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Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called “the Virtual Confidence (VC)” by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference.

Unsynchronization of the separate transmit-receive beams makes it a challenging task to obtain high-quality images for nonparallel-track bistatic synthetic aperture radar (NP-BiSAR). To accommodate this issue, we propose an imaging configuration where the receiver's beam follows the transmitter's one actively by adjusting the squint angle of receiver. And a frequency extended nonlinear chirp scaling (FENLCS) algorithm is modified to cope with new effects introduced by the innovative configuration, which is based on an improved quadratic ellipse model. Based on the new model, some innovative improvements on image formation are made, including a residual azimuth-dependent high-order range cell migration correction (ADH-RCMC) and a rederived FENLCS that takes highly varying Doppler centroid into consideration, which contribute to better imaging quality. Simulation results validate the effectiveness of the proposed configuration and algorithm.

Additional file 3: Table S1. Association of CARM1 level with clinicopathological parameters of patients with gastric cancer.

Additional file 5: Table S3. Antibodies and regents used in experiments.

Additional file 4: Table S2. Sequences of primers and targets for siRNA.

A 150W high power amplifier (PA) with an operating frequency range of 2.3 — 2.55GHz and high efficiency feature is proposed in this paper. The load pull and source pull technology is used to determine the best input and output impedance at maximum power output point, and the broadband matching of input and output impedance is completed by the method of parallel capacitance and microstrip line. When the PA design is completed, the structural parts are designed to ensure that the PA works in a good heat dissipation state. The drain efficiency of the PA is up to 53%, and the output power is 52 dBm, what's more, the in-band gain fluctuation is less than 2dB. Within the range of 6 dB of power back-off, the PA efficiency always stays in the range of 50%∼53%. This Doherty PA can be a good candidate for sub-6 5G applications.

Background: Marine microalgae, Isochrysis galbana Parke, is an important diet microalgal species with high nutritional value. Different from other unicellular microalgae, its cell contains two chloroplasts which are the major sites for lipid synthesis. Results: Here, we optimized a chloroplast isolation approach suitable for I. galbana Parke, and evaluated the purity and integrity of the isolated chloroplasts by microscopic observations and biochemical assays. The chloroplast lipids were sequenced by ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Glycolipids were the main chloroplast lipids, and DGDG, MGDG, and SQDG were the most abundant glyceroglycolipids. DGMG and SQMG were not observed among the chloroplast lipids. In addition, DG was the most abundant neutral lipid. A part of fatty acyl R1/R2 with MGMGs, DGDGs, MGDGs, SQDGs, PEs, and PCs were not found in chloroplasts. The fatty acid proportion of chloroplast lipids were increased, decreased, or remained unchanged compared with the whole-cell. Conclusions: This newly developed isolation approach was a simple and reliable method to isolate chloroplasts with high integrity and purity. Collectively, our findings show that such an isolation approach may be used in studies on many different aspects of chloroplast biology, and offers a useful reference for isolation of chloroplast from other marine microalgae.

Airport navigation lights are an important guarantee for flying at night and in complex weather conditions. The stability and reliability of the lighting system directly affect flight safety. However, due to the large number and the wide range of navigation lights, the manpower and material resources required for the inspection of the entire airport are huge, and the manual inspections are likely to cause problems such as missing investigations, mis-examinations, and statistical errors, which will directly affect the safety of the entire airport. At the same time, since the outer casing and the lamp holder of the navigation lights are made of metal, the wireless transmission capability of the monitoring device is required to be stronger. Therefore, the conventional monitoring method and the wireless communication method cannot meet the requirements of the navigational light monitoring system. The low-power wireless monitoring method and communication mechanism of navigational lights in the shielding environment can enable the battery-powered monitoring device to realize the long-term monitoring of the navigation light state in the shielding environment and upload the data to the cloud platform.

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.

In wireless sensor networks (WSNs), sensor nodes near static sink will have more traffic load to forward and the network lifetime will get largely reduced. This problem is referred to as the hotspot problem. Recently, adopting sink mobility has been considered as a good strategy to overcome the hotspot problem. Despite its many advantages, due to the dynamic network topology caused by sink mobility, data transmission to the mobile sink is a challenging task. To achieve efficient data dissemination, nodes need to reconstruct their routes toward the latest location of the mobile sink, which weakens the energy conservation aim. In this paper, we proposed an energy-efficient cluster-based dynamic routes adjustment approach (EECDRA) which aims to minimize the routes reconstruction cost of the sensor nodes while maintaining nearly optimal routes to the latest location of the mobile sinks. The network is divided into several equal clusters and cluster heads are selected within each cluster. We also set some communication rules that manage routes reconstruction process accordingly requiring only a limited number of nodes to readjust their data delivery routes toward the mobile sinks. Simulation results show that the mobile sinks for reducing reconstruction of route have improved the energy efficiency and prolonged lifetime of wireless sensor network.

Wireless Sensor Networks (WSNs) can be viewed as a network with hundreds or thousands of randomly deployed sensors, whose coverage control problem has the characteristics of self-organized groups. This paper studies the coverage optimization strategy based on swarm intelligence for wireless sensor networks. In WSNs, the random deployment of nodes causes the coverage of the blind area and the redundancy of the coverage. We propose a new algorithm based on virtual force and glowworm swarm optimization algorithm. Firstly, the utilization rate of the nodes and the effective coverage of the network are the optimization objectives and the corresponding mathematical model is established. Then, the virtual force algorithm and the glowworm swarm optimization algorithm are used to solve the problem of modeling, and the optimal coverage scheme for WSNs is obtained. Simulation results show that the virtual force and glowworm swarm optimization algorithm can effectively improve the coverage of WSNs nodes, reduce the redundancy of sensor nodes, and reduce the cost of network effectively. Besides, the network survival time can get prolonged.

In wireless sensor networks (WSNs), the clustering routing technology can improve the scalability of the network. When the cluster head transmits data to the base station in a multi hop manner, the residual energy of cluster head and path condition are not considered. So it can reduce the lifetime of cluster head and seriously affect the network lifetime. We propose an energy-balanced unequal clustering routing algorithm for wireless sensor networks. Firstly, the non-uniform clustering method is applied to the network. Secondly when calculating the cluster radius, the residual energy of nodes, the density of nodes and the distances between the nodes and base station will be taken into account. Then, the algorithm establishes the shortest path tree to search the optimal multi-hop transmission paths to realize efficient data transmission from sensor nodes to base station. Simulation results demonstrate that the improved algorithm can efficiently decrease the dead speed of the nodes, balance the energy dissipation of all nodes, and prolong the network lifetime.

Wireless sensor networks (WSNs) with one static sink often suffer from energy hole problem, which means energy consumption of certain sensors near the sink or on critical path is much faster than other sensor nodes. Finally, network partition and isolated nodes are caused. To solve this problem, we propose an Energy-Efficient Strategy Enhancing Lifetime for WSNs based on MECA and PEGASIS. This algorithm aims at minimizing and balancing energy consumption for all sensor nodes and cluster heads. We divide the network into several equal clusters and the intra-cluster routing exploits multi-hop routing to save energy. The cluster heads will connect a chain such as PEGASIS, which can avoid distant cluster head from sending data to the sink node. Simulation results show that our proposed routing algorithm consumes much less than traditional routing algorithm like LEACH.