Development of air-assisted atomization device for the delivery of cells in viscous biological ink prepared with sodium alginate.

Skin wounds, especially large-area skin trauma, would bring great pain and even fatal risk to patients. In recent years, local autologous cell transplantation has shown great potential for wound healing and re-epithelialization. However, when the cell suspension prepared with normal saline is delivered to the wound, due to its low viscosity, it is easy to form big drops in the deposition and lose them from the wound bed, resulting in cell loss and uneven coverage. Here, we developed a novel air-assisted atomization device (AAAD). Under proper atomization parameters, 1% (w/v) sodium alginate (SA) solution carrier could be sprayed uniformly. Compared with normal saline, the run-off of the SA on the surface of porcine skin was greatly reduced. In theory, the spray height of AAAD could be set to achieve the adjustment of a large spray area of 1-12 cm2. In the measurement of droplet velocity and HaCaT cell viability, the spray height of AAAD would affect the droplet settling velocity and then the cell delivery survival rate (CSR). Compared with the spray height of 50 mm, the CSR of 100 mm was significantly higher and could reach 91.09% ± 1.82% (92.82% ± 2.15% in control). For bio-ink prepared with 1% (w/v) SA, the viability remained the same during the 72-h incubation. Overall, the novel AAAD uniformly atomized bio-ink with high viscosity and maintained the viability and proliferation rate during the delivery of living cells. Therefore, AAAD has great potential in cell transplantation therapy, especially for large-area or irregular skin wounds.

Hot Deformation Behavior of Hastelloy C276 Alloy: Microstructural Variation and Constitutive Models.

Isothermal deformation experiments of the Hastelloy C276 alloy were executed using the Gleeble-3500 hot simulator at a temperature range of 1000-1150 °C and a strain rate range of 0.01-10 s-1. Microstructural evolution mechanisms were analyzed via transmission electron microscope (TEM) and electron backscatter diffraction (EBSD). Results reveal that the influences of hot compression parameters on the microstructure variation features and flow behaviors of the Hastelloy C276 alloy were significant. The intense strain hardening (SH) effects caused by the accumulation of substructures were promoted when the strain rates were increased, and true stresses exhibited a notable increasing tendency. However, the apparent DRV effects caused by the annihilation of substructures and the increasingly dynamic recrystallization (DRX) behaviors occurred at high compressed temperature, inducing the reduction in true stresses. In addition, a physical-based (PB) constitutive model and a long short-term memory (LSTM) model optimized using the particle swarm optimization (PSO) algorithm were established to predict the flow behavior of Hastelloy C276 alloy. The smaller average absolute relative error and greater relation coefficient suggest that the LSTM model possesses a higher forecasting accuracy than the PB model.

Learning naturalistic driving environment with statistical realism.

For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations.

Dense reinforcement learning for safety validation of autonomous vehicles.

One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critical events1. Here we report the development of an intelligent testing environment, where artificial-intelligence-based background agents are trained to validate the safety performances of autonomous vehicles in an accelerated mode, without loss of unbiasedness. From naturalistic driving data, the background agents learn what adversarial manoeuvre to execute through a dense deep-reinforcement-learning (D2RL) approach, in which Markov decision processes are edited by removing non-safety-critical states and reconnecting critical ones so that the information in the training data is densified. D2RL enables neural networks to learn from densified information with safety-critical events and achieves tasks that are intractable for traditional deep-reinforcement-learning approaches. We demonstrate the effectiveness of our approach by testing a highly automated vehicle in both highway and urban test tracks with an augmented-reality environment, combining simulated background vehicles with physical road infrastructure and a real autonomous test vehicle. Our results show that the D2RL-trained agents can accelerate the evaluation process by multiple orders of magnitude (103 to 105 times faster). In addition, D2RL will enable accelerated testing and training with other safety-critical autonomous systems.

Microstructural Evolution and an Improved Dynamic Recrystallization Kinetic Model of a Ni-Cr-Mo Alloy in Hot Deformation.

Microstructural evolution and dynamic recrystallization (DRX) behaviors of a Ni-Cr-Mo alloy were researched utilizing hot compressive experiments. The changed features of dislocation, subgrain and grain structure correlating to forming parameters were examined by transmission electron microscope (TEM) and electron backscatter diffraction (EBSD). Results illustrate that the consumption of dislocation and the coarsening of substructure/DRX grain are prominently enhanced with an increased forming temperature. However, the annihilation/interaction of dislocation and the expansion of subgrain/DRX grain boundary can be limited at a larger strain rate. Meanwhile, considering the discrepancy in DRX variation rates concerning the strain rate's ranges, an improved DRX kinetic model was developed. Compared to the classical DRX kinetic model, the good consistency between the forecasted and tested results demonstrates that the established improved DRX kinetic model can precisely characterize the DRX features of the Ni-Cr-Mo alloy over a wide strain rate range. Additionally, the EBSD's quantitative statistical results proved that the variation of DRX grain size can be supremely defined as the power formulation of the forming temperature and strain rate.

