From Software-Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving

On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University-Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles

Magnetic Phase Imaging using Lorentz Near-field Electron Ptychography

Over the past few years, the combination of diffuser and near-field electron ptychography has drawn more attention by its ability to recover large field of view with few diffraction patterns. In this paper, we purpose a novel design and implementation of amplitude diffuser. The amplitude diffuser introduces structures to the illumination while reducing the inelastic scattering. And the amplitude diffuser is implemented at the condenser lens aperture, allowing us to vary the illumination size under the same microscope setup. We demonstrate the reconstruction results under both conventional Transmission Electron Microscopy (TEM) mode as well as Lorentz mode.Comment: Presented in ISCS2

Analytical model for nonlinear vibration of flexible rotor system

An analytical model is proposed to analyze a series of typical nonlinear behaviors of flexible rotor system, such as resonance, oscillation, whirl and whip. The model is constructed by introducing a defined nonlinear scale factor ε, nonlinear stiffness and nonlinear damping. Based on multi-scale method, the analytical solutions of steady-state and transient-state are derived, and the nonlinear natural frequency and Frequency Response Equation (FRE) are obtained. A transient time scale factor t1 is defined to reflect the transient-state influence on steady-state solution. The experimental result also verifies the rationality and validity of the analytical model and the analytical solutions

Genome-wide association study of maize resistance to Pythium aristosporum stalk rot

Stalk rot, a severe and widespread soil-borne disease in maize, globally reduces yield and quality. Recent documentation reveals that Pythium aristosporum has emerged as one of the dominant causal agents of maize stalk rot. However, a previous study of maize stalk rot disease resistance mechanisms and breeding had mainly focused on other pathogens, neglecting P. aristosporum. To mitigate crop loss, resistance breeding is the most economical and effective strategy against this disease. This study involved characterizing resistance in 295 inbred lines using the drilling inoculation method and genotyping them via sequencing. By combining with population structure, disease resistance phenotype, and genome-wide association study (GWAS), we identified 39 significant single-nucleotide polymorphisms (SNPs) associated with P. aristosporum stalk rot resistance by utilizing six statistical methods. Bioinformatics analysis of these SNPs revealed 69 potential resistance genes, among which Zm00001d051313 was finally evaluated for its roles in host defense response to P. aristosporum infection. Through virus-induced gene silencing (VIGS) verification and physiological index determination, we found that transient silencing of Zm00001d051313 promoted P. aristosporum infection, indicating a positive regulatory role of this gene in maize’s antifungal defense mechanism. Therefore, these findings will help advance our current understanding of the underlying mechanisms of maize defense to Pythium stalk rot

Wind Dynamic Environment and Wind Tunnel Simulation Experiment of Bridge Sand Damage in Xierong Section of Lhasa–Linzhi Railway

The Lhasa–Linzhi Railway is located in the sandy area of the South Tibet valley, with high elevation and cold temperature. The Xierong section is a bridge section where blown sand hazards are severe. However, the disaster-causing mechanism of blown sand hazards in this section is currently unclear, thereby hindering targeted sand prevention and control. To address this problem, the wind dynamic environment of and causes of sand damage in this section are investigated through the field observation of the locale and a wind tunnel simulation experiment. Results show that the dominant sand-moving wind direction in the Xierong section is SSE. The wind speed, frequency of sand-moving wind, sand drift potential (DP), and maximum possible sand transport quantity (Q) in this section are relatively high during spring (March to May) and low during other seasons. The yearly resultant sand transport direction (RDD, RA) is SW. The angle between the route trend of this section and the sand transportation direction is 30°–45°, and the sand source is located in the east side of the railway. During spring, sand materials are blown up by the wind, forming blown sand flow and movement from the NE to SW direction. Increased wind speed area is formed between the top of the slope shoulder of the windward side of the bridge and the downwind direction of 3H, causing blown sand erosion. Meanwhile, weakened wind speed areas are formed within the distance of -3H at the upwind direction and from the downwind direction of the 3H to 20H of the bridge. These areas accumulate sand materials at the upwind and downwind directions of the bridge, thereby resulting in blown sand hazards. This research provides a scientific basis for the prevention and control of sand damage in the locale

Ti3+ Defective SnS2/TiO2 Heterojunction Photocatalyst for Visible-Light Driven Reduction of CO2 to CO with High Selectivity

In recent years, defective TiO2-based composite nanomaterials have received much attention in the field of photocatalysis. In this work, TiB2 was used as a precursor to successfully prepare Ti3+ defective TiO2 (TiO2-B) with a truncated bipyramidal structure by a one-step method. Then, the SnS2 nanosheets were assembled onto the as-prepared TiO2-B through simple hydrothermal reaction. TiO2-B exhibits strong visible light absorption properties, but the recombination rate of the photo-generated electron-hole pair was high and does not exhibit ideal photocatalytic performance. Upon introducing SnS2, the heterojunction catalyst SnS2-Ti3+ defective TiO2 (SnS2/TiO2-B) not only possesses the strong light absorption from UV to visible light region, the lowest photo-generated charge recombination rate but also achieves a more negative conduction band potential than the reduction potential of CO2 to CO, and thereby, exhibits the significantly enhanced selectivity and yield of CO in photocatalytic CO2 reduction. Notably, SnS2/TiO2-B produces CO at a rate of 58 µmol·h−1·g−1 with CO selectivity of 96.3% under visible light irradiation, which is 2 and 19 times greater than those of alone TiO2-B and SnS2, respectively. Finally, a plausible photocatalytic mechanism on SnS2/TiO2-B was proposed that the electron transfer between TiO2 and SnS2 follows the Z-scheme mode. Our results present an effective way to gain highly efficient TiO2 based photocatalysts for CO2 reduction by combining different modification methods of TiO2 and make full use of the synergistic effects

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields

Morphological and neurophysiological impairment of the nerve in type II macrodactyly.

