The impact of driving conditions on light-duty vehicle emissions in real-world driving

To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix. First published online 19 March 202

A vector spectrum analyzer of 55.1 THz spectral bandwidth and 99 kHz frequency resolution

The analysis of optical spectra - emission or absorption - has been arguably the most powerful approach for discovering and understanding matters. The invention and development of many kinds of spectrometers have equipped us with versatile yet ultra-sensitive diagnostic tools for trace gas detection, isotope analysis, and resolving hyperfine structures of atoms and molecules. With proliferating data and information, urgent and demanding requirements have been placed today on spectrum analysis with ever-increasing spectral bandwidth and frequency resolution. These requirements are especially stringent for broadband laser sources that carry massive information, and for dispersive devices used in information processing systems. In addition, spectrum analyzers are expected to probe the device's phase response where extra information is encoded. Here we demonstrate a novel vector spectrum analyzer (VSA) that is capable to characterize passive devices and active laser sources in one setup. Such a dual-mode VSA can measure loss, phase response and dispersion property of passive devices. It also can coherently map a broadband laser spectrum into the RF domain. The VSA features a bandwidth of 55.1 THz (1260 to 1640 nm), frequency resolution of 99 kHz, and dynamic range of 56 dB. Meanwhile, our fiber-based VSA is compact and robust. It requires neither high-speed modulators and photodetectors, nor any active feedback control. Finally, we successfully employ our VSA for applications including characterization of integrated dispersive waveguides, mapping frequency comb spectra, and coherent light detection and ranging (LiDAR). Our VSA presents an innovative approach for device analysis and laser spectroscopy, and can play a critical role in future photonic systems and applications for sensing, communication, imaging, and quantum information processing

Systematic analysis of the role and significance of target genes of active ingredients of traditional Chinese medicine injections in the progression and immune microenvironment of hepatocellular carcinoma

Background: Traditional Chinese medicine in China is an important adjuvant therapy for the treatment of hepatocellular carcinoma (HCC) and traditional Chinese medicines injections have a wide range of clinical applications. The purpose of this study was to identify the active ingredients and related genes of traditional Chinese medicine injections that can treat hepatocellular carcinoma.Methods: Effective small molecule components were extracted from 14 types of traditional Chinese medicines from 8 injections and the main gene targets were identified. The 968 patients with HCC were classified based on the target gene set, and the characteristics of patients with different subtypes were analyzed. Patients with two subtypes of HCC were compared with normal tissues and cirrhosis to identify important gene targets related to traditional Chinese medicines in HCC progression.Results: In this study, 138 important genes associated with traditional Chinese medicines were identified and two HCC subtypes were identified. By analyzing the differences between the two subtypes, 25 related genes were associated with HCC subtypes. Through clinical and pharmacological analysis, this study identified quercetin as an important traditional Chinese medicines small molecule and secreted phosphoprotein 1 (SPP1) as an important oncogene in HCC.Conclusion: Traditional Chinese medicines injection is an important adjuvant treatment modality for HCC. SPP1 is an important oncogene in HCC

Inertial navigation and positioning system for underground driverless trai

Current underground driverless train positioning technology calculates the train displacement based on optical sensor installed on the wheels. When the underground driverless train skids on the wet track, the positioning error will be generated. In view of the above problem, an inertial navigation and positioning system for underground driverless train was proposed. Inertial navigation module was introduced in the system based on current underground driverless train positioning technology, and double threshold algorithm was used to judge abnormal driving condition of the train combined with photoelectric sensor data and inertial navigation data, and safety factor of driverless train was increased. The inertial navigation module uses LPMS-NAV2 to obtain acceleration and heading angle of target, and the position coordinates of the target is calculated. In view of the problem that acceleration measurement of the target is affected by gravity acceleration, z-axis acceleration compensation method is used to eliminate the error brought by gravity. In view of the problem of cumulative error of positioning of the target, weight feedback constraint algorithm is introduced, and square difference loss function is constructed for weighted constraint of the positioning point, so as to reduce the cumulative error. A position beacon is set at each fork of the underground roadway to perform secondary calibration on the positioning information to further improve the positioning accuracy. The indoor test results show that the average positioning error of the inertial navigation and positioning system for the underground driverless train is 0.52 m

A New Scheduling Algorithm for Reducing Data Aggregation Latency in Wireless Sensor Networks * Abstract

Existing works on data aggregation in wireless sensor networks (WSNs) usually use a single channel which results in a long latency due to high interference, especially in high-density networks. Therefore, data aggregation is a fundamental yet time-consuming task in WSNs. We present an improved algorithm to reduce data aggregation latency. Our algorithm has a latency bound of 16R + Δ – 11, where Δ is the maximum degree and R is the network radius. We prove that our algorithm has smaller latency than the algorithm in [1]. The simulation results show that our algorithm has much better performance in practice than previous works