| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Qu, L. X., et al.
(författare)
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Quantitative numerical analysis of flow past a circular cylinder at Reynolds number between 50 and 200
- 2013
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Ingår i: Journal of Fluids and Structures. - : Elsevier BV. - 1095-8622 .- 0889-9746. ; 39, s. 347-370
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Tidskriftsartikel (refereegranskat)abstract
- Results of numerical simulations are presented for flow past a stationary circular cylinder at low Reynolds numbers (Re=50-200). The simulations were carried out using a finite-volume code employing a fractional step method with second-order accuracy in both space and time. A sensitivity study on numerical parameters concerning the domain size, grid independence and time step resolution was carried out in detail for Re=100. Global time-averaged results on force coefficients, non-dimensional velocities and pressures, including their corresponding r.m.s. values, as well as various quantities related to the separation and vortex shedding characteristics are presented. A non-monotonous streamwise velocity recovery in the intermediate wake is observed for Re > 50, a phenomenon that has been grossly overlooked in the past. There are two plateaus along the wake centerline, in particular for Re=200. The first, which is the most distinct, ranges from about x=9 to x=16 at a wake deficit velocity of 0.38, x being counted in diameters behind the cylinder axis; the second one appears from x=25 to x=28 at a wake deficit velocity of 0.54. This phenomenon seems to be related to an associated change-over in the orientation of the von Karman vortices and the merging trends, especially for Re=200 beyond x=25, as observed from instantaneous vorticity fields. Three-dimensional simulations using spanwise lengths of 10 and 12 (diameters) were carried out at Re=200. After a long initial phase with regular three-dimensional mode A flow features increasing very slowly in amplitude, the flow went into a state with distinct pulsating forces acting on the cylinder, the pulsations being seemingly randomly localized across the cylinder span. In this second, much more chaotic, flow state, the time-averaged results were in agreement with previous experiments and with parts of previous numerical studies.
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| 3. |
- Qu, Xiaobo, 1983, et al.
(författare)
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Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach
- 2020
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 257
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Tidskriftsartikel (refereegranskat)abstract
- It has been well recognized that human driver's limits, heterogeneity, and selfishness substantially compromise the performance of our urban transport systems. In recent years, in order to deal with these deficiencies, our urban transport systems have been transforming with the blossom of key vehicle technology innovations, most notably, connected and automated vehicles. In this paper, we develop a car following model for electric, connected and automated vehicles based on reinforcement learning with the aim to dampen traffic oscillations (stop-and-go traffic waves) caused by human drivers and improve electric energy consumption. Compared to classical modelling approaches, the proposed reinforcement learning based model significantly reduces the modelling constraints and has the capability of self-learning and self-correction. Experiment results demonstrate that the proposed model is able to improve travel efficiency by reducing the negative impact of traffic oscillations, and it can also reduce the average electric energy consumption.
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| 4. |
- Zhou, M. F., et al.
(författare)
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A recurrent neural network based microscopic car following model to predict traffic oscillation
- 2017
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Ingår i: Transportation Research, Part C: Emerging Technologies. - : Elsevier BV. - 0968-090X. ; 84, s. 245-264
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics.
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| 5. |
- Zhou, M. F., et al.
(författare)
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Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach
- 2020
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 21:1, s. 433-443
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Tidskriftsartikel (refereegranskat)abstract
- The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.
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