect of switching time on RFR on conversion is very significant. When compared to steady operation, RFR operation provides the highest conversion at smaller switching times. At large switching times, the effect of reversal of flow direction becomes less dominant and reactor behavior approaches steady state.

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.