Sheathless acoustic based flow cell sorter for enrichment of rare cells.

Cell enrichment is a powerful tool in many kinds of cell research, especially in applications with low abundance cell types. In this work, we developed a microfluidic fluorescence activated cell sorting device that was able to perform on-demand, low loss cell detection, and sorting. The chip utilizes three-dimensional acoustic standing waves to position all cells in the same fluid velocity regime without sheath. When the cells pass through a laser interrogation region, the scattering and fluorescent signals are detected, translated and transported to software. The target cells are then identified by gating on the plots. Short bursts of standing acoustic waves are triggered by order from PC to sort target cells within predefined gating region. For very low abundance and rare labeled lymphocytes mixed with high concentration unlabeled white blood cells (WBCs), (1-100 labeled lymphocytes are diluted in 106 WBCs in 1 ml volume fluid), the device is able to remove more than 98% WBCs and recover labeled lymphocytes with efficiency of 80%. We further demonstrated that this device worked with real clinical samples by successfully isolating fetal nucleated red blood cells (FNRBCs) in the blood samples from pregnant women with male fetus. The obtained cells were sequenced and the expressions of (sex determining region Y) SRY genes were tested to determine fetal cell proportion. In genetic analysis, the proportion of fetal cells in the final picked sample is up to 40.64%. With this ability, the device proposed could be valuable for biomedical applications involving fetal cells, circulating tumor cells, and stem cells.

Microstructural Variation and a Physical Mechanism Model for a Ti-55511 Alloy during Double-Stage Hot Deformation with Stepped Strain Rates in the ß Region.

The microstructural variation and high-temperature flow features of a Ti-55511 alloy in the ß region are studied by utilizing double-stage compression with a stepped strain rate. The results demonstrate that the stresses in the latter stage of hot compression markedly reduce as the strain at the previous stage or the strain rate at the previous/latter stage drops. Moreover, the annihilation/interaction of substructures is promoted, and the distinct refinement of the dynamic recrystallization (DRX) in the ß grain can be found. However, the coarsening of the ß grain and the consumption of dislocation substructures are accelerated at high temperatures. Furthermore, the principal DRX nucleation mechanism of the Ti-55511 alloy during double-stage compression with a stepped strain rate in the ß region is discontinuous DRX. Additionally, by using the microstructural variation characteristics related to the forming parameters, a physical mechanism equation is modeled to forecast the forming features, the DRX fraction, and the size of the ß grain in the investigated alloy. The forecasted results are in accordance with the tested results, indicating that the established model can accurately forecast the microstructure variation and flow features of the studied alloy.

Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.

Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.

Safety assessment of highly automated driving systems in test tracks: A new framework.

Among the three major safety assessment methods (i.e., simulation, test track, and on-road test) for highly automated driving systems (ADS), test tracks provide high fidelity and a safe and controllable testing environment. However, due to the lack of realistic background traffic, scenarios that can be tested in test tracks are usually static and limited. To address this limitation, a new safety assessment framework is proposed in this paper, which integrates an augmented reality (AR) testing platform and a testing scenario library generation (TSLG) method. The AR testing platform generates simulated background traffic in test tracks, which interact with subject ADS under test, to create a realistic traffic environment. The TSLG method can systematically generate a set of critical scenarios under each operational design domain (ODD) and the critical scenarios generated from the TSLG method can be imported into the AR testing platform. The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS. Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost-effective fashion. In the cut-in case study, the proposed framework is estimated to accelerate the assessment process by 9.87×104 times comparing to the on-road test approach.

Note: Low sliding friction piezoelectric motor using motion of hula hoop.

A piezoelectric motor that imitates the motion of a hula hoop was developed. It has the advantage of low sliding wear as static friction force is used to drive the rotor. The rotation speed of the rotor can be regulated by changing the exciting frequency of piezoelectric transducers. The stator is composed of four piezoelectric plates attached to a steel rod. Two orthogonal bending modes were excited by driving two pairs of piezoelectric plates, and circular motion was formed on the rod. The reversed motion of the rotor can be obtained by adjusting the phase of two bending modes. The prototype stator working at 39.1 Hz produced a rotation speed of 1142 rpm and a torque of 0.23 mN m.