BACKGROUND:Macrodactyly is a congenital malformation characterized by aggressive overgrowth of multiple tissues, including subcutaneous fat, nerves, and bones in digits or limbs. In type II macrodactyly, the peripheral nerve is enlarged; however, the morphological and functional characteristics of the affected peripheral nerves have rarely been evaluated. METHODS:In this research, six macrodactyly patients and three polydactyly patients (control) were studied. Pre-operative sensory nerve action potential and intra-operative nerve action potential tests were performed. The microstructure and ultrastructure of the enlarged nerves were observed and neurofilament (NF) expression was evaluated using immunofluorescent staining. RESULTS:Axon impairment of the digital nerves originating from the median nerve (MN) was observed. A compensatory reinnervation from the ulnar nerve (UN) was found in two of the six patients, and significant morphological changes were observed in the enlarged nerve. The myelinated nerve fibers decreased, the lamellar structure of the myelin sheath changed, and the density of the NFs of the unmyelinated fibers decreased. There was aberrant distribution of NFs in the macrodactylous nerve tissues. In patients with compensatory UN reinnervation, the number of myelinated and unmyelinated fibers increased to normal levels; however, the diameter of the myelinated fibers apparently decreased. CONCLUSIONS:The morphology and function of the macrodactylous enlarged nerve was impaired in type II macrodactyly patients; however, the unaffected UN partially compensated for the lost function of the affected MN under specific situations. Electrophysiological tests should be performed to determine the function of the affected nerve and surgical treatment for type II macrodactyly could be refined

Ice Velocity in Upstream of Heilongjiang Based on UAV Low-Altitude Remote Sensing and the SIFT Algorithm

In river management, it is important to obtain ice velocity quickly and accurately during ice flood periods. However, traditional ice velocity monitoring methods require buoys, which are costly and inefficient to distribute. It was found that UAV remote sensing images combined with machine vision technology yielded obvious practical advantages in ice velocity monitoring. Current research has mainly monitored sea ice velocity through GPS or satellite remote sensing technology, with few reports available on river ice velocity monitoring. Moreover, traditional river ice velocity monitoring methods are subjective. To solve the problems of existing time-consuming and inaccurate ice velocity monitoring methods, a new ice velocity extraction method based on UAV remote sensing technology is proposed in this article. In this study, the Mohe River section in Heilongjiang Province was chosen as the research area. High-resolution orthoimages were obtained with a UAV during the ice flood period, and feature points in drift ice images were then extracted with the scale-invariant feature transform (SIFT) algorithm. Moreover, the extracted feature points were matched with the brute force (BF) algorithm. According to optimization results obtained with the random sample consensus (RANSAC) algorithm, the motion trajectories of these feature points were tracked, and an ice displacement rate field was finally established. The results indicated that the average ice velocities in the research area reached 2.00 and 0.74 m/s, and the maximum ice velocities on the right side of the river center were 2.65 and 1.04 m/s at 16:00 on 25 April 2021 and 8:00 on 26 April 2021, respectively. The ice velocity decreased from the river center toward the river banks. The proposed ice velocity monitoring technique and reported data in this study could provide an effective reference for the prediction of ice flood disasters

A Regional Maize Yield Hierarchical Linear Model Combining Landsat 8 Vegetative Indices and Meteorological Data: Case Study in Jilin Province

The use of satellite remote sensing could effectively predict maize yield. However, many statistical prediction models using remote sensing data cannot extend to the regional scale without considering the regional climate. This paper first introduced the hierarchical linear modeling (HLM) method to solve maize-yield prediction problems over years and regions. The normalized difference vegetation index (NDVI), calculated by the spectrum of the Landsat 8 operational land imager (OLI), and meteorological data were introduced as input parameters in the maize-yield prediction model proposed in this paper. We built models using 100 samples from 10 areas, and used 101 other samples from 34 areas to evaluate the model’s performance in Jilin province. HLM provided higher accuracy with an adjusted determination coefficient equal to 0.75, root mean square error (RMSEV) equal to 0.94 t/ha, and normalized RMSEV equal to 9.79%. Results showed that the HLM approach outperformed linear regression (LR) and multiple LR (MLR) methods. The HLM method based on the Landsat 8 OLI NDVI and meteorological data could flexibly adjust in different regional climatic conditions. They had higher spatiotemporal expansibility than that of widely used yield estimation models (e.g., LR and MLR). This is helpful for the accurate management of maize fields